From d9fa92c35c18b64df4931a24ae7edc68578f9a64 Mon Sep 17 00:00:00 2001 From: Alex Strashny Date: Wed, 28 Feb 2024 22:23:38 -0500 Subject: [PATCH] deffK --- R/tab.R | 34 ++-- R/total.R | 8 +- R/z_deffK.R | 22 +++ ...tory-Medical-Care-Survey-NAMCS-tables.html | 157 +++++++++--------- docs/pkgdown.yml | 2 +- docs/search.json | 2 +- ...atory-Medical-Care-Survey-NAMCS-tables.Rmd | 17 +- 7 files changed, 123 insertions(+), 119 deletions(-) create mode 100644 R/z_deffK.R diff --git a/R/tab.R b/R/tab.R index f444bff..9fd53c4 100644 --- a/R/tab.R +++ b/R/tab.R @@ -149,11 +149,21 @@ tab = function(... } ## - sto = svytotal(frm, design) # , deff = TRUE) + sto = svytotal(frm, design) # , deff = "replace") mmcr = data.frame(x = as.numeric(sto) , s = sqrt(diag(attr(sto, "var"))) ) - mmcr$samp.size = .calc_samp_size(design = design, vr = vr, counts = counts) - mmcr$counts = counts + mmcr$counts = counts + + # deff = attr(sto, "deff") %>% diag + # I am having trouble interpreting this deff. + # In some situations, results are unusual. + # total(), tab("AGER"), tab("PAYNOCHG") + # Using Kish design effect instead. + + mmcr$deff = by(design$prob, design$variables[,vr], deffK) %>% as.numeric + mmcr$samp.size = mmcr$counts / mmcr$deff + idx.bad = which(mmcr$samp.size > mmcr$counts) + mmcr$samp.size[idx.bad] = mmcr$counts[idx.bad] df1 = degf(design) mmcr$degf = df1 @@ -261,21 +271,3 @@ tab = function(... } df1 } - - -.calc_samp_size = function(design, vr, counts) { - - # In svytotal(frm, design, deff = TRUE), DEff sometimes - # appears incorrect. If no variability, DEff = Inf. - # Calculating "Kish's Effective Sample Size" directly, bypassing DEff - # deff = attr(sto, "deff") %>% diag - - design$wi = 1 / design$prob - design$wi[design$prob <= 0] = 0 - design$wi2 = design$wi^2 - sum_wi = by(design$wi, design$variables[,vr], sum) %>% as.numeric - sum_wi2 = by(design$wi2, design$variables[,vr], sum) %>% as.numeric - neff = sum_wi^2 / sum_wi2 - assert_that(length(neff) == length(counts)) - pmin(counts, neff) -} diff --git a/R/total.R b/R/total.R index 3539951..8f42fbe 100644 --- a/R/total.R +++ b/R/total.R @@ -31,12 +31,16 @@ total = function(csv = getOption("surveytable.csv") ) { } ## - sto = svytotal(~Total, design) # , deff = TRUE) + sto = svytotal(~Total, design) # , deff = "replace") mmcr = data.frame(x = as.numeric(sto) , s = sqrt(diag(attr(sto, "var"))) ) - mmcr$samp.size = .calc_samp_size(design = design, vr = "Total", counts = counts) mmcr$counts = counts + mmcr$deff = deffK(design$prob) + mmcr$samp.size = mmcr$counts / mmcr$deff + idx.bad = which(mmcr$samp.size > mmcr$counts) + mmcr$samp.size[idx.bad] = mmcr$counts[idx.bad] + df1 = degf(design) mmcr$degf = df1 diff --git a/R/z_deffK.R b/R/z_deffK.R new file mode 100644 index 0000000..a838da4 --- /dev/null +++ b/R/z_deffK.R @@ -0,0 +1,22 @@ +# From: +# Richard Valliant and George Zipf (2023). PracTools: Designing and +# Weighting Survey Samples. R package version 1.4.2. +# https://CRAN.R-project.org/package=PracTools + +# Kish design effect +# Kish design effect due to unequal weights +# w: vector of inverses of selection probabilities for a sample + +# deffK = function (w) +deffK = function(prob) +{ + w = 1 / prob + assert_that(all(w > 0), all(w < Inf)) + + # if (any(w <= 0)) + # warning("Some weights are less than or equal to 0.\n") + n <- length(w) + ret = 1 + sum((w - mean(w))^2)/n/mean(w)^2 + assert_that(all(ret > 0)) + ret +} diff --git a/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html b/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html index a353c94..f532dc7 100644 --- a/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html +++ b/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html @@ -93,9 +93,7 @@ since a single patient can make multiple visits.

Selected variables from NAMCS 2019 come with the surveytable package, for use in examples, in an object -called namcs2019sv. To make the below code more reusable, -we’ll call the survey by a generic name, namely, -mysurvey.

+called namcs2019sv.

Begin

@@ -106,8 +104,7 @@

Begin library(surveytable)

Now, specify the survey that you’d like to analyze.

-mysurvey = namcs2019sv
-set_survey(mysurvey)
+set_survey(namcs2019sv)
Survey info {NAMCS 2019 PUF} @@ -615,28 +612,24 @@

Rates

-

Recall that survey objects have an element called -variables, which is a data frame that contains the survey -variables. Let’s examine the levels of MSA.

-
-levels(mysurvey$variables$MSA)
-## [1] "MSA (Metropolitan Statistical Area)" "Non-MSA"

To calculate the rates for a particular variable, we need to provide a data frame with a variable called Level that matches the -levels of the variable in the survey. Population gives the -population estimate.

-

For example, for MSA, we need a data frame as -follows:

-
+levels of the variable in the survey, and a variable called
+Population that gives the population size (which is assumed
+to be a constant rather than a random variable).

+
For MSA, we can see the levels of the variables just by
+using the tab() command, just as we did above. Thus, to
+calculate rates, we need a data frame as follows:

+
 uspop2019$MSA
 ##                                 Level Population
 ## 1 MSA (Metropolitan Statistical Area)  277229518
 ## 2                             Non-MSA   45957179

 
Now that we have the appropriate population estimates, the rate
 is:

-
+
 tab_rate("MSA", uspop2019$MSA)

-
+

 
 Metropolitan Statistical Area Status of physician location (rate per 100
 population) {NAMCS 2019 PUF}
@@ -705,10 +698,10 @@ 
Rates
 

 
We can also calculate rates of a specific variable based on the
 entire population:

-
+
 tab_rate("MDDO", uspop2019$total)
 ## * Rate based on the entire population.

-
+

@@ -774,10 +767,10 @@ 
Rates
 
 
 

 
 Type of doctor (MD or DO) (rate per 100 population) {NAMCS 2019 PUF}
 

-
+
 tab_rate("SPECCAT", uspop2019$total)
 ## * Rate based on the entire population.

-
+

 
 Type of specialty (Primary, Medical, Surgical) (rate per 100 population)
 {NAMCS 2019 PUF}
@@ -871,9 +864,9 @@ 
Counts and percentages
 
This table presents estimates for each age group, as well as for each
 age group by sex.

-
+
 var_list("age")

-
+

@@ -920,15 +913,15 @@ 
Counts and percentagesAGER, we need another age group variable,
 with different age categories. We create it using the
 var_cut function.

-
+
 var_cut("Age group", "AGE"
         , c(-Inf, 0, 4, 14, 64, Inf)
         , c("Under 1", "1-4", "5-14", "15-64", "65 and over") )

 
Now that we’ve created the Age group variable, we can
 create the tables:

-
+
 tab("AGER", "Age group", "SEX")

-

 
 Variables beginning with ‘age’ {NAMCS 2019 PUF}
 

+

@@ -1150,7 +1143,7 @@ 
Counts and percentages
 
 

 
 Patient age recode {NAMCS 2019 PUF}
 

-
+

@@ -1343,7 +1336,7 @@ 
Counts and percentages
 
 

 
 Age group {NAMCS 2019 PUF}
 

-
+

@@ -1449,9 +1442,9 @@ 
Counts and percentages
 
 

 
 Patient sex {NAMCS 2019 PUF}
 

-
+
 tab_cross("AGER", "SEX")

-
+

@@ -1851,9 +1844,9 @@ 
Counts and percentages
 
Rates
 

-
+
 tab_rate("AGER", uspop2019$AGER)

-

 
 (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}
 

+

@@ -1987,10 +1980,10 @@ 
Rates
 
 
 

 
 Patient age recode (rate per 100 population) {NAMCS 2019 PUF}
 

-
+
 tab_rate("Age group", uspop2019$`Age group`)
 ## * Population for some levels not defined: 15-64

-
+

@@ -2107,9 +2100,9 @@ 
Rates
 
 
 

 
 Age group (rate per 100 population) {NAMCS 2019 PUF}
 

-
+
 tab_rate("SEX", uspop2019$SEX)

-
+

@@ -2178,7 +2171,7 @@ 
Rates
 
To calculate the rates for one variable (AGER) by
 another variable (SEX), we need population estimates in the
 following format:

-
+
 uspop2019$`AGER x SEX`
 ##                Level Subset Population
 ## 1     Under 15 years Female   29604762
@@ -2194,9 +2187,9 @@ 
Rates
 ## 11       65-74 years   Male   14586962
 ## 12 75 years and over   Male    9098236

 
Once we have these population estimates, the rates are:

-
+
 tab_subset_rate("AGER", "SEX", uspop2019$`AGER x SEX`)

-

 
 Patient sex (rate per 100 population) {NAMCS 2019 PUF}
 

+

 
 Patient age recode (Patient sex = Female) (rate per 100 population)
 {NAMCS 2019 PUF}
@@ -2331,7 +2324,7 @@ 
Rates
 
 
 

-
+

 
 Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS
 2019 PUF}
@@ -2478,7 +2471,7 @@ 
Table 5importsurvey package, which automatically detects binary
 variables and imports them as logical variables.)

-
+
 #
 var_all("Medicare and Medicaid", c("PAYMCARE", "PAYMCAID"))
 
@@ -2499,7 +2492,7 @@ 
Table 5tab("PAYPRIV", "PAYMCARE", "PAYMCAID", "Medicare and Medicaid"
   , "No insurance", "Self-pay", "No charge"
   , "PAYWKCMP", "PAYOTH", "Unknown or blank")

-
+

@@ -2605,7 +2598,7 @@ 
Table 5
 
 

 
 Expected source of payment for visit: Private insurance {NAMCS 2019 PUF}
 

-
+

@@ -2711,7 +2704,7 @@ 
Table 5
 
 

 
 Expected source of payment for visit: Medicare {NAMCS 2019 PUF}
 

-
+

 
 Expected source of payment for visit: Medicaid or CHIP or other
 state-based program {NAMCS 2019 PUF}
@@ -2818,7 +2811,7 @@ 
Table 5
 
 

-
+

@@ -2924,7 +2917,7 @@ 
Table 5
 
 

 
 Medicare and Medicaid {NAMCS 2019 PUF}
 

-
+

@@ -3041,7 +3034,7 @@ 
Table 5
 
 

 
 No insurance {NAMCS 2019 PUF}
 

-
+

@@ -3158,7 +3151,7 @@ 
Table 5
 
 

 
 Self-pay {NAMCS 2019 PUF}
 

-
+

@@ -3273,7 +3266,7 @@ 
Table 5
 
 

 
 No charge {NAMCS 2019 PUF}
 

-
+

 
 Expected source of payment for visit: Workers Compensation {NAMCS 2019
 PUF}
@@ -3391,7 +3384,7 @@ 
Table 5
 
 

-
+

@@ -3497,7 +3490,7 @@ 
Table 5
 
 

 
 Expected source of payment for visit: Other {NAMCS 2019 PUF}
 

-
+

@@ -3614,13 +3607,13 @@ 
Table 6In the table, the “Unknown” and “Blank” values are collapsed into a
 single value. We can collapse two or more levels of a factor into a
 single level using the var_collapse function.

-
+
 var_collapse("PRIMCARE", "Unknown if PCP", c("Unknown", "Blank"))
 var_collapse("REFER", "Unknown if referred", c("Unknown", "Blank"))

 
Now, for the table:

-
+
 tab("PRIMCARE", "REFER", "SENBEFOR")

-

 
 Unknown or blank {NAMCS 2019 PUF}
 

+

@@ -3755,7 +3748,7 @@ 
Table 6
 
 

 
 Are you the patient’s primary care provider? {NAMCS 2019 PUF}
 

-
+

@@ -3919,7 +3912,7 @@ 
Table 6
 
 

 
 Was patient referred for visit? {NAMCS 2019 PUF}
 

-
+

@@ -4028,9 +4021,9 @@ 
Table 6The percentages within each subset that is defined by
 SENBEFOR add up to 100% – for this reason, we want to use
 tab_subset(), not tab_cross().

-
+
 tab_subset("PRIMCARE", "SENBEFOR")

-

 
 Has this patient been seen in your practice before? {NAMCS 2019 PUF}
 

+

 
 Are you the patient’s primary care provider? (Has this patient been seen
 in your practice before? = Yes, established patient) {NAMCS 2019 PUF}
@@ -4166,7 +4159,7 @@ 
Table 6
 
 

-
+

 
 Are you the patient’s primary care provider? (Has this patient been seen
 in your practice before? = No, new patient) {NAMCS 2019 PUF}
@@ -4313,9 +4306,9 @@ 
Table 6
 
 

-
+
 tab_subset("REFER", "SENBEFOR")

-
+

 
 Was patient referred for visit? (Has this patient been seen in your
 practice before? = Yes, established patient) {NAMCS 2019 PUF}
@@ -4480,7 +4473,7 @@ 
Table 6
 
 

-
+

 
 Was patient referred for visit? (Has this patient been seen in your
 practice before? = No, new patient) {NAMCS 2019 PUF}
@@ -4655,7 +4648,7 @@ 
Table 11Let’s create Age group from AGE and cross
 AGER and SEX to create a variable called
 Age x Sex:

-
+
 var_cut("Age group", "AGE"
         , c(-Inf, 0, 4, 14, 64, Inf)
         , c("Under 1", "1-4", "5-14", "15-64", "65 and over") )
@@ -4665,9 +4658,9 @@ 
Table 11To see the possible values of MAJOR (Major reason for
 this visit), and to estimate the total count for preventive care
 visits:

-
+
 tab("MAJOR")

-
+

@@ -4920,9 +4913,9 @@ 
Table 11
 
To create the tables of age, sex, and their interaction, and limit
 them to only the preventive care visits:

-
+
 tab_subset("AGER", "MAJOR", "Preventive care")

-

 
 Major reason for this visit {NAMCS 2019 PUF}
 

+

 
 Patient age recode (Major reason for this visit = Preventive care)
 {NAMCS 2019 PUF}
@@ -5145,9 +5138,9 @@ 
Table 11
 
 

-
+
 tab_subset("Age group", "MAJOR", "Preventive care")

-
+

 
 Age group (Major reason for this visit = Preventive care) {NAMCS 2019
 PUF}
@@ -5341,9 +5334,9 @@ 
Table 11
 
 

-
+
 tab_subset("SEX", "MAJOR", "Preventive care")

-
+

 
 Patient sex (Major reason for this visit = Preventive care) {NAMCS 2019
 PUF}
@@ -5450,9 +5443,9 @@ 
Table 11
 
 

-
+
 tab_subset("Age x Sex", "MAJOR", "Preventive care")

-
+

 
 (Patient age recode) x (Patient sex) (Major reason for this visit =
 Preventive care) {NAMCS 2019 PUF}
@@ -5852,11 +5845,11 @@ 
Table 11As each of the above commands is similar, and differs only in the
 first variable that is passed to the tab_subset() function,
 this code can be streamlined with a for loop:

-
+
 for (vr in c("AGER", "Age group", "SEX", "Age x Sex")) {
     print( tab_subset(vr, "MAJOR", "Preventive care") )
 }

-
+

 
 Patient age recode (Major reason for this visit = Preventive care)
 {NAMCS 2019 PUF}
@@ -6079,7 +6072,7 @@ 
Table 11
 
 

-
+

 
 Age group (Major reason for this visit = Preventive care) {NAMCS 2019
 PUF}
@@ -6273,7 +6266,7 @@ 
Table 11
 
 

-
+

 
 Patient sex (Major reason for this visit = Preventive care) {NAMCS 2019
 PUF}
@@ -6380,7 +6373,7 @@ 
Table 11
 
 

-
+

 
 (Patient age recode) x (Patient sex) (Major reason for this visit =
 Preventive care) {NAMCS 2019 PUF}
@@ -6788,7 +6781,7 @@ 
More advanced coding
+
 tmp_file = tempfile(fileext = ".csv")
 suppressMessages( set_output(csv = tmp_file) )
 
@@ -6833,14 +6826,14 @@ 
More advanced coding
+
 vr = "AGER"
 var_cross("tmp", "MAJOR", vr)
 ## Warning in var_cross("tmp", "MAJOR", vr): tmp: overwriting a variable that
 ## already exists.
 lvl = levels(surveytable:::env$survey$variables[,vr])[1]
 tab_subset("SPECCAT", "tmp", paste0("Preventive care : ", lvl))

-
+

 
 Type of specialty (Primary, Medical, Surgical) ((Major reason for this
 visit) x (Patient age recode) = Preventive care : Under 15 years) {NAMCS
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index a3760a5..46a26af 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables: Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html
   surveytable: surveytable.html
-last_built: 2024-02-23T16:05Z
+last_built: 2024-02-29T02:56Z
 urls:
   reference: https://cdcgov.github.io/surveytable/reference
   article: https://cdcgov.github.io/surveytable/articles
diff --git a/docs/search.json b/docs/search.json
index 9622d85..b792f31 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"begin","dir":"Articles","previous_headings":"","what":"Begin","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Begin loading surveytable package. , print message explaining specify survey ’d like analyze. omitting message . Now, specify survey ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey name, survey design variables, number observations verify looks correct.","code":"library(surveytable) mysurvey = namcs2019sv set_survey(mysurvey)"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages","dir":"Articles","previous_headings":"Table 1","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows overall estimated count well counts percentages type doctor, physician specialty, metropolitan statistical area. variables necessary creating table already survey, making commands straightforward. Total {NAMCS 2019 PUF} Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"total() tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates","dir":"Articles","previous_headings":"Table 1","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"published table also shows several rates. calculate rates, addition survey, need source information population estimates. typically use function read.csv() load population estimates get correct format. surveytable package comes object called uspop2019 contains several population estimates use examples. overall population estimate: overall population estimate, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} Recall survey objects element called variables, data frame contains survey variables. Let’s examine levels MSA. calculate rates particular variable, need provide data frame variable called Level matches levels variable survey. Population gives population estimate. example, MSA, need data frame follows: Now appropriate population estimates, rate : Metropolitan Statistical Area Status physician location (rate per 100 population) {NAMCS 2019 PUF} can also calculate rates specific variable based entire population: Type doctor (MD ) (rate per 100 population) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) ## [1] \"list\" names(uspop2019) ## [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         ## [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total ## [1] 323186697 total_rate(uspop2019$total) levels(mysurvey$variables$MSA) ## [1] \"MSA (Metropolitan Statistical Area)\" \"Non-MSA\" uspop2019$MSA ##                                 Level Population ## 1 MSA (Metropolitan Statistical Area)  277229518 ## 2                             Non-MSA   45957179 tab_rate(\"MSA\", uspop2019$MSA) tab_rate(\"MDDO\", uspop2019$total) ## * Rate based on the entire population. tab_rate(\"SPECCAT\", uspop2019$total) ## * Rate based on the entire population."},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages-1","dir":"Articles","previous_headings":"Table 3","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table presents estimates age group, well age group sex. Variables beginning ‘age’ {NAMCS 2019 PUF} survey couple relevant age-related variables. AGE patient age years. AGER categorical variable based AGE. However, table, addition AGER, need another age group variable, different age categories. create using var_cut function. Now ’ve created Age group variable, can create tables: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} Patient sex {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}","code":"var_list(\"age\") var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) tab(\"AGER\", \"Age group\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates-1","dir":"Articles","previous_headings":"Table 3","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Patient age recode (rate per 100 population) {NAMCS 2019 PUF} Age group (rate per 100 population) {NAMCS 2019 PUF} Patient sex (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population estimates following format: population estimates, rates : Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"tab_rate(\"AGER\", uspop2019$AGER) tab_rate(\"Age group\", uspop2019$`Age group`) ## * Population for some levels not defined: 15-64 tab_rate(\"SEX\", uspop2019$SEX) uspop2019$`AGER x SEX` ##                Level Subset Population ## 1     Under 15 years Female   29604762 ## 2        15-24 years Female   20730118 ## 3        25-44 years Female   43192143 ## 4        45-64 years Female   42508901 ## 5        65-74 years Female   16673240 ## 6  75 years and over Female   12421444 ## 7     Under 15 years   Male   30921894 ## 8        15-24 years   Male   20988582 ## 9        25-44 years   Male   42407267 ## 10       45-64 years   Male   40053148 ## 11       65-74 years   Male   14586962 ## 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-5","dir":"Articles","previous_headings":"","what":"Table 5","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table gives expected sources payment. use PAY* variables create several new variables required table. Note PAY* variables logical (TRUE FALSE), simplifies workflow. (survey imported R using importsurvey package, automatically detects binary variables imports logical variables.) Expected source payment visit: Private insurance {NAMCS 2019 PUF} Expected source payment visit: Medicare {NAMCS 2019 PUF} Expected source payment visit: Medicaid CHIP state-based program {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} insurance {NAMCS 2019 PUF} Self-pay {NAMCS 2019 PUF} charge {NAMCS 2019 PUF} Expected source payment visit: Workers Compensation {NAMCS 2019 PUF} Expected source payment visit: {NAMCS 2019 PUF} Unknown blank {NAMCS 2019 PUF} Check presentation standards flags! NCHS presentation standards rules, estimates shown.","code":"# var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\"))  # var_any(\"Payment used\", c(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\"   , \"PAYWKCMP\", \"PAYOTH\", \"PAYDK\")) var_not(\"No other payment used\", \"Payment used\")  var_all(\"Self-pay\", c(\"PAYSELF\", \"No other payment used\")) var_all(\"No charge\", c(\"PAYNOCHG\", \"No other payment used\")) var_any(\"No insurance\", c(\"Self-pay\", \"No charge\"))  # var_case(\"No pay\", \"NOPAY\", \"No categories marked\") var_any(\"Unknown or blank\", c(\"PAYDK\", \"No pay\"))  ## tab(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\", \"Medicare and Medicaid\"   , \"No insurance\", \"Self-pay\", \"No charge\"   , \"PAYWKCMP\", \"PAYOTH\", \"Unknown or blank\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-6","dir":"Articles","previous_headings":"","what":"Table 6","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows primary care provider referral status, prior-visit status. table, “Unknown” “Blank” values collapsed single value. can collapse two levels factor single level using var_collapse function. Now, table: patient’s primary care provider? {NAMCS 2019 PUF} patient referred visit? {NAMCS 2019 PUF} patient seen practice ? {NAMCS 2019 PUF} percentages within subset defined SENBEFOR add 100% – reason, want use tab_subset(), tab_cross(). patient’s primary care provider? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient’s primary care provider? (patient seen practice ? = , new patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = , new patient) {NAMCS 2019 PUF}","code":"var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) var_collapse(\"REFER\", \"Unknown if referred\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\", \"REFER\", \"SENBEFOR\") tab_subset(\"PRIMCARE\", \"SENBEFOR\") tab_subset(\"REFER\", \"SENBEFOR\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-11","dir":"Articles","previous_headings":"","what":"Table 11","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows information Table 3, preventive care visits. , estimates age group, well age group sex, preventive care visits. Let’s create Age group AGE cross AGER SEX create variable called Age x Sex: see possible values MAJOR (Major reason visit), estimate total count preventive care visits: Major reason visit {NAMCS 2019 PUF} create tables age, sex, interaction, limit preventive care visits: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) (Major reason visit = Preventive care) {NAMCS 2019 PUF} commands similar, differs first variable passed tab_subset() function, code can streamlined loop: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) (Major reason visit = Preventive care) {NAMCS 2019 PUF} Note called inside loop, print() function needs called explicitly.","code":"var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) ## Warning in var_cut(\"Age group\", \"AGE\", c(-Inf, 0, 4, 14, 64, Inf), c(\"Under 1\", ## : Age group: overwriting a variable that already exists. var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"MAJOR\") tab_subset(\"AGER\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age group\", \"MAJOR\", \"Preventive care\") tab_subset(\"SEX\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age x Sex\", \"MAJOR\", \"Preventive care\") for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     print( tab_subset(vr, \"MAJOR\", \"Preventive care\") ) }"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"more-advanced-coding","dir":"Articles","previous_headings":"Table 11","what":"More advanced coding","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"addition, age-sex category, published table shows percentage preventive care visits made primary care physicians. calculate percentages, slightly involved loop needed. code, followed explanation: Since tab_subset() called within loop, wanted print screen, need use print( tab_subset(*) ). Since don’t want print screen, call print() omitted. Since many tables produced, output sent CSV file. , loop goes age, sex, age / sex interaction variables, calling variables vr. MAJOR vr crossed, result stored variable called tmp. Next, inner loop goes levels vr, calling levels lvl. code tabulates SPECCAT (Type specialty – Primary, Medical, Surgical) subset tmp (MAJOR crossed vr) restricted “Preventive care:” followed lvl, level vr, “15 years” AGER. Finally, CSV output turned . run code, tables stored CSV file. give idea tables look like, just one tables: Type specialty (Primary, Medical, Surgical) ((Major reason visit) x (Patient age recode) = Preventive care : 15 years) {NAMCS 2019 PUF} match percentage published table, see “Primary care specialty” row. sure check presentation standards flags.","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) )  for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     var_cross(\"tmp\", \"MAJOR\", vr)     for (lvl in levels(surveytable:::env$survey$variables[,vr])) {         tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care : \", lvl))     } } ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists.  ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists.  ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. set_output(csv = \"\") ## * Turning off CSV output. ## * ?set_output for other options. vr = \"AGER\" var_cross(\"tmp\", \"MAJOR\", vr) ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. lvl = levels(surveytable:::env$survey$variables[,vr])[1] tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care : \", lvl))"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"concepts","dir":"Articles","previous_headings":"Preliminaries","what":"Concepts","title":"Introduction to surveytable","text":"two important concepts need learn distinguish: data frame standard way storing data R. data frame rectangular data. Variables columns, observations rows. Example: data frame, , represent complex survey. , just looking data frame, R know sampling weights , strata , etc. Even variables represent sampling weights, etc, part data frame, just looking data frame, R know variable represents weights survey design variables. can get data frame R many different ways. data currently comma-separated values (CSV) file, can use read.csv(). ’s SAS file, can use package like haven importsurvey. ’s already R format, use readRDS(), . survey object object describes survey. tells R sampling weights , strata , . data frame can converted survey object using survey::svydesign() function; survey uses replicate weights, survey::svrepdesign() function used. Generally speaking, need convert data frame survey object . converted, can save saveRDS() (similar). future, can load readRDS(). need re-convert data frame survey object every time.","code":"head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"namcs","dir":"Articles","previous_headings":"Preliminaries","what":"NAMCS","title":"Introduction to surveytable","text":"Examples tutorial use survey called National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF). NAMCS “annual nationally representative sample survey visits non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, pathologists.” Note unit observation visits, patients – distinction important since single patient can make multiple visits. surveytable package comes data frame selected variables NAMCS, called namcs2019sv_df (sv = selected variables; df = data frame). survey object survey called namcs2019sv. namcs2019sv object analyze. really need namcs2019sv. reason package namcs2019sv_df illustrate convert data frame survey object.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"more-concepts","dir":"Articles","previous_headings":"Preliminaries","what":"More concepts","title":"Introduction to surveytable","text":"importing data another source, SAS CSV, analysts aware standard way variables handled R. Specifically, categorical variables stored factor. true / false variables stored factor well, programming tasks easier stored logical. Unknown values stored missing (NA). variable contains “special values”, negative value indicating age missing, “special values” need converted NA. Variables namcs2019sv_df already stored correctly. Thus, AGER (patient’s age group) factor variable; PAYNOCHG (indicates whether charge physician visit) logical variable; AGE (patient’s age years) numeric variable.","code":"library(\"surveytable\") class(namcs2019sv_df$AGER) #> [1] \"factor\" class(namcs2019sv_df$PAYNOCHG) #> [1] \"logical\" class(namcs2019sv_df$AGE) #> [1] \"numeric\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-a-survey-object","dir":"Articles","previous_headings":"Preliminaries","what":"Create a survey object","title":"Introduction to surveytable","text":"seen , tables produced surveytable clearer either variable names descriptive, variables \"label\" attribute descriptive. namcs2019sv_df, variables already \"label\" attribute set. example, variable name AGE descriptive, variable descriptive \"label\" attribute: Documentation NAMCS survey provides names survey design variables. Specifically, NAMCS, cluster ID’s, also known primary sampling units (PSU’s), given CPSUM; strata given CSTRATM; sampling weights given PATWT. Thus, namcs2019sv_df data frame can turned survey object follows: Tables produced surveytable clearer either name survey object descriptive, object \"label\" attribute descriptive. Let’s set attribute mysurvey: mysurvey object now identical namcs2019sv. Let’s verify : just successfully created survey object data frame.","code":"attr(namcs2019sv_df$AGE, \"label\") #> [1] \"Patient age in years\" mysurvey = survey::svydesign(ids = ~ CPSUM   , strata = ~ CSTRATM   , weights = ~ PATWT   , data = namcs2019sv_df) attr(mysurvey, \"label\") = \"NAMCS 2019 PUF\" identical(namcs2019sv, mysurvey) #> [1] TRUE"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"begin-analysis","dir":"Articles","previous_headings":"","what":"Begin analysis","title":"Introduction to surveytable","text":"First, specify survey object ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey label, survey design variables, number observations verify looks correct.","code":"set_survey(namcs2019sv)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"list-variables","dir":"Articles","previous_headings":"Begin analysis","what":"List variables","title":"Introduction to surveytable","text":"var_list() function lists variables survey. avoid unintentionally listing variables survey, can many, starting characters variable names specified. example, list variables start letters age, type: Variables beginning ‘age’ {NAMCS 2019 PUF} table lists variable name; class, type variable; variable label, long name variable. Common classes factor (categorical variable), logical (yes / variable), numeric.","code":"var_list(\"age\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-categorical-and-logical-variables","dir":"Articles","previous_headings":"","what":"Tabulate categorical and logical variables","title":"Introduction to surveytable","text":"main function surveytable package tab(), tabulates variables. operates categorical logical variables, presents estimated counts, standard errors (SEs) 95% confidence intervals (CIs), percentages, SEs CIs. example, tabulate AGER, type: Patient age recode {NAMCS 2019 PUF} table title shows variable label (long variable name) survey label. level variable, table shows: estimated count, standard error, 95% confidence interval; estimated percentage, standard error, 95% confidence interval. NCHS presentation standards. tab() function also applies National Center Health Statistics (NCHS) presentation standards counts percentages, flags estimates , according standards, suppressed, footnoted, reviewed analyst. CIs displayed ones used NCHS presentation standards. Specifically, counts, tables show log Student’s t 95% CI, adaptations complex surveys; percentages, show 95% Korn Graubard CI. One need anything extra perform presentation standards checking – performed automatically. example, let’s tabulate PAYNOCHG: Expected source payment visit: Charge/Charity {NAMCS 2019 PUF} table tells us , according NCHS presentation standards, estimated number visits charge visit suppressed due low precision. However, lack percentage flag indicates estimated percentage visits can shown. Drop missing values. variables might contain missing values (NA). Consider following variable, part actual survey, constructed specifically example: Type specialty (BAD - use) {NAMCS 2019 PUF} calculate percentages based non-missing values , use drop_na argument: Type specialty (BAD - use) (knowns ) {NAMCS 2019 PUF} table gives percentages based knowns, , based non-NA values. Multiple tables. Multiple tables can created single command: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"tab(\"AGER\") tab(\"PAYNOCHG\") tab(\"SPECCAT.bad\") tab(\"SPECCAT.bad\", drop_na = TRUE) tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"entire-population","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Entire population","title":"Introduction to surveytable","text":"Estimate total count entire population using total() command: Total {NAMCS 2019 PUF}","code":"total()"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"subsets-or-interactions","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Subsets or interactions","title":"Introduction to surveytable","text":"create table AGER value variable SEX, type: Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} addition giving long name variable tabulated, title table reflects value subsetting variable (case, either Female Male). tab_subset() command, table (, subset), percentages add 100%. tab_cross() function similar – crosses interacts two variables generates table using new variable. Thus, create table interaction AGER SEX, type: (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} estimated counts produced tab_subset() tab_cross() , percentages different. tab_subset() command, within table (, within subset), percentages add 100%. hand, tab_cross(), percentages across entire population add 100%.","code":"tab_subset(\"AGER\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-numeric-variables","dir":"Articles","previous_headings":"","what":"Tabulate numeric variables","title":"Introduction to surveytable","text":"tab() tab_subset() functions also work numeric variables, though variables, output different. tabulate NUMMED (number medications), numeric variable, type: Number medications coded {NAMCS 2019 PUF} , table title shows variable label (long variable name) survey label. table shows percentage values missing (NA), mean, standard error mean (SEM), standard deviation (SD). Subsetting works : Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF}","code":"tab(\"NUMMED\") tab_subset(\"NUMMED\", \"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"perform-statistical-hypothesis-testing","dir":"Articles","previous_headings":"","what":"Perform statistical hypothesis testing","title":"Introduction to surveytable","text":"tab_subset() function makes easy perform hypothesis testing using test argument. argument TRUE, test association performed. addition, t-tests pairs levels performed well.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables","title":"Introduction to surveytable","text":"Consider relationship AGER SPECCAT: Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association Patient age recode Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15-24 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 25-44 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 45-64 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 65-74 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 75 years ) {NAMCS 2019 PUF} According tables, association physician specialty type patient age. instance, patients 15 years, statistical difference primary care physician specialty medical care specialty. older patients, 45-64 age group, statistical difference two specialty types. another example, consider relationship MRI SPECCAT: MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association MRI Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = FALSE) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = TRUE) {NAMCS 2019 PUF} According tables, statistical association MRI physician specialty. 3 specialty types, minority visits MRI’s. visits MRI’s, statistical difference specialty types. general rule thumb, since statistical association MRI physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate MRI without subsetting SPECCAT.","code":"tab_subset(\"AGER\", \"SPECCAT\", test = TRUE) tab_subset(\"MRI\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"numeric-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Numeric variables","title":"Introduction to surveytable","text":"relationship NUMMED AGER: Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF} Association Number medications coded Patient age recode {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Patient age recode {NAMCS 2019 PUF} According tables, association number medications age category. NUMMED statistically similar “15 years” “15-24 years” AGER categories. statistically different pairs age categories. Finally, let’s look relationship NUMMED SPECCAT: Number medications coded (different levels Type specialty (Primary, Medical, Surgical)) {NAMCS 2019 PUF} Association Number medications coded Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According tables, association number medications physician specialty type. NUMMED statistically similar pairs physician specialties. general rule thumb, since statistical association number medications physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate NUMMED without subsetting SPECCAT.","code":"tab_subset(\"NUMMED\", \"AGER\", test = TRUE) tab_subset(\"NUMMED\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables-single-variable","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables (single variable)","title":"Introduction to surveytable","text":"test whether pair SPECCAT levels statistically similar different, type: Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According , surgical medical care specialties statistically similar, statistically different primary care.","code":"tab(\"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"calculate-rates","dir":"Articles","previous_headings":"","what":"Calculate rates","title":"Introduction to surveytable","text":"rate ratio count estimates based survey question divided population size, assumed known. example, number physician visits per 100 people population rate: number physician visits estimated namcs2019sv survey, number people population comes another source. calculate rates, addition survey, need source information population size. typically use function read.csv() load population figures get correct format. surveytable package comes object called uspop2019 contains several population figures use examples. Let’s examine uspop2019: overall population size country whole : overall population size, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame column called Level matches levels variable survey, column called Population gives size population level. example, AGER, data frame follows: Now appropriate population figures, rates table obtained typing: Patient age recode (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population figures following format: data frame, rates table obtained typing: Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) #> [1] \"list\" names(uspop2019) #> [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         #> [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total #> [1] 323186697 total_rate(uspop2019$total) uspop2019$AGER #>               Level Population #> 1    Under 15 years   60526656 #> 2       15-24 years   41718700 #> 3       25-44 years   85599410 #> 4       45-64 years   82562049 #> 5       65-74 years   31260202 #> 6 75 years and over   21519680 tab_rate(\"AGER\", uspop2019$AGER) uspop2019$`AGER x SEX` #>                Level Subset Population #> 1     Under 15 years Female   29604762 #> 2        15-24 years Female   20730118 #> 3        25-44 years Female   43192143 #> 4        45-64 years Female   42508901 #> 5        65-74 years Female   16673240 #> 6  75 years and over Female   12421444 #> 7     Under 15 years   Male   30921894 #> 8        15-24 years   Male   20988582 #> 9        25-44 years   Male   42407267 #> 10       45-64 years   Male   40053148 #> 11       65-74 years   Male   14586962 #> 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-or-modify-variables","dir":"Articles","previous_headings":"","what":"Create or modify variables","title":"Introduction to surveytable","text":"situations, might necessary modify survey variables, create new ones. section describes . Convert factor logical. variable MAJOR (major reason visit) several levels. Major reason visit {NAMCS 2019 PUF} Notice one levels called \"Preventive care\". Suppose analyst interested whether visit preventive care visit – interested visit types. can create new variable called Preventive care visits TRUE preventive care visits FALSE types visits, follows: Preventive care visits {NAMCS 2019 PUF} creates logical variable TRUE preventive care visits tabulates . using var_case() function, specify name new logical variable created, existing factor variable, one levels factor variable set TRUE logical variable. Thus, analyst interested surgery-related visits, indicated two different levels MAJOR, type: Surgery-related visits {NAMCS 2019 PUF} Collapse levels. variable PRIMCARE (whether physician patient’s primary care provider) levels Unknown Blank, among others. patient’s primary care provider? {NAMCS 2019 PUF} collapse Unknown Blank single level, type: patient’s primary care provider? {NAMCS 2019 PUF} Convert numeric factor. variable AGE numeric. Patient age years {NAMCS 2019 PUF} create new variable age categories based AGE, type: Age group {NAMCS 2019 PUF} var_cut() command, specify following information: name new categorical variable; name existing numeric variable; cut points – note intervals inclusive right; category labels. Check whether variable true. series logical variables, can check whether TRUE using var_any() command. physician visit considered “imaging services” visit number imaging services ordered provided. Imaging services indicated using logical variables, MRI XRAY. create Imaging services variable, type: Imaging services {NAMCS 2019 PUF} Interact variables. tab_cross() function creates table interaction two variables, save interacted variable. create interacted variable, use var_cross() command: Specify name new variable well names two variables interact. Copy variable. Create new variable copy another variable using var_copy(). can modify copy, original remains unchanged. example: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} , AGER variable remains unchanged, Age group variable fewer categories. variables data frame. Recall survey objects element called variables, data frame contains survey variables. advanced users can create modify variables variables data frame directly. modify variables, must call set_survey() . example: Survey info {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} Note, however, var_*() functions modify survey object specified set_survey() directly. Rather, modify variables inside following data frame: surveytable:::env$survey$variables. use var_*() functions need access modified / created variables, ’s look. example:","code":"tab(\"MAJOR\") var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") var_case(\"Surgery-related visits\"   , \"MAJOR\"   , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") tab(\"PRIMCARE\") var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\") tab(\"AGE\") var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) tab(\"Age group\") var_any(\"Imaging services\"   , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\"   , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") var_cross(\"Age x Sex\", \"AGER\", \"SEX\") var_copy(\"Age group\", \"AGER\") #> Warning in var_copy(\"Age group\", \"AGER\"): Age group: overwriting a variable #> that already exists. var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\") class(namcs2019sv$variables) #> [1] \"data.frame\" namcs2019sv$variables$`Medicare and Medicaid` = (    namcs2019sv$variables$PAYMCARE & namcs2019sv$variables$PAYMCAID) set_survey(namcs2019sv) tab(\"Medicare and Medicaid\") var_cross(\"newvar\", \"MAJOR\", \"AGER\") # This should give NULL. The new variable does not exist here: namcs2019sv$variables$newvar #> NULL # Rather, the new variable is here: str(surveytable:::env$survey$variables$newvar) #>  Factor w/ 42 levels \"Blank : Under 15 years\",..: 17 31 24 38 38 24 30 31 24 38 ... #>  - attr(*, \"label\")= chr \"(Major reason for this visit) x (Patient age recode)\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"save-the-output","dir":"Articles","previous_headings":"","what":"Save the output","title":"Introduction to surveytable","text":"tab* total* functions argument called csv specifies name comma-separated values (CSV) file save output . Alternatively, can name default CSV output file using set_output() function. example, following directs surveytable send future output CSV file, create tables, turn sending output file: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF} tabulation functions called within R Markdown notebook, produce HTML tables. makes easy incorporate output surveytable package directly documents, presentations, “shiny” web apps, output types. Finally, tabulation functions return tables produce. advanced analysts can use functionality integrate surveytable programming tasks.","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) tab(\"MDDO\", \"SPECCAT\", \"MSA\") set_output(csv = \"\") #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Alex Strashny. Author, maintainer.","code":""},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Strashny (2023). surveytable: Formatted Survey Estimates. https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable.","code":"@Manual{,   title = {surveytable: Formatted Survey Estimates},   author = {Alex Strashny},   year = {2023},   note = {https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable}, }"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"survey-table-formatted-survey-estimates","dir":"","previous_headings":"","what":"Formatted Survey Estimates","title":"Formatted Survey Estimates","text":"surveytable package provides short understandable commands generate tabulated, formatted, rounded survey estimates. One useful function, operates categorical logical variables, tabulates estimated counts percentages standard errors confidence intervals. functions list variables survey, estimate total population, tabulate survey subsets variable interactions, tabulate numeric variables, tabulate rates, create modify survey variables, perform hypothesis tests, save output. tabulation functions check National Center Health Statistics (NCHS) presentation standards flag low-precision estimates. surveytable code called R Markdown notebook Quarto document, generates HTML tables, can incorporated directly documents.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Formatted Survey Estimates","text":"Install CRAN: get development version GitHub:","code":"install.packages(\"surveytable\") install.packages(c(\"remotes\", \"git2r\")) remotes::install_github(\"CDCgov/surveytable\", upgrade = \"never\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Formatted Survey Estimates","text":"Find documentation surveytable : https://cdcgov.github.io/surveytable/","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Formatted Survey Estimates","text":"basic example, get started. Survey info {NAMCS 2019 PUF} Patient age recode {NAMCS 2019 PUF}","code":"library(surveytable) set_survey(namcs2019sv) tab(\"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"public-domain-standard-notice","dir":"","previous_headings":"","what":"Public Domain Standard Notice","title":"Formatted Survey Estimates","text":"repository constitutes work United States Government subject domestic copyright protection 17 USC § 105. repository public domain within United States, copyright related rights work worldwide waived CC0 1.0 Universal public domain dedication. contributions repository released CC0 dedication. submitting pull request agreeing comply waiver copyright interest.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"license-standard-notice","dir":"","previous_headings":"","what":"License Standard Notice","title":"Formatted Survey Estimates","text":"repository utilizes code licensed terms Apache Software License therefore licensed ASL v2 later. source code repository free: can redistribute /modify terms Apache Software License version 2, (option) later version. source code repository distributed hope useful, WITHOUT WARRANTY; without even implied warranty MERCHANTABILITY FITNESS PARTICULAR PURPOSE. See Apache Software License details. received copy Apache Software License along program. , see https://www.apache.org/licenses/LICENSE-2.0.html source code forked open source projects inherit license.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"privacy-standard-notice","dir":"","previous_headings":"","what":"Privacy Standard Notice","title":"Formatted Survey Estimates","text":"repository contains non-sensitive, publicly available data information. material community participation covered Disclaimer Code Conduct. information CDC’s privacy policy, please visit https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"contributing-standard-notice","dir":"","previous_headings":"","what":"Contributing Standard Notice","title":"Formatted Survey Estimates","text":"Anyone encouraged contribute repository forking submitting pull request. (new GitHub, might start basic tutorial.) contributing project, grant world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license users terms Apache Software License v2 later. comments, messages, pull requests, submissions received CDC including GitHub page may subject applicable federal law, including limited Federal Records Act, may archived. Learn https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"records-management-standard-notice","dir":"","previous_headings":"","what":"Records Management Standard Notice","title":"Formatted Survey Estimates","text":"repository source government records, copy increase collaboration collaborative potential. government records published CDC web site.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"additional-standard-notices","dir":"","previous_headings":"","what":"Additional Standard Notices","title":"Formatted Survey Estimates","text":"Please refer CDC’s Template Repository information contributing repository, public domain notices disclaimers, code conduct.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. 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Submission of Contributions","title":"Apache License","text":"Unless explicitly state otherwise, Contribution intentionally submitted inclusion Work Licensor shall terms conditions License, without additional terms conditions. Notwithstanding , nothing herein shall supersede modify terms separate license agreement may executed Licensor regarding Contributions.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"6-trademarks","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"6. Trademarks","title":"Apache License","text":"License grant permission use trade names, trademarks, service marks, product names Licensor, except required reasonable customary use describing origin Work reproducing content NOTICE file.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"7-disclaimer-of-warranty","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"7. Disclaimer of Warranty","title":"Apache License","text":"Unless required applicable law agreed writing, Licensor provides Work (Contributor provides Contributions) “” BASIS, WITHOUT WARRANTIES CONDITIONS KIND, either express implied, including, without limitation, warranties conditions TITLE, NON-INFRINGEMENT, MERCHANTABILITY, FITNESS PARTICULAR PURPOSE. solely responsible determining appropriateness using redistributing Work assume risks associated exercise permissions License.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"8-limitation-of-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"8. 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Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner]  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a codebook for the survey — codebook","title":"Create a codebook for the survey — codebook","text":"Create codebook survey","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a codebook for the survey — codebook","text":"","code":"codebook(all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a codebook for the survey — codebook","text":"tabulate variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a codebook for the survey — codebook","text":"list tables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a codebook for the survey — codebook","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  codebook() #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  #>                                     Codebook {NAMCS 2019 PUF}                                      #> +------------------------------------------------------------------------------------------------+ #> ¦ Item no. ¦ Variable    ¦ Description           ¦ Class   ¦ Missing (%) ¦ Values                ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        1 ¦ CPSUM       ¦ Masked provider       ¦ numeric ¦           0 ¦ 100001 - 100398       ¦ #> ¦          ¦             ¦ marker                ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        2 ¦ CSTRATM     ¦ Masked sampling       ¦ numeric ¦           0 ¦ 10119101 - 10419115   ¦ #> ¦          ¦             ¦ stratum from which    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ provider was selected ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        3 ¦ PATWT       ¦ Patient visit weight  ¦ numeric ¦           0 ¦ 7064.00718 -          ¦ #> ¦          ¦             ¦ used for national and ¦         ¦             ¦ 1120996.55599         ¦ #> ¦          ¦             ¦ subnational estimates ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        4 ¦ MDDO        ¦ Type of doctor (MD or ¦ factor  ¦           0 ¦ M.D. - Doctor of      ¦ #> ¦          ¦             ¦ DO)                   ¦         ¦             ¦ Medicine, D.O. -      ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Doctor of Osteopathy  ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        5 ¦ SPECCAT     ¦ Type of specialty     ¦ factor  ¦           0 ¦ Primary care          ¦ #> ¦          ¦             ¦ (Primary, Medical,    ¦         ¦             ¦ specialty, Surgical   ¦ #> ¦          ¦             ¦ Surgical)             ¦         ¦             ¦ care specialty,       ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Medical care          ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ specialty             ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        6 ¦ MSA         ¦ Metropolitan          ¦ factor  ¦           0 ¦ MSA (Metropolitan     ¦ #> ¦          ¦             ¦ Statistical Area      ¦         ¦             ¦ Statistical Area),    ¦ #> ¦          ¦             ¦ Status of physician   ¦         ¦             ¦ Non-MSA               ¦ #> ¦          ¦             ¦ location              ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        7 ¦ AGER        ¦ Patient age recode    ¦ factor  ¦           0 ¦ Under 15 years, 15-24 ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ years, 25-44 years,   ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ 45-64 years, 65-74    ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ years, 75 years and   ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ over                  ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        8 ¦ SEX         ¦ Patient sex           ¦ factor  ¦           0 ¦ Female, Male          ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        9 ¦ AGE         ¦ Patient age in years  ¦ numeric ¦           0 ¦ 0 - 94                ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       10 ¦ NOPAY       ¦ Expected source of    ¦ factor  ¦           0 ¦ One or more           ¦ #> ¦          ¦             ¦ payment for visit: No ¦         ¦             ¦ categories marked, No ¦ #> ¦          ¦             ¦ answer to item        ¦         ¦             ¦ categories marked     ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       11 ¦ PAYPRIV     ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Private insurance     ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       12 ¦ PAYMCARE    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Medicare              ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       13 ¦ PAYMCAID    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Medicaid or CHIP or   ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ other state-based     ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ program               ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       14 ¦ PAYWKCMP    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Workers Compensation  ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       15 ¦ PAYOTH      ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Other                 ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       16 ¦ PAYDK       ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Unknown               ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       17 ¦ PAYSELF     ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Self-pay              ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       18 ¦ PAYNOCHG    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit: No ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Charge/Charity        ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       19 ¦ PRIMCARE    ¦ Are you the patient's ¦ factor  ¦           0 ¦ Blank, Unknown, Yes,  ¦ #> ¦          ¦             ¦ primary care          ¦         ¦             ¦ No                    ¦ #> ¦          ¦             ¦ provider?             ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       20 ¦ REFER       ¦ Was patient referred  ¦ factor  ¦           0 ¦ Blank, Unknown, Not   ¦ #> ¦          ¦             ¦ for visit?            ¦         ¦             ¦ applicable, Yes, No   ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       21 ¦ SENBEFOR    ¦ Has this patient been ¦ factor  ¦           0 ¦ Yes, established      ¦ #> ¦          ¦             ¦ seen in your practice ¦         ¦             ¦ patient, No, new      ¦ #> ¦          ¦             ¦ before?               ¦         ¦             ¦ patient               ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       22 ¦ MAJOR       ¦ Major reason for this ¦ factor  ¦           0 ¦ Blank, New problem    ¦ #> ¦          ¦             ¦ visit                 ¦         ¦             ¦ (less than 3 mos.     ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ onset), Chronic       ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ problem, routine,     ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Chronic problem,      ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ flare-up,             ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Pre-surgery,          ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Post-surgery,         ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Preventive care       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       23 ¦ NUMMED      ¦ Number of medications ¦ numeric ¦           0 ¦ 0 - 30                ¦ #> ¦          ¦             ¦ coded                 ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       24 ¦ ANYIMAGE    ¦ Any imaging           ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       25 ¦ BONEDENS    ¦ Bone mineral density  ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       26 ¦ CATSCAN     ¦ CT Scan               ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       27 ¦ ECHOCARD    ¦ Echocardiogram        ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       28 ¦ OTHULTRA    ¦ Ultrasound            ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       29 ¦ MAMMO       ¦ Mammography           ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       30 ¦ MRI         ¦ MRI                   ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       31 ¦ XRAY        ¦ X-ray                 ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       32 ¦ OTHIMAGE    ¦ Other imaging         ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       33 ¦ SPECCAT.bad ¦ Type of specialty     ¦ factor  ¦          20 ¦ Primary care          ¦ #> ¦          ¦             ¦ (BAD - do not use)    ¦         ¦             ¦ specialty, Surgical   ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ care specialty,       ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Medical care          ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ specialty             ¦ #> +------------------------------------------------------------------------------------------------+ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":null,"dir":"Reference","previous_headings":"","what":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"Selected variables data system visits office-based physicians. Note unit observation visits, patients - distinction important since single patient can make multiple visits.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"","code":"namcs2019sv  namcs2019sv_df"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"object class survey.design2 (inherits survey.design) 8250 rows 33 columns. object class data.frame 8250 rows 33 columns.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/namcs2019_sas.zip Survey design variables: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/readme2019-sas.txt SAS formats: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/nam19for.txt Documentation: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/doc2019-508.pdf National Summary Tables: https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"namcs2019sv_df data frame. namcs2019sv survey object created namcs2019sv_df using [survey::svydesign()].","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Print surveytable tables — print.surveytable_table","title":"Print surveytable tables — print.surveytable_table","text":"Print surveytable tables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print surveytable tables — print.surveytable_table","text":"","code":"# S3 method for surveytable_table print(x, ...)  # S3 method for surveytable_list print(x, ...)"},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print surveytable tables — print.surveytable_table","text":"x object class surveytable_table surveytable_list. ... ignored","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print surveytable tables — print.surveytable_table","text":"x invisibly.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print surveytable tables — print.surveytable_table","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  table1 = tab(\"AGER\") print(table1) #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  table_many = tab(\"MDDO\", \"SPECCAT\", \"MSA\") print(table_many) #>                            Type of doctor (MD or DO) {NAMCS 2019 PUF}                            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦      980,280 ¦   48,388 ¦  889,842 ¦ 1,079,910 ¦    94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦       56,204 ¦    6,602 ¦   44,597 ¦    70,832 ¦     5.4 ¦ 0.7 ¦  4.2 ¦  6.9 ¦ #> ¦ of Osteopathy ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>  #>                 Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                 #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care  ¦      521,466 ¦   31,136 ¦  463,840 ¦  586,252 ¦    50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦      214,832 ¦   31,110 ¦  161,661 ¦  285,490 ¦    20.7 ¦ 3   ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care  ¦      300,186 ¦   43,497 ¦  225,806 ¦  399,067 ¦    29   ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>            Metropolitan Statistical Area Status of physician location {NAMCS 2019 #> PUF}            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA           ¦      973,676 ¦   50,515 ¦  879,490 ¦ 1,077,947 ¦    93.9 ¦ 1.7 ¦ 89.7 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Statistical   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Area)         ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA       ¦       62,809 ¦   17,549 ¦   36,249 ¦   108,830 ¦     6.1 ¦ 1.7 ¦  3.2 ¦ 10.3 ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>"},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":null,"dir":"Reference","previous_headings":"","what":"Rounding counts — set_count_1k","title":"Rounding counts — set_count_1k","text":"Determines counts rounded.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rounding counts — set_count_1k","text":"","code":"set_count_1k()  set_count_int()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rounding counts — set_count_1k","text":"(Nothing.)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rounding counts — set_count_1k","text":"set_count_1k(): round counts nearest 1,000. set_count_int(): round counts nearest integer.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rounding counts — set_count_1k","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  set_count_int() #> * Rounding counts to the nearest integer. #> * ?set_count_int for other options. total() #>                    Total {NAMCS 2019 PUF}                    #> +----------------------------------------------------------+ #> ¦        Number ¦         SE ¦          LL ¦            UL ¦ #> +---------------+------------+-------------+---------------¦ #> ¦ 1,036,484,356 ¦ 48,836,217 ¦ 945,013,590 ¦ 1,136,808,860 ¦ #> +----------------------------------------------------------+ #>   (Checked presentation standards. Nothing to                #>   report.)                                                   #>   set_count_1k() #> * Rounding counts to the nearest 1,000. #> * ?set_count_1k for other options. total() #>                      Total {NAMCS 2019 PUF}                       #> +---------------------------------------------------------------+ #> ¦  Number (000) ¦      SE (000) ¦      LL (000) ¦      UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦     1,036,484 ¦        48,836 ¦       945,014 ¦     1,136,809 ¦ #> +---------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Set output defaults — set_output","title":"Set output defaults — set_output","text":"show_output() shows current defaults.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set output defaults — set_output","text":"","code":"set_output(drop_na = NULL, max_levels = NULL, csv = NULL)  show_output()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set output defaults — set_output","text":"drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file \"\" turn CSV output","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set output defaults — set_output","text":"(Nothing.)","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set output defaults — set_output","text":"","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) tab(\"AGER\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  set_output(csv = \"\") # Turn off CSV output #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify the survey to analyze — set_survey","title":"Specify the survey to analyze — set_survey","text":"need specify survey functions, tab(), work.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(design, opts = \"NCHS\", csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify the survey to analyze — set_survey","text":"design either survey object (survey.design svyrep.design) data.frame unweighted survey. opts set certain options. See . csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify the survey to analyze — set_survey","text":"Info survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify the survey to analyze — set_survey","text":"opts: \"nchs\": Round counts nearest 1,000 -- see set_count_1k(). Identify low-precision estimates (surveytable.check_present option). Percentage CI's: adjust Korn-Graubard CI's number degrees freedom, matching SUDAAN calculation (surveytable.adjust_svyciprop option). \"general\": Round counts nearest integer -- see set_count_int(). look low-precision estimates (surveytable.check_present option). Percentage CI's: use standard Korn-Graubard CI's. Optionally, survey can attribute called label, long name survey. Optionally, variable survey can attribute called label, variable's long name.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Show package options — show_options","title":"Show package options — show_options","text":"Show package options","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show package options — show_options","text":"","code":"show_options(sw = \"surveytable\")"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show package options — show_options","text":"sw starting characters","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show package options — show_options","text":"List options values.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show package options — show_options","text":"","code":"show_options() #> $surveytable.adjust_svyciprop #> [1] TRUE #>  #> $surveytable.adjust_svyciprop.df_method #> [1] \"NHIS\" #>  #> $surveytable.check_present #> [1] TRUE #>  #> $surveytable.csv #> [1] \"\" #>  #> $surveytable.drop_na #> [1] FALSE #>  #> $surveytable.max_levels #> [1] 20 #>  #> $surveytable.names_count #> [1] \"Number (000)\" \"SE (000)\"     \"LL (000)\"     \"UL (000)\"     #>  #> $surveytable.names_prct #> [1] \"Percent\" \"SE\"      \"LL\"      \"UL\"      #>  #> $surveytable.present_count #> [1] \".present_count\" #>  #> $surveytable.present_prop #> [1] \".present_prop\" #>  #> $surveytable.present_restricted #> [1] \".present_restricted\" #>  #> $surveytable.rate_per #> [1] 100 #>  #> $surveytable.survey_label #> [1] \"NAMCS 2019 PUF\" #>  #> $surveytable.svychisq_statistic #> [1] \"F\" #>  #> $surveytable.tx_count #> [1] \".tx_count_1k\" #>  #> $surveytable.tx_prct #> [1] \".tx_prct\" #>  #> $surveytable.tx_rate #> [1] \".tx_rate\" #>"},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — surveytable-options","title":"Package options — surveytable-options","text":"Run show_options() see available options.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":null,"dir":"Reference","previous_headings":"","what":"surveytable: Formatted Survey Estimates — surveytable-package","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Short understandable commands generate tabulated, formatted, rounded survey estimates. Mostly wrapper 'survey' package (Lumley (2004) doi: 10.18637/jss.v009.i08  https://CRAN.R-project.org/package=survey) identifies low-precision estimates using National Center Health Statistics (NCHS) presentation standards (Parker et al. (2017) https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf, Parker et al. (2023) doi: 10.15620/cdc:124368 ).","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Maintainer: Alex Strashny AStrashny@cdc.gov (ORCID)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a survey, while preserving variable labels — survey_subset","title":"Subset a survey, while preserving variable labels — survey_subset","text":"Subset survey, preserving variable labels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"survey_subset(design, subset, label)"},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a survey, while preserving variable labels — survey_subset","text":"design survey object subset expression specifying sub-population label survey label newly created survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a survey, while preserving variable labels — survey_subset","text":"new survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"children = survey_subset(namcs2019sv, AGE < 18, \"Children < 18\") set_survey(children) #>                          Survey info {Children < 18}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        1,066 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (139) clusters.                           ¦ #> ¦           ¦              ¦ survey_subset(namcs2019sv, AGE < 18, \"Children ¦ #> ¦           ¦              ¦ < 18\")                                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"AGER\") #>                               Patient age recode {Children < 18}                                #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    86.1 ¦ 1.6 ¦ 82.6 ¦ 89.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       19,004 ¦    2,872 ¦   14,051 ¦   25,702 ¦    13.9 ¦ 1.6 ¦ 10.8 ¦ 17.4 ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"version survey::svyciprop() adjusts degrees freedom method = \"beta\".","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"svyciprop_adjusted(   formula,   design,   method = c(\"logit\", \"likelihood\", \"asin\", \"beta\", \"mean\", \"xlogit\"),   level = 0.95,   df_method,   ... )"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"formula see survey::svyciprop(). design see survey::svyciprop(). method see survey::svyciprop(). level see survey::svyciprop(). df_method df calculated: \"default\" \"NHIS\". ... see survey::svyciprop().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"point estimate proportion, confidence interval attribute.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"Written Makram Talih 2019. df_method: \"default\", df = degf(design); \"NHIS\", df = nrow(design) - 1. use function tabulations, call set_survey() opts = \"NCHS\" argument, type: options(surveytable.adjust_svyciprop = TRUE).","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"set_survey(namcs2019sv, opts = \"NCHS\") #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"AGER\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate variables — tab","title":"Tabulate variables — tab","text":"Tabulate categorical (factor), logical, numeric variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate variables — tab","text":"","code":"tab(   ...,   test = FALSE,   alpha = 0.05,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate variables — tab","text":"... names variables (quotes) test perform hypothesis tests? alpha significance level tests drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate variables — tab","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate variables — tab","text":"categorical logical variables, presents estimated counts, standard errors (SEs) confidence intervals (CIs), percentages, SEs CIs. Checks presentation guidelines counts percentages flags estimates , according guidelines, suppressed, footnoted, reviewed analyst. numeric variables, presents percentage observations known values, mean known values, standard error mean (SEM), standard deviation (SD). CIs calculated 95% confidence level. CIs count estimates log Student's t CIs, adaptations complex surveys. CIs percentage estimates Korn Graubard CIs.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate variables — tab","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"AGER\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  tab(\"MDDO\", \"SPECCAT\", \"MSA\") #>                            Type of doctor (MD or DO) {NAMCS 2019 PUF}                            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦      980,280 ¦   48,388 ¦  889,842 ¦ 1,079,910 ¦    94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦       56,204 ¦    6,602 ¦   44,597 ¦    70,832 ¦     5.4 ¦ 0.7 ¦  4.2 ¦  6.9 ¦ #> ¦ of Osteopathy ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>  #>                 Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                 #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care  ¦      521,466 ¦   31,136 ¦  463,840 ¦  586,252 ¦    50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦      214,832 ¦   31,110 ¦  161,661 ¦  285,490 ¦    20.7 ¦ 3   ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care  ¦      300,186 ¦   43,497 ¦  225,806 ¦  399,067 ¦    29   ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>            Metropolitan Statistical Area Status of physician location {NAMCS 2019 #> PUF}            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA           ¦      973,676 ¦   50,515 ¦  879,490 ¦ 1,077,947 ¦    93.9 ¦ 1.7 ¦ 89.7 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Statistical   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Area)         ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA       ¦       62,809 ¦   17,549 ¦   36,249 ¦   108,830 ¦     6.1 ¦ 1.7 ¦  3.2 ¦ 10.3 ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>   # Numeric variables tab(\"NUMMED\") #> Number of medications coded {NAMCS 2019 PUF} #> +-------------------------------+ #> ¦ % known ¦ Mean ¦   SEM ¦   SD ¦ #> +---------+------+-------+------¦ #> ¦     100 ¦ 3.46 ¦ 0.268 ¦ 4.43 ¦ #> +-------------------------------+ #>   # Hypothesis testing with categorical variables tab(\"AGER\", test = TRUE) #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #> Comparison of all possible pairs of Patient age recode {NAMCS 2019 PUF} #> +-----------------------------------------------------+ #> ¦ Level 1        ¦ Level 2           ¦ p-value ¦ Flag ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 15-24 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 25-44 years       ¦   0.012 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 75 years and over ¦   0.022 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 25-44 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 65-74 years       ¦   0.065 ¦      ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 75 years and over ¦   0.878 ¦      ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 65-74 years    ¦ 75 years and over ¦   0.019 ¦ *    ¦ #> +-----------------------------------------------------+ #>   Design-based t-test. *: p-value <= 0.05               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates — tab_rate","title":"Calculate rates — tab_rate","text":"Calculate rates categorical (factor) logical variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates — tab_rate","text":"","code":"tab_rate(   vr,   pop,   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates — tab_rate","text":"vr variable tabulate pop either single number data.frame columns named Level Population. Level must exactly match levels vr. Population population level vr. per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates — tab_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates — tab_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  # pop is a data frame tab_rate(\"MSA\", uspop2019$MSA) #> Metropolitan Statistical Area Status of physician location (rate per #> 100 population) {NAMCS 2019 PUF} #> +-----------------------------------------------------------+ #> ¦ Level                      ¦  Rate ¦   SE ¦    LL ¦    UL ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ MSA (Metropolitan          ¦ 351.2 ¦ 18.2 ¦ 317.2 ¦ 388.8 ¦ #> ¦ Statistical Area)          ¦       ¦      ¦       ¦       ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ Non-MSA                    ¦ 136.7 ¦ 38.2 ¦  78.9 ¦ 236.8 ¦ #> +-----------------------------------------------------------+ #>   (Checked presentation standards. Nothing to                 #>   report.)                                                    #>   # pop is a single number tab_rate(\"MDDO\", uspop2019$total) #> * Rate based on the entire population. #> Type of doctor (MD or DO) (rate per 100 population) {NAMCS 2019 PUF} #> +---------------------------------------------------------+ #> ¦ Level                      ¦  Rate ¦ SE ¦    LL ¦    UL ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ M.D. - Doctor of Medicine  ¦ 303.3 ¦ 15 ¦ 275.3 ¦ 334.1 ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ D.O. - Doctor of           ¦  17.4 ¦  2 ¦  13.8 ¦  21.9 ¦ #> ¦ Osteopathy                 ¦       ¦    ¦       ¦       ¦ #> +---------------------------------------------------------+ #>   (Checked presentation standards. Nothing to               #>   report.)                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate subsets or interactions — tab_cross","title":"Tabulate subsets or interactions — tab_cross","text":"Create subsets survey using one variable, tabulate another variable within subsets. Interact two variables tabulate.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"tab_cross(   vr,   vrby,   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )  tab_subset(   vr,   vrby,   lvls = c(),   test = FALSE,   alpha = 0.05,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate subsets or interactions — tab_cross","text":"vr variable tabulate vrby use variable subset survey max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file lvls (optional) show levels vrby test perform hypothesis tests? alpha significance level tests drop_na drop missing values (NA)? Categorical variables .","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate subsets or interactions — tab_cross","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate subsets or interactions — tab_cross","text":"tab_subset creates subsets using levels vrby, tabulates vr subset. Optionally, use lvls levels vrby. vr can categorical (factor), logical, numeric. tab_cross crosses interacts vr vrby tabulates new variable. Tables created using tab_subset tab_cross counts different percentages. tab_subset, percentages within subset add 100%. tab_cross, percentages across entire population add 100%. Also see var_cross(). test = TRUE performs test association two variables. Also performs t-tests possible pairs levels vr vrby.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>   # For each SEX, tabulate AGER tab_subset(\"AGER\", \"SEX\") #>                   Patient age recode (Patient sex = Female) {NAMCS 2019 PUF}                    #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       59,958 ¦    7,206 ¦   47,318 ¦   75,974 ¦     9.9 ¦ 1.2 ¦  7.6 ¦ 12.6 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       41,128 ¦    4,532 ¦   33,066 ¦   51,156 ¦     6.8 ¦ 0.7 ¦  5.4 ¦  8.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      113,708 ¦   11,461 ¦   93,256 ¦  138,646 ¦    18.8 ¦ 1.6 ¦ 15.8 ¦ 22.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      175,978 ¦   16,009 ¦  147,153 ¦  210,450 ¦    29.1 ¦ 1.7 ¦ 25.8 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      120,099 ¦   11,066 ¦  100,171 ¦  143,992 ¦    19.8 ¦ 1.5 ¦ 17   ¦ 22.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       94,173 ¦   11,085 ¦   74,682 ¦  118,751 ¦    15.6 ¦ 1.5 ¦ 12.8 ¦ 18.7 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>                    Patient age recode (Patient sex = Male) {NAMCS 2019 PUF}                     #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       57,959 ¦    7,728 ¦   44,570 ¦   75,371 ¦    13.4 ¦ 1.7 ¦ 10.3 ¦ 17.1 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       23,728 ¦    4,344 ¦   16,457 ¦   34,210 ¦     5.5 ¦ 0.8 ¦  4   ¦  7.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦       56,562 ¦    7,277 ¦   43,861 ¦   72,942 ¦    13.1 ¦ 1.3 ¦ 10.7 ¦ 15.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      133,528 ¦   12,956 ¦  110,319 ¦  161,619 ¦    30.9 ¦ 1.6 ¦ 27.8 ¦ 34.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦       86,766 ¦    6,767 ¦   74,409 ¦  101,176 ¦    20.1 ¦ 1.5 ¦ 17.3 ¦ 23.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       72,896 ¦    6,661 ¦   60,872 ¦   87,296 ¦    16.9 ¦ 1.5 ¦ 14   ¦ 20.2 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>   # Same counts as tab_subset(), but different percentages. tab_cross(\"AGER\", \"SEX\") #>                      (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}                      #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       59,958 ¦    7,206 ¦   47,318 ¦   75,974 ¦     5.8 ¦ 0.7 ¦  4.5 ¦  7.3 ¦ #> ¦ years :       ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       41,128 ¦    4,532 ¦   33,066 ¦   51,156 ¦     4   ¦ 0.4 ¦  3.2 ¦  4.9 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦      113,708 ¦   11,461 ¦   93,256 ¦  138,646 ¦    11   ¦ 1   ¦  9   ¦ 13.2 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      175,978 ¦   16,009 ¦  147,153 ¦  210,450 ¦    17   ¦ 1.1 ¦ 14.9 ¦ 19.3 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦      120,099 ¦   11,066 ¦  100,171 ¦  143,992 ¦    11.6 ¦ 1   ¦  9.7 ¦ 13.7 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       94,173 ¦   11,085 ¦   74,682 ¦  118,751 ¦     9.1 ¦ 0.9 ¦  7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       57,959 ¦    7,728 ¦   44,570 ¦   75,371 ¦     5.6 ¦ 0.7 ¦  4.3 ¦  7.2 ¦ #> ¦ years : Male  ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       23,728 ¦    4,344 ¦   16,457 ¦   34,210 ¦     2.3 ¦ 0.4 ¦  1.6 ¦  3.2 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦       56,562 ¦    7,277 ¦   43,861 ¦   72,942 ¦     5.5 ¦ 0.6 ¦  4.3 ¦  6.8 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      133,528 ¦   12,956 ¦  110,319 ¦  161,619 ¦    12.9 ¦ 1   ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦       86,766 ¦    6,767 ¦   74,409 ¦  101,176 ¦     8.4 ¦ 0.6 ¦  7.2 ¦  9.7 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       72,896 ¦    6,661 ¦   60,872 ¦   87,296 ¦     7   ¦ 0.6 ¦  5.9 ¦  8.3 ¦ #> ¦ over : Male   ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>   # Numeric variables tab_subset(\"NUMMED\", \"AGER\") #> Number of medications coded (for different levels of Patient age #> recode) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level             ¦ % known ¦ Mean ¦   SEM ¦   SD ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ Under 15 years    ¦     100 ¦ 1.58 ¦ 0.168 ¦ 1.75 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 15-24 years       ¦     100 ¦ 1.64 ¦ 0.112 ¦ 1.7  ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 25-44 years       ¦     100 ¦ 2.15 ¦ 0.225 ¦ 2.74 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 45-64 years       ¦     100 ¦ 3.49 ¦ 0.303 ¦ 4.49 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 65-74 years       ¦     100 ¦ 4.44 ¦ 0.431 ¦ 5.03 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 75 years and over ¦     100 ¦ 5.53 ¦ 0.494 ¦ 5.59 ¦ #> +---------------------------------------------------+ #>   # Hypothesis testing tab_subset(\"NUMMED\", \"AGER\", test = TRUE) #> Number of medications coded (for different levels of Patient age #> recode) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level             ¦ % known ¦ Mean ¦   SEM ¦   SD ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ Under 15 years    ¦     100 ¦ 1.58 ¦ 0.168 ¦ 1.75 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 15-24 years       ¦     100 ¦ 1.64 ¦ 0.112 ¦ 1.7  ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 25-44 years       ¦     100 ¦ 2.15 ¦ 0.225 ¦ 2.74 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 45-64 years       ¦     100 ¦ 3.49 ¦ 0.303 ¦ 4.49 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 65-74 years       ¦     100 ¦ 4.44 ¦ 0.431 ¦ 5.03 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 75 years and over ¦     100 ¦ 5.53 ¦ 0.494 ¦ 5.59 ¦ #> +---------------------------------------------------+ #>  #> Association between Number of medications coded and Patient age recode #> {NAMCS 2019 PUF} #> +-----------------------------------+ #> ¦         p-value ¦ Flag            ¦ #> +-----------------+-----------------¦ #> ¦               0 ¦ *               ¦ #> +-----------------------------------+ #>   Wald test. *: p-value <= 0.05       #>  #> Comparison of Number of medications coded across all possible pairs of #> Patient age recode {NAMCS 2019 PUF} #> +-----------------------------------------------------+ #> ¦ Level 1        ¦ Level 2           ¦ p-value ¦ Flag ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 15-24 years       ¦   0.739 ¦      ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 25-44 years       ¦   0.043 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 25-44 years       ¦   0.029 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 65-74 years       ¦   0.007 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 65-74 years    ¦ 75 years and over ¦   0.002 ¦ *    ¦ #> +-----------------------------------------------------+ #>   Design-based t-test. *: p-value <= 0.05               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates for subsets — tab_subset_rate","title":"Calculate rates for subsets — tab_subset_rate","text":"Create subsets survey using one variable, tabulate rates another variable within subsets.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"tab_subset_rate(   vr,   vrby,   pop,   lvls = c(),   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates for subsets — tab_subset_rate","text":"vr variable tabulate vrby use variable subset survey pop data.frame columns named Level, Subset, Population. Level must exactly match levels vr. Subset must exactly match levels vrby. Population population level vr vrby. lvls (optional) show levels vrby per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates for subsets — tab_subset_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`) #> Patient age recode (Patient sex = Female) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level             ¦        Rate ¦          SE ¦          LL ¦          UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years    ¦       202.5 ¦        24.3 ¦       159.8 ¦       256.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years       ¦       198.4 ¦        21.9 ¦       159.5 ¦       246.8 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years       ¦       263.3 ¦        26.5 ¦       215.9 ¦       321   ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years       ¦       414   ¦        37.7 ¦       346.2 ¦       495.1 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years       ¦       720.3 ¦        66.4 ¦       600.8 ¦       863.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦       758.1 ¦        89.2 ¦       601.2 ¦       956   ¦ #> +---------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                        #>  #> Patient age recode (Patient sex = Male) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level             ¦        Rate ¦          SE ¦          LL ¦          UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years    ¦       187.4 ¦        25   ¦       144.1 ¦       243.7 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years       ¦       113.1 ¦        20.7 ¦        78.4 ¦       163   ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years       ¦       133.4 ¦        17.2 ¦       103.4 ¦       172   ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years       ¦       333.4 ¦        32.3 ¦       275.4 ¦       403.5 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years       ¦       594.8 ¦        46.4 ¦       510.1 ¦       693.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦       801.2 ¦        73.2 ¦       669.1 ¦       959.5 ¦ #> +---------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                        #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":null,"dir":"Reference","previous_headings":"","what":"Total count — total","title":"Total count — total","text":"Total count","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total count — total","text":"","code":"total(csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total count — total","text":"csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total count — total","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total count — total","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  total() #>                      Total {NAMCS 2019 PUF}                       #> +---------------------------------------------------------------+ #> ¦  Number (000) ¦      SE (000) ¦      LL (000) ¦      UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦     1,036,484 ¦        48,836 ¦       945,014 ¦     1,136,809 ¦ #> +---------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Overall rate — total_rate","title":"Overall rate — total_rate","text":"Overall rate","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Overall rate — total_rate","text":"","code":"total_rate(   pop,   per = getOption(\"surveytable.rate_per\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Overall rate — total_rate","text":"pop population per calculate rate per many items population csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Overall rate — total_rate","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Overall rate — total_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  total_rate(uspop2019$total) #>         Total (rate per 100 population) {NAMCS 2019 PUF}          #> +---------------------------------------------------------------+ #> ¦          Rate ¦            SE ¦            LL ¦            UL ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦         320.7 ¦          15.1 ¦         292.4 ¦         351.7 ¦ #> +---------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":null,"dir":"Reference","previous_headings":"","what":"US Population in 2019 — uspop2019","title":"US Population in 2019 — uspop2019","text":"Population estimates civilian non-institutional population United States July 1, 2019. Used calculating rates. usage examples, see *_rate functions.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"US Population in 2019 — uspop2019","text":"","code":"uspop2019"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"US Population in 2019 — uspop2019","text":"object class list length 7.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Are all the variables true? (Logical AND) — var_all","title":"Are all the variables true? (Logical AND) — var_all","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"var_all(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Are all the variables true? (Logical AND) — var_all","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Are all the variables true? (Logical AND) — var_all","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\")) tab(\"Medicare and Medicaid\") #>                          Medicare and Medicaid {NAMCS 2019 PUF}                          #> +--------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ FALSE ¦    1,016,202 ¦   47,395 ¦  927,389 ¦ 1,113,520 ¦      98 ¦ 0.5 ¦ 96.9 ¦ 98.9 ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ TRUE  ¦       20,282 ¦    5,177 ¦   12,120 ¦    33,941 ¦       2 ¦ 0.5 ¦  1.1 ¦  3.1 ¦ #> +--------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                   #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Is any variable true? (Logical OR) — var_any","title":"Is any variable true? (Logical OR) — var_any","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"var_any(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is any variable true? (Logical OR) — var_any","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is any variable true? (Logical OR) — var_any","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_any(\"Imaging services\" , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\" , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") #>                            Imaging services {NAMCS 2019 PUF}                            #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦      901,115 ¦   43,298 ¦  820,085 ¦  990,151 ¦    86.9 ¦ 1.1 ¦ 84.6 ¦ 89.1 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE  ¦      135,369 ¦   13,574 ¦  111,134 ¦  164,890 ¦    13.1 ¦ 1.1 ¦ 10.9 ¦ 15.4 ¦ #> +-------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert factor to logical — var_case","title":"Convert factor to logical — var_case","text":"Convert factor logical","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert factor to logical — var_case","text":"","code":"var_case(newvr, vr, cases)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert factor to logical — var_case","text":"newvr name new logical variable created vr factor variable cases one levels vr converted TRUE. levels converted FALSE.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert factor to logical — var_case","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert factor to logical — var_case","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") #>                         Preventive care visits {NAMCS 2019 PUF}                         #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦      812,861 ¦   45,220 ¦  728,841 ¦  906,566 ¦    78.4 ¦ 1.7 ¦ 74.9 ¦ 81.7 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE  ¦      223,624 ¦   18,520 ¦  190,068 ¦  263,103 ¦    21.6 ¦ 1.7 ¦ 18.3 ¦ 25.1 ¦ #> +-------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                  #>  var_case(\"Surgery-related visits\" , \"MAJOR\" , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") #>                         Surgery-related visits {NAMCS 2019 PUF}                          #> +--------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ FALSE ¦      969,451 ¦   47,976 ¦  879,793 ¦ 1,068,246 ¦    93.5 ¦ 0.8 ¦ 91.9 ¦ 94.9 ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ TRUE  ¦       67,034 ¦    7,810 ¦   53,273 ¦    84,348 ¦     6.5 ¦ 0.8 ¦  5.1 ¦  8.1 ¦ #> +--------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                   #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse factor levels — var_collapse","title":"Collapse factor levels — var_collapse","text":"Collapse two levels factor variable single level.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse factor levels — var_collapse","text":"","code":"var_collapse(vr, newlevel, oldlevels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse factor levels — var_collapse","text":"vr factor variable newlevel name new level oldlevels vector old levels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse factor levels — var_collapse","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse factor levels — var_collapse","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"PRIMCARE\") #>                  Are you the patient's primary care provider? {NAMCS 2019 PUF}                   #> +----------------------------------------------------------------------------------------------+ #> ¦ Level   ¦      Number ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ Flags ¦ #> ¦         ¦       (000) ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦       ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Blank   ¦       1,150 ¦      478 ¦      440 ¦    3,005 ¦     0.1 ¦ 0   ¦  0   ¦  0.2 ¦ Cx    ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Unknown ¦      39,519 ¦    9,507 ¦   24,520 ¦   63,692 ¦     3.8 ¦ 0.9 ¦  2.3 ¦  6   ¦       ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Yes     ¦     383,481 ¦   28,555 ¦  331,362 ¦  443,798 ¦    37   ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦       ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ No      ¦     612,335 ¦   43,282 ¦  533,050 ¦  703,413 ¦    59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦       ¦ #> +----------------------------------------------------------------------------------------------+ #>   Cx: suppress count (and rate)                                                                  #>  var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Blank\", \"Unknown\")) tab(\"PRIMCARE\") #>                  Are you the patient's primary care provider? {NAMCS 2019 PUF}                  #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Unknown if    ¦       40,669 ¦    9,479 ¦   25,619 ¦   64,560 ¦     3.9 ¦ 0.9 ¦  2.4 ¦  6.1 ¦ #> ¦ PCP           ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Yes           ¦      383,481 ¦   28,555 ¦  331,362 ¦  443,798 ¦    37   ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ No            ¦      612,335 ¦   43,282 ¦  533,050 ¦  703,413 ¦    59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Copy a variable — var_copy","title":"Copy a variable — var_copy","text":"Create new variable copy another variable. can modify copy, original remains unchanged. See examples.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Copy a variable — var_copy","text":"","code":"var_copy(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Copy a variable — var_copy","text":"newvr name new variable created vr variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Copy a variable — var_copy","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Copy a variable — var_copy","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_copy(\"Age group\", \"AGER\") var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>                                   Age group {NAMCS 2019 PUF}                                    #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-64         ¦      479,777 ¦   32,175 ¦  420,624 ¦  547,247 ¦    46.3 ¦ 1.8 ¦ 42.7 ¦ 49.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65+           ¦      373,935 ¦   24,523 ¦  328,777 ¦  425,296 ¦    36.1 ¦ 1.9 ¦ 32.3 ¦ 40   ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross or interact two variables — var_cross","title":"Cross or interact two variables — var_cross","text":"Create new variable interaction two variables. Also see tab_cross().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross or interact two variables — var_cross","text":"","code":"var_cross(newvr, vr, vrby)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross or interact two variables — var_cross","text":"newvr name new variable created vr first variable vrby second variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cross or interact two variables — var_cross","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross or interact two variables — var_cross","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"Age x Sex\") #>                      (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}                      #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       59,958 ¦    7,206 ¦   47,318 ¦   75,974 ¦     5.8 ¦ 0.7 ¦  4.5 ¦  7.3 ¦ #> ¦ years :       ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       41,128 ¦    4,532 ¦   33,066 ¦   51,156 ¦     4   ¦ 0.4 ¦  3.2 ¦  4.9 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦      113,708 ¦   11,461 ¦   93,256 ¦  138,646 ¦    11   ¦ 1   ¦  9   ¦ 13.2 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      175,978 ¦   16,009 ¦  147,153 ¦  210,450 ¦    17   ¦ 1.1 ¦ 14.9 ¦ 19.3 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦      120,099 ¦   11,066 ¦  100,171 ¦  143,992 ¦    11.6 ¦ 1   ¦  9.7 ¦ 13.7 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       94,173 ¦   11,085 ¦   74,682 ¦  118,751 ¦     9.1 ¦ 0.9 ¦  7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       57,959 ¦    7,728 ¦   44,570 ¦   75,371 ¦     5.6 ¦ 0.7 ¦  4.3 ¦  7.2 ¦ #> ¦ years : Male  ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       23,728 ¦    4,344 ¦   16,457 ¦   34,210 ¦     2.3 ¦ 0.4 ¦  1.6 ¦  3.2 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦       56,562 ¦    7,277 ¦   43,861 ¦   72,942 ¦     5.5 ¦ 0.6 ¦  4.3 ¦  6.8 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      133,528 ¦   12,956 ¦  110,319 ¦  161,619 ¦    12.9 ¦ 1   ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦       86,766 ¦    6,767 ¦   74,409 ¦  101,176 ¦     8.4 ¦ 0.6 ¦  7.2 ¦  9.7 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       72,896 ¦    6,661 ¦   60,872 ¦   87,296 ¦     7   ¦ 0.6 ¦  5.9 ¦  8.3 ¦ #> ¦ over : Male   ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert numeric to factor — var_cut","title":"Convert numeric to factor — var_cut","text":"Create new categorical variable based numeric variable.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert numeric to factor — var_cut","text":"","code":"var_cut(newvr, vr, breaks, labels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert numeric to factor — var_cut","text":"newvr name new factor variable created vr numeric variable breaks see cut() labels see cut()","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert numeric to factor — var_cut","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert numeric to factor — var_cut","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_cut(\"Age group\" , \"AGE\" , c(-Inf, 0, 4, 14, 64, Inf) , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\")) tab(\"Age group\") #>                                  Age group {NAMCS 2019 PUF}                                   #> +-------------------------------------------------------------------------------------------+ #> ¦ Level       ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 1     ¦       31,148 ¦    5,282 ¦   22,269 ¦   43,566 ¦     3   ¦ 0.5 ¦  2.1 ¦  4.1 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 1-4         ¦       38,240 ¦    5,444 ¦   28,864 ¦   50,662 ¦     3.7 ¦ 0.5 ¦  2.7 ¦  4.8 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 5-14        ¦       48,529 ¦    5,741 ¦   38,430 ¦   61,282 ¦     4.7 ¦ 0.5 ¦  3.7 ¦  5.9 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-64       ¦      544,632 ¦   36,082 ¦  478,254 ¦  620,223 ¦    52.5 ¦ 2   ¦ 48.6 ¦ 56.5 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65 and over ¦      373,935 ¦   24,523 ¦  328,777 ¦  425,296 ¦    36.1 ¦ 1.9 ¦ 32.3 ¦ 40   ¦ #> +-------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                        #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":null,"dir":"Reference","previous_headings":"","what":"List variables in a survey. — var_list","title":"List variables in a survey. — var_list","text":"List variables survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List variables in a survey. — var_list","text":"","code":"var_list(sw = \"\", all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List variables in a survey. — var_list","text":"sw starting characters variable name (case insensitive) print variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List variables in a survey. — var_list","text":"table","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List variables in a survey. — var_list","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_list(\"age\") #> Variables beginning with 'age' {NAMCS 2019 PUF} #> +-------------------------------------------+ #> ¦ Variable ¦ Class   ¦ Long name            ¦ #> +----------+---------+----------------------¦ #> ¦ AGE      ¦ numeric ¦ Patient age in years ¦ #> +----------+---------+----------------------¦ #> ¦ AGER     ¦ factor  ¦ Patient age recode   ¦ #> +-------------------------------------------+ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical NOT — var_not","title":"Logical NOT — var_not","text":"Logical ","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical NOT — var_not","text":"","code":"var_not(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical NOT — var_not","text":"newvr name new variable created vr logical variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logical NOT — var_not","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical NOT — var_not","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_not(\"Private insurance not used\", \"PAYPRIV\")"},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-development-version","dir":"Changelog","previous_headings":"","what":"surveytable (development version)","title":"surveytable (development version)","text":"codebook() Improved output. Allows unweighted survey data.frame. Can set certain options using argument.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-092","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.2","title":"surveytable 0.9.2","text":"CRAN release: 2024-01-18 Addressed CRAN comments.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-091","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.1","title":"surveytable 0.9.1","text":"Initial CRAN submission.","code":""}]
+[{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"begin","dir":"Articles","previous_headings":"","what":"Begin","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Begin loading surveytable package. , print message explaining specify survey ’d like analyze. omitting message . Now, specify survey ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey name, survey design variables, number observations verify looks correct.","code":"library(surveytable) set_survey(namcs2019sv)"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages","dir":"Articles","previous_headings":"Table 1","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows overall estimated count well counts percentages type doctor, physician specialty, metropolitan statistical area. variables necessary creating table already survey, making commands straightforward. Total {NAMCS 2019 PUF} Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"total() tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates","dir":"Articles","previous_headings":"Table 1","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"published table also shows several rates. calculate rates, addition survey, need source information population estimates. typically use function read.csv() load population estimates get correct format. surveytable package comes object called uspop2019 contains several population estimates use examples. overall population estimate: overall population estimate, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame variable called Level matches levels variable survey, variable called Population gives population size (assumed constant rather random variable). MSA, can see levels variables just using tab() command, just . Thus, calculate rates, need data frame follows: Now appropriate population estimates, rate : Metropolitan Statistical Area Status physician location (rate per 100 population) {NAMCS 2019 PUF} can also calculate rates specific variable based entire population: Type doctor (MD ) (rate per 100 population) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) ## [1] \"list\" names(uspop2019) ## [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         ## [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total ## [1] 323186697 total_rate(uspop2019$total) uspop2019$MSA ##                                 Level Population ## 1 MSA (Metropolitan Statistical Area)  277229518 ## 2                             Non-MSA   45957179 tab_rate(\"MSA\", uspop2019$MSA) tab_rate(\"MDDO\", uspop2019$total) ## * Rate based on the entire population. tab_rate(\"SPECCAT\", uspop2019$total) ## * Rate based on the entire population."},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages-1","dir":"Articles","previous_headings":"Table 3","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table presents estimates age group, well age group sex. Variables beginning ‘age’ {NAMCS 2019 PUF} survey couple relevant age-related variables. AGE patient age years. AGER categorical variable based AGE. However, table, addition AGER, need another age group variable, different age categories. create using var_cut function. Now ’ve created Age group variable, can create tables: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} Patient sex {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}","code":"var_list(\"age\") var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) tab(\"AGER\", \"Age group\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates-1","dir":"Articles","previous_headings":"Table 3","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Patient age recode (rate per 100 population) {NAMCS 2019 PUF} Age group (rate per 100 population) {NAMCS 2019 PUF} Patient sex (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population estimates following format: population estimates, rates : Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"tab_rate(\"AGER\", uspop2019$AGER) tab_rate(\"Age group\", uspop2019$`Age group`) ## * Population for some levels not defined: 15-64 tab_rate(\"SEX\", uspop2019$SEX) uspop2019$`AGER x SEX` ##                Level Subset Population ## 1     Under 15 years Female   29604762 ## 2        15-24 years Female   20730118 ## 3        25-44 years Female   43192143 ## 4        45-64 years Female   42508901 ## 5        65-74 years Female   16673240 ## 6  75 years and over Female   12421444 ## 7     Under 15 years   Male   30921894 ## 8        15-24 years   Male   20988582 ## 9        25-44 years   Male   42407267 ## 10       45-64 years   Male   40053148 ## 11       65-74 years   Male   14586962 ## 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-5","dir":"Articles","previous_headings":"","what":"Table 5","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table gives expected sources payment. use PAY* variables create several new variables required table. Note PAY* variables logical (TRUE FALSE), simplifies workflow. (survey imported R using importsurvey package, automatically detects binary variables imports logical variables.) Expected source payment visit: Private insurance {NAMCS 2019 PUF} Expected source payment visit: Medicare {NAMCS 2019 PUF} Expected source payment visit: Medicaid CHIP state-based program {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} insurance {NAMCS 2019 PUF} Self-pay {NAMCS 2019 PUF} charge {NAMCS 2019 PUF} Expected source payment visit: Workers Compensation {NAMCS 2019 PUF} Expected source payment visit: {NAMCS 2019 PUF} Unknown blank {NAMCS 2019 PUF} Check presentation standards flags! NCHS presentation standards rules, estimates shown.","code":"# var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\"))  # var_any(\"Payment used\", c(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\"   , \"PAYWKCMP\", \"PAYOTH\", \"PAYDK\")) var_not(\"No other payment used\", \"Payment used\")  var_all(\"Self-pay\", c(\"PAYSELF\", \"No other payment used\")) var_all(\"No charge\", c(\"PAYNOCHG\", \"No other payment used\")) var_any(\"No insurance\", c(\"Self-pay\", \"No charge\"))  # var_case(\"No pay\", \"NOPAY\", \"No categories marked\") var_any(\"Unknown or blank\", c(\"PAYDK\", \"No pay\"))  ## tab(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\", \"Medicare and Medicaid\"   , \"No insurance\", \"Self-pay\", \"No charge\"   , \"PAYWKCMP\", \"PAYOTH\", \"Unknown or blank\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-6","dir":"Articles","previous_headings":"","what":"Table 6","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows primary care provider referral status, prior-visit status. table, “Unknown” “Blank” values collapsed single value. can collapse two levels factor single level using var_collapse function. Now, table: patient’s primary care provider? {NAMCS 2019 PUF} patient referred visit? {NAMCS 2019 PUF} patient seen practice ? {NAMCS 2019 PUF} percentages within subset defined SENBEFOR add 100% – reason, want use tab_subset(), tab_cross(). patient’s primary care provider? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient’s primary care provider? (patient seen practice ? = , new patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = , new patient) {NAMCS 2019 PUF}","code":"var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) var_collapse(\"REFER\", \"Unknown if referred\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\", \"REFER\", \"SENBEFOR\") tab_subset(\"PRIMCARE\", \"SENBEFOR\") tab_subset(\"REFER\", \"SENBEFOR\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-11","dir":"Articles","previous_headings":"","what":"Table 11","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows information Table 3, preventive care visits. , estimates age group, well age group sex, preventive care visits. Let’s create Age group AGE cross AGER SEX create variable called Age x Sex: see possible values MAJOR (Major reason visit), estimate total count preventive care visits: Major reason visit {NAMCS 2019 PUF} create tables age, sex, interaction, limit preventive care visits: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) (Major reason visit = Preventive care) {NAMCS 2019 PUF} commands similar, differs first variable passed tab_subset() function, code can streamlined loop: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) (Major reason visit = Preventive care) {NAMCS 2019 PUF} Note called inside loop, print() function needs called explicitly.","code":"var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) ## Warning in var_cut(\"Age group\", \"AGE\", c(-Inf, 0, 4, 14, 64, Inf), c(\"Under 1\", ## : Age group: overwriting a variable that already exists. var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"MAJOR\") tab_subset(\"AGER\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age group\", \"MAJOR\", \"Preventive care\") tab_subset(\"SEX\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age x Sex\", \"MAJOR\", \"Preventive care\") for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     print( tab_subset(vr, \"MAJOR\", \"Preventive care\") ) }"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"more-advanced-coding","dir":"Articles","previous_headings":"Table 11","what":"More advanced coding","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"addition, age-sex category, published table shows percentage preventive care visits made primary care physicians. calculate percentages, slightly involved loop needed. code, followed explanation: Since tab_subset() called within loop, wanted print screen, need use print( tab_subset(*) ). Since don’t want print screen, call print() omitted. Since many tables produced, output sent CSV file. , loop goes age, sex, age / sex interaction variables, calling variables vr. MAJOR vr crossed, result stored variable called tmp. Next, inner loop goes levels vr, calling levels lvl. code tabulates SPECCAT (Type specialty – Primary, Medical, Surgical) subset tmp (MAJOR crossed vr) restricted “Preventive care:” followed lvl, level vr, “15 years” AGER. Finally, CSV output turned . run code, tables stored CSV file. give idea tables look like, just one tables: Type specialty (Primary, Medical, Surgical) ((Major reason visit) x (Patient age recode) = Preventive care : 15 years) {NAMCS 2019 PUF} match percentage published table, see “Primary care specialty” row. sure check presentation standards flags.","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) )  for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     var_cross(\"tmp\", \"MAJOR\", vr)     for (lvl in levels(surveytable:::env$survey$variables[,vr])) {         tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care : \", lvl))     } } ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists.  ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists.  ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. set_output(csv = \"\") ## * Turning off CSV output. ## * ?set_output for other options. vr = \"AGER\" var_cross(\"tmp\", \"MAJOR\", vr) ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. lvl = levels(surveytable:::env$survey$variables[,vr])[1] tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care : \", lvl))"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"concepts","dir":"Articles","previous_headings":"Preliminaries","what":"Concepts","title":"Introduction to surveytable","text":"two important concepts need learn distinguish: data frame standard way storing data R. data frame rectangular data. Variables columns, observations rows. Example: data frame, , represent complex survey. , just looking data frame, R know sampling weights , strata , etc. Even variables represent sampling weights, etc, part data frame, just looking data frame, R know variable represents weights survey design variables. can get data frame R many different ways. data currently comma-separated values (CSV) file, can use read.csv(). ’s SAS file, can use package like haven importsurvey. ’s already R format, use readRDS(), . survey object object describes survey. tells R sampling weights , strata , . data frame can converted survey object using survey::svydesign() function; survey uses replicate weights, survey::svrepdesign() function used. Generally speaking, need convert data frame survey object . converted, can save saveRDS() (similar). future, can load readRDS(). need re-convert data frame survey object every time.","code":"head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"namcs","dir":"Articles","previous_headings":"Preliminaries","what":"NAMCS","title":"Introduction to surveytable","text":"Examples tutorial use survey called National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF). NAMCS “annual nationally representative sample survey visits non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, pathologists.” Note unit observation visits, patients – distinction important since single patient can make multiple visits. surveytable package comes data frame selected variables NAMCS, called namcs2019sv_df (sv = selected variables; df = data frame). survey object survey called namcs2019sv. namcs2019sv object analyze. really need namcs2019sv. reason package namcs2019sv_df illustrate convert data frame survey object.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"more-concepts","dir":"Articles","previous_headings":"Preliminaries","what":"More concepts","title":"Introduction to surveytable","text":"importing data another source, SAS CSV, analysts aware standard way variables handled R. Specifically, categorical variables stored factor. true / false variables stored factor well, programming tasks easier stored logical. Unknown values stored missing (NA). variable contains “special values”, negative value indicating age missing, “special values” need converted NA. Variables namcs2019sv_df already stored correctly. Thus, AGER (patient’s age group) factor variable; PAYNOCHG (indicates whether charge physician visit) logical variable; AGE (patient’s age years) numeric variable.","code":"library(\"surveytable\") class(namcs2019sv_df$AGER) #> [1] \"factor\" class(namcs2019sv_df$PAYNOCHG) #> [1] \"logical\" class(namcs2019sv_df$AGE) #> [1] \"numeric\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-a-survey-object","dir":"Articles","previous_headings":"Preliminaries","what":"Create a survey object","title":"Introduction to surveytable","text":"seen , tables produced surveytable clearer either variable names descriptive, variables \"label\" attribute descriptive. namcs2019sv_df, variables already \"label\" attribute set. example, variable name AGE descriptive, variable descriptive \"label\" attribute: Documentation NAMCS survey provides names survey design variables. Specifically, NAMCS, cluster ID’s, also known primary sampling units (PSU’s), given CPSUM; strata given CSTRATM; sampling weights given PATWT. Thus, namcs2019sv_df data frame can turned survey object follows: Tables produced surveytable clearer either name survey object descriptive, object \"label\" attribute descriptive. Let’s set attribute mysurvey: mysurvey object now identical namcs2019sv. Let’s verify : just successfully created survey object data frame.","code":"attr(namcs2019sv_df$AGE, \"label\") #> [1] \"Patient age in years\" mysurvey = survey::svydesign(ids = ~ CPSUM   , strata = ~ CSTRATM   , weights = ~ PATWT   , data = namcs2019sv_df) attr(mysurvey, \"label\") = \"NAMCS 2019 PUF\" identical(namcs2019sv, mysurvey) #> [1] TRUE"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"begin-analysis","dir":"Articles","previous_headings":"","what":"Begin analysis","title":"Introduction to surveytable","text":"First, specify survey object ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey label, survey design variables, number observations verify looks correct.","code":"set_survey(namcs2019sv)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"list-variables","dir":"Articles","previous_headings":"Begin analysis","what":"List variables","title":"Introduction to surveytable","text":"var_list() function lists variables survey. avoid unintentionally listing variables survey, can many, starting characters variable names specified. example, list variables start letters age, type: Variables beginning ‘age’ {NAMCS 2019 PUF} table lists variable name; class, type variable; variable label, long name variable. Common classes factor (categorical variable), logical (yes / variable), numeric.","code":"var_list(\"age\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-categorical-and-logical-variables","dir":"Articles","previous_headings":"","what":"Tabulate categorical and logical variables","title":"Introduction to surveytable","text":"main function surveytable package tab(), tabulates variables. operates categorical logical variables, presents estimated counts, standard errors (SEs) 95% confidence intervals (CIs), percentages, SEs CIs. example, tabulate AGER, type: Patient age recode {NAMCS 2019 PUF} table title shows variable label (long variable name) survey label. level variable, table shows: estimated count, standard error, 95% confidence interval; estimated percentage, standard error, 95% confidence interval. NCHS presentation standards. tab() function also applies National Center Health Statistics (NCHS) presentation standards counts percentages, flags estimates , according standards, suppressed, footnoted, reviewed analyst. CIs displayed ones used NCHS presentation standards. Specifically, counts, tables show log Student’s t 95% CI, adaptations complex surveys; percentages, show 95% Korn Graubard CI. One need anything extra perform presentation standards checking – performed automatically. example, let’s tabulate PAYNOCHG: Expected source payment visit: Charge/Charity {NAMCS 2019 PUF} table tells us , according NCHS presentation standards, estimated number visits charge visit suppressed due low precision. However, lack percentage flag indicates estimated percentage visits can shown. Drop missing values. variables might contain missing values (NA). Consider following variable, part actual survey, constructed specifically example: Type specialty (BAD - use) {NAMCS 2019 PUF} calculate percentages based non-missing values , use drop_na argument: Type specialty (BAD - use) (knowns ) {NAMCS 2019 PUF} table gives percentages based knowns, , based non-NA values. Multiple tables. Multiple tables can created single command: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"tab(\"AGER\") tab(\"PAYNOCHG\") tab(\"SPECCAT.bad\") tab(\"SPECCAT.bad\", drop_na = TRUE) tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"entire-population","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Entire population","title":"Introduction to surveytable","text":"Estimate total count entire population using total() command: Total {NAMCS 2019 PUF}","code":"total()"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"subsets-or-interactions","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Subsets or interactions","title":"Introduction to surveytable","text":"create table AGER value variable SEX, type: Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} addition giving long name variable tabulated, title table reflects value subsetting variable (case, either Female Male). tab_subset() command, table (, subset), percentages add 100%. tab_cross() function similar – crosses interacts two variables generates table using new variable. Thus, create table interaction AGER SEX, type: (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} estimated counts produced tab_subset() tab_cross() , percentages different. tab_subset() command, within table (, within subset), percentages add 100%. hand, tab_cross(), percentages across entire population add 100%.","code":"tab_subset(\"AGER\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-numeric-variables","dir":"Articles","previous_headings":"","what":"Tabulate numeric variables","title":"Introduction to surveytable","text":"tab() tab_subset() functions also work numeric variables, though variables, output different. tabulate NUMMED (number medications), numeric variable, type: Number medications coded {NAMCS 2019 PUF} , table title shows variable label (long variable name) survey label. table shows percentage values missing (NA), mean, standard error mean (SEM), standard deviation (SD). Subsetting works : Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF}","code":"tab(\"NUMMED\") tab_subset(\"NUMMED\", \"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"perform-statistical-hypothesis-testing","dir":"Articles","previous_headings":"","what":"Perform statistical hypothesis testing","title":"Introduction to surveytable","text":"tab_subset() function makes easy perform hypothesis testing using test argument. argument TRUE, test association performed. addition, t-tests pairs levels performed well.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables","title":"Introduction to surveytable","text":"Consider relationship AGER SPECCAT: Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association Patient age recode Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15-24 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 25-44 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 45-64 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 65-74 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 75 years ) {NAMCS 2019 PUF} According tables, association physician specialty type patient age. instance, patients 15 years, statistical difference primary care physician specialty medical care specialty. older patients, 45-64 age group, statistical difference two specialty types. another example, consider relationship MRI SPECCAT: MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association MRI Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = FALSE) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = TRUE) {NAMCS 2019 PUF} According tables, statistical association MRI physician specialty. 3 specialty types, minority visits MRI’s. visits MRI’s, statistical difference specialty types. general rule thumb, since statistical association MRI physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate MRI without subsetting SPECCAT.","code":"tab_subset(\"AGER\", \"SPECCAT\", test = TRUE) tab_subset(\"MRI\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"numeric-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Numeric variables","title":"Introduction to surveytable","text":"relationship NUMMED AGER: Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF} Association Number medications coded Patient age recode {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Patient age recode {NAMCS 2019 PUF} According tables, association number medications age category. NUMMED statistically similar “15 years” “15-24 years” AGER categories. statistically different pairs age categories. Finally, let’s look relationship NUMMED SPECCAT: Number medications coded (different levels Type specialty (Primary, Medical, Surgical)) {NAMCS 2019 PUF} Association Number medications coded Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According tables, association number medications physician specialty type. NUMMED statistically similar pairs physician specialties. general rule thumb, since statistical association number medications physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate NUMMED without subsetting SPECCAT.","code":"tab_subset(\"NUMMED\", \"AGER\", test = TRUE) tab_subset(\"NUMMED\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables-single-variable","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables (single variable)","title":"Introduction to surveytable","text":"test whether pair SPECCAT levels statistically similar different, type: Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According , surgical medical care specialties statistically similar, statistically different primary care.","code":"tab(\"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"calculate-rates","dir":"Articles","previous_headings":"","what":"Calculate rates","title":"Introduction to surveytable","text":"rate ratio count estimates based survey question divided population size, assumed known. example, number physician visits per 100 people population rate: number physician visits estimated namcs2019sv survey, number people population comes another source. calculate rates, addition survey, need source information population size. typically use function read.csv() load population figures get correct format. surveytable package comes object called uspop2019 contains several population figures use examples. Let’s examine uspop2019: overall population size country whole : overall population size, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame column called Level matches levels variable survey, column called Population gives size population level. example, AGER, data frame follows: Now appropriate population figures, rates table obtained typing: Patient age recode (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population figures following format: data frame, rates table obtained typing: Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) #> [1] \"list\" names(uspop2019) #> [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         #> [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total #> [1] 323186697 total_rate(uspop2019$total) uspop2019$AGER #>               Level Population #> 1    Under 15 years   60526656 #> 2       15-24 years   41718700 #> 3       25-44 years   85599410 #> 4       45-64 years   82562049 #> 5       65-74 years   31260202 #> 6 75 years and over   21519680 tab_rate(\"AGER\", uspop2019$AGER) uspop2019$`AGER x SEX` #>                Level Subset Population #> 1     Under 15 years Female   29604762 #> 2        15-24 years Female   20730118 #> 3        25-44 years Female   43192143 #> 4        45-64 years Female   42508901 #> 5        65-74 years Female   16673240 #> 6  75 years and over Female   12421444 #> 7     Under 15 years   Male   30921894 #> 8        15-24 years   Male   20988582 #> 9        25-44 years   Male   42407267 #> 10       45-64 years   Male   40053148 #> 11       65-74 years   Male   14586962 #> 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-or-modify-variables","dir":"Articles","previous_headings":"","what":"Create or modify variables","title":"Introduction to surveytable","text":"situations, might necessary modify survey variables, create new ones. section describes . Convert factor logical. variable MAJOR (major reason visit) several levels. Major reason visit {NAMCS 2019 PUF} Notice one levels called \"Preventive care\". Suppose analyst interested whether visit preventive care visit – interested visit types. can create new variable called Preventive care visits TRUE preventive care visits FALSE types visits, follows: Preventive care visits {NAMCS 2019 PUF} creates logical variable TRUE preventive care visits tabulates . using var_case() function, specify name new logical variable created, existing factor variable, one levels factor variable set TRUE logical variable. Thus, analyst interested surgery-related visits, indicated two different levels MAJOR, type: Surgery-related visits {NAMCS 2019 PUF} Collapse levels. variable PRIMCARE (whether physician patient’s primary care provider) levels Unknown Blank, among others. patient’s primary care provider? {NAMCS 2019 PUF} collapse Unknown Blank single level, type: patient’s primary care provider? {NAMCS 2019 PUF} Convert numeric factor. variable AGE numeric. Patient age years {NAMCS 2019 PUF} create new variable age categories based AGE, type: Age group {NAMCS 2019 PUF} var_cut() command, specify following information: name new categorical variable; name existing numeric variable; cut points – note intervals inclusive right; category labels. Check whether variable true. series logical variables, can check whether TRUE using var_any() command. physician visit considered “imaging services” visit number imaging services ordered provided. Imaging services indicated using logical variables, MRI XRAY. create Imaging services variable, type: Imaging services {NAMCS 2019 PUF} Interact variables. tab_cross() function creates table interaction two variables, save interacted variable. create interacted variable, use var_cross() command: Specify name new variable well names two variables interact. Copy variable. Create new variable copy another variable using var_copy(). can modify copy, original remains unchanged. example: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} , AGER variable remains unchanged, Age group variable fewer categories. variables data frame. Recall survey objects element called variables, data frame contains survey variables. advanced users can create modify variables variables data frame directly. modify variables, must call set_survey() . example: Survey info {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} Note, however, var_*() functions modify survey object specified set_survey() directly. Rather, modify variables inside following data frame: surveytable:::env$survey$variables. use var_*() functions need access modified / created variables, ’s look. example:","code":"tab(\"MAJOR\") var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") var_case(\"Surgery-related visits\"   , \"MAJOR\"   , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") tab(\"PRIMCARE\") var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\") tab(\"AGE\") var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) tab(\"Age group\") var_any(\"Imaging services\"   , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\"   , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") var_cross(\"Age x Sex\", \"AGER\", \"SEX\") var_copy(\"Age group\", \"AGER\") #> Warning in var_copy(\"Age group\", \"AGER\"): Age group: overwriting a variable #> that already exists. var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\") class(namcs2019sv$variables) #> [1] \"data.frame\" namcs2019sv$variables$`Medicare and Medicaid` = (    namcs2019sv$variables$PAYMCARE & namcs2019sv$variables$PAYMCAID) set_survey(namcs2019sv) tab(\"Medicare and Medicaid\") var_cross(\"newvar\", \"MAJOR\", \"AGER\") # This should give NULL. The new variable does not exist here: namcs2019sv$variables$newvar #> NULL # Rather, the new variable is here: str(surveytable:::env$survey$variables$newvar) #>  Factor w/ 42 levels \"Blank : Under 15 years\",..: 17 31 24 38 38 24 30 31 24 38 ... #>  - attr(*, \"label\")= chr \"(Major reason for this visit) x (Patient age recode)\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"save-the-output","dir":"Articles","previous_headings":"","what":"Save the output","title":"Introduction to surveytable","text":"tab* total* functions argument called csv specifies name comma-separated values (CSV) file save output . Alternatively, can name default CSV output file using set_output() function. example, following directs surveytable send future output CSV file, create tables, turn sending output file: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF} tabulation functions called within R Markdown notebook, produce HTML tables. makes easy incorporate output surveytable package directly documents, presentations, “shiny” web apps, output types. Finally, tabulation functions return tables produce. advanced analysts can use functionality integrate surveytable programming tasks.","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) tab(\"MDDO\", \"SPECCAT\", \"MSA\") set_output(csv = \"\") #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Alex Strashny. Author, maintainer.","code":""},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Strashny (2023). surveytable: Formatted Survey Estimates. https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable.","code":"@Manual{,   title = {surveytable: Formatted Survey Estimates},   author = {Alex Strashny},   year = {2023},   note = {https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable}, }"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"survey-table-formatted-survey-estimates","dir":"","previous_headings":"","what":"Formatted Survey Estimates","title":"Formatted Survey Estimates","text":"surveytable package provides short understandable commands generate tabulated, formatted, rounded survey estimates. One useful function, operates categorical logical variables, tabulates estimated counts percentages standard errors confidence intervals. functions list variables survey, estimate total population, tabulate survey subsets variable interactions, tabulate numeric variables, tabulate rates, create modify survey variables, perform hypothesis tests, save output. tabulation functions check National Center Health Statistics (NCHS) presentation standards flag low-precision estimates. surveytable code called R Markdown notebook Quarto document, generates HTML tables, can incorporated directly documents.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Formatted Survey Estimates","text":"Install CRAN: get development version GitHub:","code":"install.packages(\"surveytable\") install.packages(c(\"remotes\", \"git2r\")) remotes::install_github(\"CDCgov/surveytable\", upgrade = \"never\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Formatted Survey Estimates","text":"Find documentation surveytable : https://cdcgov.github.io/surveytable/","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Formatted Survey Estimates","text":"basic example, get started. Survey info {NAMCS 2019 PUF} Patient age recode {NAMCS 2019 PUF}","code":"library(surveytable) set_survey(namcs2019sv) tab(\"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"public-domain-standard-notice","dir":"","previous_headings":"","what":"Public Domain Standard Notice","title":"Formatted Survey Estimates","text":"repository constitutes work United States Government subject domestic copyright protection 17 USC § 105. repository public domain within United States, copyright related rights work worldwide waived CC0 1.0 Universal public domain dedication. contributions repository released CC0 dedication. submitting pull request agreeing comply waiver copyright interest.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"license-standard-notice","dir":"","previous_headings":"","what":"License Standard Notice","title":"Formatted Survey Estimates","text":"repository utilizes code licensed terms Apache Software License therefore licensed ASL v2 later. source code repository free: can redistribute /modify terms Apache Software License version 2, (option) later version. source code repository distributed hope useful, WITHOUT WARRANTY; without even implied warranty MERCHANTABILITY FITNESS PARTICULAR PURPOSE. See Apache Software License details. received copy Apache Software License along program. , see https://www.apache.org/licenses/LICENSE-2.0.html source code forked open source projects inherit license.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"privacy-standard-notice","dir":"","previous_headings":"","what":"Privacy Standard Notice","title":"Formatted Survey Estimates","text":"repository contains non-sensitive, publicly available data information. material community participation covered Disclaimer Code Conduct. information CDC’s privacy policy, please visit https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"contributing-standard-notice","dir":"","previous_headings":"","what":"Contributing Standard Notice","title":"Formatted Survey Estimates","text":"Anyone encouraged contribute repository forking submitting pull request. (new GitHub, might start basic tutorial.) contributing project, grant world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license users terms Apache Software License v2 later. comments, messages, pull requests, submissions received CDC including GitHub page may subject applicable federal law, including limited Federal Records Act, may archived. Learn https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"records-management-standard-notice","dir":"","previous_headings":"","what":"Records Management Standard Notice","title":"Formatted Survey Estimates","text":"repository source government records, copy increase collaboration collaborative potential. government records published CDC web site.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"additional-standard-notices","dir":"","previous_headings":"","what":"Additional Standard Notices","title":"Formatted Survey Estimates","text":"Please refer CDC’s Template Repository information contributing repository, public domain notices disclaimers, code conduct.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. 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Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner]  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a codebook for the survey — codebook","title":"Create a codebook for the survey — codebook","text":"Create codebook survey","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a codebook for the survey — codebook","text":"","code":"codebook(all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a codebook for the survey — codebook","text":"tabulate variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a codebook for the survey — codebook","text":"list tables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a codebook for the survey — codebook","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  codebook() #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  #>                                     Codebook {NAMCS 2019 PUF}                                      #> +------------------------------------------------------------------------------------------------+ #> ¦ Item no. ¦ Variable    ¦ Description           ¦ Class   ¦ Missing (%) ¦ Values                ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        1 ¦ CPSUM       ¦ Masked provider       ¦ numeric ¦           0 ¦ 100001 - 100398       ¦ #> ¦          ¦             ¦ marker                ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        2 ¦ CSTRATM     ¦ Masked sampling       ¦ numeric ¦           0 ¦ 10119101 - 10419115   ¦ #> ¦          ¦             ¦ stratum from which    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ provider was selected ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        3 ¦ PATWT       ¦ Patient visit weight  ¦ numeric ¦           0 ¦ 7064.00718 -          ¦ #> ¦          ¦             ¦ used for national and ¦         ¦             ¦ 1120996.55599         ¦ #> ¦          ¦             ¦ subnational estimates ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        4 ¦ MDDO        ¦ Type of doctor (MD or ¦ factor  ¦           0 ¦ M.D. - Doctor of      ¦ #> ¦          ¦             ¦ DO)                   ¦         ¦             ¦ Medicine, D.O. -      ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Doctor of Osteopathy  ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        5 ¦ SPECCAT     ¦ Type of specialty     ¦ factor  ¦           0 ¦ Primary care          ¦ #> ¦          ¦             ¦ (Primary, Medical,    ¦         ¦             ¦ specialty, Surgical   ¦ #> ¦          ¦             ¦ Surgical)             ¦         ¦             ¦ care specialty,       ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Medical care          ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ specialty             ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        6 ¦ MSA         ¦ Metropolitan          ¦ factor  ¦           0 ¦ MSA (Metropolitan     ¦ #> ¦          ¦             ¦ Statistical Area      ¦         ¦             ¦ Statistical Area),    ¦ #> ¦          ¦             ¦ Status of physician   ¦         ¦             ¦ Non-MSA               ¦ #> ¦          ¦             ¦ location              ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        7 ¦ AGER        ¦ Patient age recode    ¦ factor  ¦           0 ¦ Under 15 years, 15-24 ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ years, 25-44 years,   ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ 45-64 years, 65-74    ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ years, 75 years and   ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ over                  ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        8 ¦ SEX         ¦ Patient sex           ¦ factor  ¦           0 ¦ Female, Male          ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦        9 ¦ AGE         ¦ Patient age in years  ¦ numeric ¦           0 ¦ 0 - 94                ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       10 ¦ NOPAY       ¦ Expected source of    ¦ factor  ¦           0 ¦ One or more           ¦ #> ¦          ¦             ¦ payment for visit: No ¦         ¦             ¦ categories marked, No ¦ #> ¦          ¦             ¦ answer to item        ¦         ¦             ¦ categories marked     ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       11 ¦ PAYPRIV     ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Private insurance     ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       12 ¦ PAYMCARE    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Medicare              ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       13 ¦ PAYMCAID    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Medicaid or CHIP or   ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ other state-based     ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ program               ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       14 ¦ PAYWKCMP    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Workers Compensation  ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       15 ¦ PAYOTH      ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Other                 ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       16 ¦ PAYDK       ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Unknown               ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       17 ¦ PAYSELF     ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit:    ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Self-pay              ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       18 ¦ PAYNOCHG    ¦ Expected source of    ¦ logical ¦           0 ¦                       ¦ #> ¦          ¦             ¦ payment for visit: No ¦         ¦             ¦                       ¦ #> ¦          ¦             ¦ Charge/Charity        ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       19 ¦ PRIMCARE    ¦ Are you the patient's ¦ factor  ¦           0 ¦ Blank, Unknown, Yes,  ¦ #> ¦          ¦             ¦ primary care          ¦         ¦             ¦ No                    ¦ #> ¦          ¦             ¦ provider?             ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       20 ¦ REFER       ¦ Was patient referred  ¦ factor  ¦           0 ¦ Blank, Unknown, Not   ¦ #> ¦          ¦             ¦ for visit?            ¦         ¦             ¦ applicable, Yes, No   ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       21 ¦ SENBEFOR    ¦ Has this patient been ¦ factor  ¦           0 ¦ Yes, established      ¦ #> ¦          ¦             ¦ seen in your practice ¦         ¦             ¦ patient, No, new      ¦ #> ¦          ¦             ¦ before?               ¦         ¦             ¦ patient               ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       22 ¦ MAJOR       ¦ Major reason for this ¦ factor  ¦           0 ¦ Blank, New problem    ¦ #> ¦          ¦             ¦ visit                 ¦         ¦             ¦ (less than 3 mos.     ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ onset), Chronic       ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ problem, routine,     ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Chronic problem,      ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ flare-up,             ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Pre-surgery,          ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Post-surgery,         ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Preventive care       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       23 ¦ NUMMED      ¦ Number of medications ¦ numeric ¦           0 ¦ 0 - 30                ¦ #> ¦          ¦             ¦ coded                 ¦         ¦             ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       24 ¦ ANYIMAGE    ¦ Any imaging           ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       25 ¦ BONEDENS    ¦ Bone mineral density  ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       26 ¦ CATSCAN     ¦ CT Scan               ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       27 ¦ ECHOCARD    ¦ Echocardiogram        ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       28 ¦ OTHULTRA    ¦ Ultrasound            ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       29 ¦ MAMMO       ¦ Mammography           ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       30 ¦ MRI         ¦ MRI                   ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       31 ¦ XRAY        ¦ X-ray                 ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       32 ¦ OTHIMAGE    ¦ Other imaging         ¦ logical ¦           0 ¦                       ¦ #> +----------+-------------+-----------------------+---------+-------------+-----------------------¦ #> ¦       33 ¦ SPECCAT.bad ¦ Type of specialty     ¦ factor  ¦          20 ¦ Primary care          ¦ #> ¦          ¦             ¦ (BAD - do not use)    ¦         ¦             ¦ specialty, Surgical   ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ care specialty,       ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ Medical care          ¦ #> ¦          ¦             ¦                       ¦         ¦             ¦ specialty             ¦ #> +------------------------------------------------------------------------------------------------+ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":null,"dir":"Reference","previous_headings":"","what":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"Selected variables data system visits office-based physicians. Note unit observation visits, patients - distinction important since single patient can make multiple visits.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"","code":"namcs2019sv  namcs2019sv_df"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"object class survey.design2 (inherits survey.design) 8250 rows 33 columns. object class data.frame 8250 rows 33 columns.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/namcs2019_sas.zip Survey design variables: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/readme2019-sas.txt SAS formats: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/nam19for.txt Documentation: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/doc2019-508.pdf National Summary Tables: https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"namcs2019sv_df data frame. namcs2019sv survey object created namcs2019sv_df using [survey::svydesign()].","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Print surveytable tables — print.surveytable_table","title":"Print surveytable tables — print.surveytable_table","text":"Print surveytable tables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print surveytable tables — print.surveytable_table","text":"","code":"# S3 method for surveytable_table print(x, ...)  # S3 method for surveytable_list print(x, ...)"},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print surveytable tables — print.surveytable_table","text":"x object class surveytable_table surveytable_list. ... ignored","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print surveytable tables — print.surveytable_table","text":"x invisibly.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print surveytable tables — print.surveytable_table","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  table1 = tab(\"AGER\") print(table1) #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  table_many = tab(\"MDDO\", \"SPECCAT\", \"MSA\") print(table_many) #>                            Type of doctor (MD or DO) {NAMCS 2019 PUF}                            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦      980,280 ¦   48,388 ¦  889,842 ¦ 1,079,910 ¦    94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦       56,204 ¦    6,602 ¦   44,597 ¦    70,832 ¦     5.4 ¦ 0.7 ¦  4.2 ¦  6.9 ¦ #> ¦ of Osteopathy ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>  #>                 Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                 #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care  ¦      521,466 ¦   31,136 ¦  463,840 ¦  586,252 ¦    50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦      214,832 ¦   31,110 ¦  161,661 ¦  285,490 ¦    20.7 ¦ 3   ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care  ¦      300,186 ¦   43,497 ¦  225,806 ¦  399,067 ¦    29   ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>            Metropolitan Statistical Area Status of physician location {NAMCS 2019 #> PUF}            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA           ¦      973,676 ¦   50,515 ¦  879,490 ¦ 1,077,947 ¦    93.9 ¦ 1.7 ¦ 89.7 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Statistical   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Area)         ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA       ¦       62,809 ¦   17,549 ¦   36,249 ¦   108,830 ¦     6.1 ¦ 1.7 ¦  3.2 ¦ 10.3 ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>"},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":null,"dir":"Reference","previous_headings":"","what":"Rounding counts — set_count_1k","title":"Rounding counts — set_count_1k","text":"Determines counts rounded.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rounding counts — set_count_1k","text":"","code":"set_count_1k()  set_count_int()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rounding counts — set_count_1k","text":"(Nothing.)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rounding counts — set_count_1k","text":"set_count_1k(): round counts nearest 1,000. set_count_int(): round counts nearest integer.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rounding counts — set_count_1k","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  set_count_int() #> * Rounding counts to the nearest integer. #> * ?set_count_int for other options. total() #>                    Total {NAMCS 2019 PUF}                    #> +----------------------------------------------------------+ #> ¦        Number ¦         SE ¦          LL ¦            UL ¦ #> +---------------+------------+-------------+---------------¦ #> ¦ 1,036,484,356 ¦ 48,836,217 ¦ 945,013,590 ¦ 1,136,808,860 ¦ #> +----------------------------------------------------------+ #>   (Checked presentation standards. Nothing to                #>   report.)                                                   #>   set_count_1k() #> * Rounding counts to the nearest 1,000. #> * ?set_count_1k for other options. total() #>                      Total {NAMCS 2019 PUF}                       #> +---------------------------------------------------------------+ #> ¦  Number (000) ¦      SE (000) ¦      LL (000) ¦      UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦     1,036,484 ¦        48,836 ¦       945,014 ¦     1,136,809 ¦ #> +---------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Set output defaults — set_output","title":"Set output defaults — set_output","text":"show_output() shows current defaults.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set output defaults — set_output","text":"","code":"set_output(drop_na = NULL, max_levels = NULL, csv = NULL)  show_output()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set output defaults — set_output","text":"drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file \"\" turn CSV output","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set output defaults — set_output","text":"(Nothing.)","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set output defaults — set_output","text":"","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) tab(\"AGER\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  set_output(csv = \"\") # Turn off CSV output #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify the survey to analyze — set_survey","title":"Specify the survey to analyze — set_survey","text":"need specify survey functions, tab(), work.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(design, opts = \"NCHS\", csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify the survey to analyze — set_survey","text":"design either survey object (survey.design svyrep.design) data.frame unweighted survey. opts set certain options. See . csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify the survey to analyze — set_survey","text":"Info survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify the survey to analyze — set_survey","text":"opts: \"nchs\": Round counts nearest 1,000 -- see set_count_1k(). Identify low-precision estimates (surveytable.check_present option). Percentage CI's: adjust Korn-Graubard CI's number degrees freedom, matching SUDAAN calculation (surveytable.adjust_svyciprop option). \"general\": Round counts nearest integer -- see set_count_int(). look low-precision estimates (surveytable.check_present option). Percentage CI's: use standard Korn-Graubard CI's. Optionally, survey can attribute called label, long name survey. Optionally, variable survey can attribute called label, variable's long name.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Show package options — show_options","title":"Show package options — show_options","text":"Show package options","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show package options — show_options","text":"","code":"show_options(sw = \"surveytable\")"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show package options — show_options","text":"sw starting characters","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show package options — show_options","text":"List options values.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show package options — show_options","text":"","code":"show_options() #> $surveytable.adjust_svyciprop #> [1] TRUE #>  #> $surveytable.adjust_svyciprop.df_method #> [1] \"NHIS\" #>  #> $surveytable.check_present #> [1] TRUE #>  #> $surveytable.csv #> [1] \"\" #>  #> $surveytable.drop_na #> [1] FALSE #>  #> $surveytable.max_levels #> [1] 20 #>  #> $surveytable.names_count #> [1] \"Number (000)\" \"SE (000)\"     \"LL (000)\"     \"UL (000)\"     #>  #> $surveytable.names_prct #> [1] \"Percent\" \"SE\"      \"LL\"      \"UL\"      #>  #> $surveytable.present_count #> [1] \".present_count\" #>  #> $surveytable.present_prop #> [1] \".present_prop\" #>  #> $surveytable.present_restricted #> [1] \".present_restricted\" #>  #> $surveytable.rate_per #> [1] 100 #>  #> $surveytable.survey_label #> [1] \"NAMCS 2019 PUF\" #>  #> $surveytable.svychisq_statistic #> [1] \"F\" #>  #> $surveytable.tx_count #> [1] \".tx_count_1k\" #>  #> $surveytable.tx_prct #> [1] \".tx_prct\" #>  #> $surveytable.tx_rate #> [1] \".tx_rate\" #>"},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — surveytable-options","title":"Package options — surveytable-options","text":"Run show_options() see available options.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":null,"dir":"Reference","previous_headings":"","what":"surveytable: Formatted Survey Estimates — surveytable-package","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Short understandable commands generate tabulated, formatted, rounded survey estimates. Mostly wrapper 'survey' package (Lumley (2004) doi: 10.18637/jss.v009.i08  https://CRAN.R-project.org/package=survey) identifies low-precision estimates using National Center Health Statistics (NCHS) presentation standards (Parker et al. (2017) https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf, Parker et al. (2023) doi: 10.15620/cdc:124368 ).","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Maintainer: Alex Strashny AStrashny@cdc.gov (ORCID)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a survey, while preserving variable labels — survey_subset","title":"Subset a survey, while preserving variable labels — survey_subset","text":"Subset survey, preserving variable labels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"survey_subset(design, subset, label)"},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a survey, while preserving variable labels — survey_subset","text":"design survey object subset expression specifying sub-population label survey label newly created survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a survey, while preserving variable labels — survey_subset","text":"new survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"children = survey_subset(namcs2019sv, AGE < 18, \"Children < 18\") set_survey(children) #>                          Survey info {Children < 18}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        1,066 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (139) clusters.                           ¦ #> ¦           ¦              ¦ survey_subset(namcs2019sv, AGE < 18, \"Children ¦ #> ¦           ¦              ¦ < 18\")                                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"AGER\") #>                               Patient age recode {Children < 18}                                #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    86.1 ¦ 1.6 ¦ 82.6 ¦ 89.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       19,004 ¦    2,872 ¦   14,051 ¦   25,702 ¦    13.9 ¦ 1.6 ¦ 10.8 ¦ 17.4 ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"version survey::svyciprop() adjusts degrees freedom method = \"beta\".","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"svyciprop_adjusted(   formula,   design,   method = c(\"logit\", \"likelihood\", \"asin\", \"beta\", \"mean\", \"xlogit\"),   level = 0.95,   df_method,   ... )"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"formula see survey::svyciprop(). design see survey::svyciprop(). method see survey::svyciprop(). level see survey::svyciprop(). df_method df calculated: \"default\" \"NHIS\". ... see survey::svyciprop().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"point estimate proportion, confidence interval attribute.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"Written Makram Talih 2019. df_method: \"default\", df = degf(design); \"NHIS\", df = nrow(design) - 1. use function tabulations, call set_survey() opts = \"NCHS\" argument, type: options(surveytable.adjust_svyciprop = TRUE).","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"set_survey(namcs2019sv, opts = \"NCHS\") #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"AGER\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate variables — tab","title":"Tabulate variables — tab","text":"Tabulate categorical (factor), logical, numeric variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate variables — tab","text":"","code":"tab(   ...,   test = FALSE,   alpha = 0.05,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate variables — tab","text":"... names variables (quotes) test perform hypothesis tests? alpha significance level tests drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate variables — tab","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate variables — tab","text":"categorical logical variables, presents estimated counts, standard errors (SEs) confidence intervals (CIs), percentages, SEs CIs. Checks presentation guidelines counts percentages flags estimates , according guidelines, suppressed, footnoted, reviewed analyst. numeric variables, presents percentage observations known values, mean known values, standard error mean (SEM), standard deviation (SD). CIs calculated 95% confidence level. CIs count estimates log Student's t CIs, adaptations complex surveys. CIs percentage estimates Korn Graubard CIs.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate variables — tab","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"AGER\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  tab(\"MDDO\", \"SPECCAT\", \"MSA\") #>                            Type of doctor (MD or DO) {NAMCS 2019 PUF}                            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦      980,280 ¦   48,388 ¦  889,842 ¦ 1,079,910 ¦    94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦       56,204 ¦    6,602 ¦   44,597 ¦    70,832 ¦     5.4 ¦ 0.7 ¦  4.2 ¦  6.9 ¦ #> ¦ of Osteopathy ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>  #>                 Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                 #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care  ¦      521,466 ¦   31,136 ¦  463,840 ¦  586,252 ¦    50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦      214,832 ¦   31,110 ¦  161,661 ¦  285,490 ¦    20.7 ¦ 3   ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care  ¦      300,186 ¦   43,497 ¦  225,806 ¦  399,067 ¦    29   ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty     ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>            Metropolitan Statistical Area Status of physician location {NAMCS 2019 #> PUF}            #> +----------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA           ¦      973,676 ¦   50,515 ¦  879,490 ¦ 1,077,947 ¦    93.9 ¦ 1.7 ¦ 89.7 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Statistical   ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> ¦ Area)         ¦              ¦          ¦          ¦           ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA       ¦       62,809 ¦   17,549 ¦   36,249 ¦   108,830 ¦     6.1 ¦ 1.7 ¦  3.2 ¦ 10.3 ¦ #> +----------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                           #>   # Numeric variables tab(\"NUMMED\") #> Number of medications coded {NAMCS 2019 PUF} #> +-------------------------------+ #> ¦ % known ¦ Mean ¦   SEM ¦   SD ¦ #> +---------+------+-------+------¦ #> ¦     100 ¦ 3.46 ¦ 0.268 ¦ 4.43 ¦ #> +-------------------------------+ #>   # Hypothesis testing with categorical variables tab(\"AGER\", test = TRUE) #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #> Comparison of all possible pairs of Patient age recode {NAMCS 2019 PUF} #> +-----------------------------------------------------+ #> ¦ Level 1        ¦ Level 2           ¦ p-value ¦ Flag ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 15-24 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 25-44 years       ¦   0.012 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 75 years and over ¦   0.022 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 25-44 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 65-74 years       ¦   0.065 ¦      ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 75 years and over ¦   0.878 ¦      ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 65-74 years    ¦ 75 years and over ¦   0.019 ¦ *    ¦ #> +-----------------------------------------------------+ #>   Design-based t-test. *: p-value <= 0.05               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates — tab_rate","title":"Calculate rates — tab_rate","text":"Calculate rates categorical (factor) logical variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates — tab_rate","text":"","code":"tab_rate(   vr,   pop,   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates — tab_rate","text":"vr variable tabulate pop either single number data.frame columns named Level Population. Level must exactly match levels vr. Population population level vr. per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates — tab_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates — tab_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  # pop is a data frame tab_rate(\"MSA\", uspop2019$MSA) #> Metropolitan Statistical Area Status of physician location (rate per #> 100 population) {NAMCS 2019 PUF} #> +-----------------------------------------------------------+ #> ¦ Level                      ¦  Rate ¦   SE ¦    LL ¦    UL ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ MSA (Metropolitan          ¦ 351.2 ¦ 18.2 ¦ 317.2 ¦ 388.8 ¦ #> ¦ Statistical Area)          ¦       ¦      ¦       ¦       ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ Non-MSA                    ¦ 136.7 ¦ 38.2 ¦  78.9 ¦ 236.8 ¦ #> +-----------------------------------------------------------+ #>   (Checked presentation standards. Nothing to                 #>   report.)                                                    #>   # pop is a single number tab_rate(\"MDDO\", uspop2019$total) #> * Rate based on the entire population. #> Type of doctor (MD or DO) (rate per 100 population) {NAMCS 2019 PUF} #> +---------------------------------------------------------+ #> ¦ Level                      ¦  Rate ¦ SE ¦    LL ¦    UL ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ M.D. - Doctor of Medicine  ¦ 303.3 ¦ 15 ¦ 275.3 ¦ 334.1 ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ D.O. - Doctor of           ¦  17.4 ¦  2 ¦  13.8 ¦  21.9 ¦ #> ¦ Osteopathy                 ¦       ¦    ¦       ¦       ¦ #> +---------------------------------------------------------+ #>   (Checked presentation standards. Nothing to               #>   report.)                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate subsets or interactions — tab_cross","title":"Tabulate subsets or interactions — tab_cross","text":"Create subsets survey using one variable, tabulate another variable within subsets. Interact two variables tabulate.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"tab_cross(   vr,   vrby,   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )  tab_subset(   vr,   vrby,   lvls = c(),   test = FALSE,   alpha = 0.05,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate subsets or interactions — tab_cross","text":"vr variable tabulate vrby use variable subset survey max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file lvls (optional) show levels vrby test perform hypothesis tests? alpha significance level tests drop_na drop missing values (NA)? Categorical variables .","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate subsets or interactions — tab_cross","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate subsets or interactions — tab_cross","text":"tab_subset creates subsets using levels vrby, tabulates vr subset. Optionally, use lvls levels vrby. vr can categorical (factor), logical, numeric. tab_cross crosses interacts vr vrby tabulates new variable. Tables created using tab_subset tab_cross counts different percentages. tab_subset, percentages within subset add 100%. tab_cross, percentages across entire population add 100%. Also see var_cross(). test = TRUE performs test association two variables. Also performs t-tests possible pairs levels vr vrby.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>   # For each SEX, tabulate AGER tab_subset(\"AGER\", \"SEX\") #>                   Patient age recode (Patient sex = Female) {NAMCS 2019 PUF}                    #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       59,958 ¦    7,206 ¦   47,318 ¦   75,974 ¦     9.9 ¦ 1.2 ¦  7.6 ¦ 12.6 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       41,128 ¦    4,532 ¦   33,066 ¦   51,156 ¦     6.8 ¦ 0.7 ¦  5.4 ¦  8.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      113,708 ¦   11,461 ¦   93,256 ¦  138,646 ¦    18.8 ¦ 1.6 ¦ 15.8 ¦ 22.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      175,978 ¦   16,009 ¦  147,153 ¦  210,450 ¦    29.1 ¦ 1.7 ¦ 25.8 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      120,099 ¦   11,066 ¦  100,171 ¦  143,992 ¦    19.8 ¦ 1.5 ¦ 17   ¦ 22.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       94,173 ¦   11,085 ¦   74,682 ¦  118,751 ¦    15.6 ¦ 1.5 ¦ 12.8 ¦ 18.7 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>                    Patient age recode (Patient sex = Male) {NAMCS 2019 PUF}                     #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       57,959 ¦    7,728 ¦   44,570 ¦   75,371 ¦    13.4 ¦ 1.7 ¦ 10.3 ¦ 17.1 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       23,728 ¦    4,344 ¦   16,457 ¦   34,210 ¦     5.5 ¦ 0.8 ¦  4   ¦  7.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦       56,562 ¦    7,277 ¦   43,861 ¦   72,942 ¦    13.1 ¦ 1.3 ¦ 10.7 ¦ 15.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      133,528 ¦   12,956 ¦  110,319 ¦  161,619 ¦    30.9 ¦ 1.6 ¦ 27.8 ¦ 34.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦       86,766 ¦    6,767 ¦   74,409 ¦  101,176 ¦    20.1 ¦ 1.5 ¦ 17.3 ¦ 23.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       72,896 ¦    6,661 ¦   60,872 ¦   87,296 ¦    16.9 ¦ 1.5 ¦ 14   ¦ 20.2 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>   # Same counts as tab_subset(), but different percentages. tab_cross(\"AGER\", \"SEX\") #>                      (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}                      #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       59,958 ¦    7,206 ¦   47,318 ¦   75,974 ¦     5.8 ¦ 0.7 ¦  4.5 ¦  7.3 ¦ #> ¦ years :       ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       41,128 ¦    4,532 ¦   33,066 ¦   51,156 ¦     4   ¦ 0.4 ¦  3.2 ¦  4.9 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦      113,708 ¦   11,461 ¦   93,256 ¦  138,646 ¦    11   ¦ 1   ¦  9   ¦ 13.2 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      175,978 ¦   16,009 ¦  147,153 ¦  210,450 ¦    17   ¦ 1.1 ¦ 14.9 ¦ 19.3 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦      120,099 ¦   11,066 ¦  100,171 ¦  143,992 ¦    11.6 ¦ 1   ¦  9.7 ¦ 13.7 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       94,173 ¦   11,085 ¦   74,682 ¦  118,751 ¦     9.1 ¦ 0.9 ¦  7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       57,959 ¦    7,728 ¦   44,570 ¦   75,371 ¦     5.6 ¦ 0.7 ¦  4.3 ¦  7.2 ¦ #> ¦ years : Male  ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       23,728 ¦    4,344 ¦   16,457 ¦   34,210 ¦     2.3 ¦ 0.4 ¦  1.6 ¦  3.2 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦       56,562 ¦    7,277 ¦   43,861 ¦   72,942 ¦     5.5 ¦ 0.6 ¦  4.3 ¦  6.8 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      133,528 ¦   12,956 ¦  110,319 ¦  161,619 ¦    12.9 ¦ 1   ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦       86,766 ¦    6,767 ¦   74,409 ¦  101,176 ¦     8.4 ¦ 0.6 ¦  7.2 ¦  9.7 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       72,896 ¦    6,661 ¦   60,872 ¦   87,296 ¦     7   ¦ 0.6 ¦  5.9 ¦  8.3 ¦ #> ¦ over : Male   ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>   # Numeric variables tab_subset(\"NUMMED\", \"AGER\") #> Number of medications coded (for different levels of Patient age #> recode) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level             ¦ % known ¦ Mean ¦   SEM ¦   SD ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ Under 15 years    ¦     100 ¦ 1.58 ¦ 0.168 ¦ 1.75 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 15-24 years       ¦     100 ¦ 1.64 ¦ 0.112 ¦ 1.7  ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 25-44 years       ¦     100 ¦ 2.15 ¦ 0.225 ¦ 2.74 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 45-64 years       ¦     100 ¦ 3.49 ¦ 0.303 ¦ 4.49 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 65-74 years       ¦     100 ¦ 4.44 ¦ 0.431 ¦ 5.03 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 75 years and over ¦     100 ¦ 5.53 ¦ 0.494 ¦ 5.59 ¦ #> +---------------------------------------------------+ #>   # Hypothesis testing tab_subset(\"NUMMED\", \"AGER\", test = TRUE) #> Number of medications coded (for different levels of Patient age #> recode) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level             ¦ % known ¦ Mean ¦   SEM ¦   SD ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ Under 15 years    ¦     100 ¦ 1.58 ¦ 0.168 ¦ 1.75 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 15-24 years       ¦     100 ¦ 1.64 ¦ 0.112 ¦ 1.7  ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 25-44 years       ¦     100 ¦ 2.15 ¦ 0.225 ¦ 2.74 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 45-64 years       ¦     100 ¦ 3.49 ¦ 0.303 ¦ 4.49 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 65-74 years       ¦     100 ¦ 4.44 ¦ 0.431 ¦ 5.03 ¦ #> +-------------------+---------+------+-------+------¦ #> ¦ 75 years and over ¦     100 ¦ 5.53 ¦ 0.494 ¦ 5.59 ¦ #> +---------------------------------------------------+ #>  #> Association between Number of medications coded and Patient age recode #> {NAMCS 2019 PUF} #> +-----------------------------------+ #> ¦         p-value ¦ Flag            ¦ #> +-----------------+-----------------¦ #> ¦               0 ¦ *               ¦ #> +-----------------------------------+ #>   Wald test. *: p-value <= 0.05       #>  #> Comparison of Number of medications coded across all possible pairs of #> Patient age recode {NAMCS 2019 PUF} #> +-----------------------------------------------------+ #> ¦ Level 1        ¦ Level 2           ¦ p-value ¦ Flag ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 15-24 years       ¦   0.739 ¦      ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 25-44 years       ¦   0.043 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 25-44 years       ¦   0.029 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 15-24 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 45-64 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 65-74 years       ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 65-74 years       ¦   0.007 ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years    ¦ 75 years and over ¦   0     ¦ *    ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 65-74 years    ¦ 75 years and over ¦   0.002 ¦ *    ¦ #> +-----------------------------------------------------+ #>   Design-based t-test. *: p-value <= 0.05               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates for subsets — tab_subset_rate","title":"Calculate rates for subsets — tab_subset_rate","text":"Create subsets survey using one variable, tabulate rates another variable within subsets.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"tab_subset_rate(   vr,   vrby,   pop,   lvls = c(),   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates for subsets — tab_subset_rate","text":"vr variable tabulate vrby use variable subset survey pop data.frame columns named Level, Subset, Population. Level must exactly match levels vr. Subset must exactly match levels vrby. Population population level vr vrby. lvls (optional) show levels vrby per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates for subsets — tab_subset_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`) #> Patient age recode (Patient sex = Female) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level             ¦        Rate ¦          SE ¦          LL ¦          UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years    ¦       202.5 ¦        24.3 ¦       159.8 ¦       256.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years       ¦       198.4 ¦        21.9 ¦       159.5 ¦       246.8 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years       ¦       263.3 ¦        26.5 ¦       215.9 ¦       321   ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years       ¦       414   ¦        37.7 ¦       346.2 ¦       495.1 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years       ¦       720.3 ¦        66.4 ¦       600.8 ¦       863.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦       758.1 ¦        89.2 ¦       601.2 ¦       956   ¦ #> +---------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                        #>  #> Patient age recode (Patient sex = Male) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level             ¦        Rate ¦          SE ¦          LL ¦          UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years    ¦       187.4 ¦        25   ¦       144.1 ¦       243.7 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years       ¦       113.1 ¦        20.7 ¦        78.4 ¦       163   ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years       ¦       133.4 ¦        17.2 ¦       103.4 ¦       172   ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years       ¦       333.4 ¦        32.3 ¦       275.4 ¦       403.5 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years       ¦       594.8 ¦        46.4 ¦       510.1 ¦       693.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦       801.2 ¦        73.2 ¦       669.1 ¦       959.5 ¦ #> +---------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                        #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":null,"dir":"Reference","previous_headings":"","what":"Total count — total","title":"Total count — total","text":"Total count","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total count — total","text":"","code":"total(csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total count — total","text":"csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total count — total","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total count — total","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  total() #>                      Total {NAMCS 2019 PUF}                       #> +---------------------------------------------------------------+ #> ¦  Number (000) ¦      SE (000) ¦      LL (000) ¦      UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦     1,036,484 ¦        48,836 ¦       945,014 ¦     1,136,809 ¦ #> +---------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Overall rate — total_rate","title":"Overall rate — total_rate","text":"Overall rate","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Overall rate — total_rate","text":"","code":"total_rate(   pop,   per = getOption(\"surveytable.rate_per\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Overall rate — total_rate","text":"pop population per calculate rate per many items population csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Overall rate — total_rate","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Overall rate — total_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  total_rate(uspop2019$total) #>         Total (rate per 100 population) {NAMCS 2019 PUF}          #> +---------------------------------------------------------------+ #> ¦          Rate ¦            SE ¦            LL ¦            UL ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦         320.7 ¦          15.1 ¦         292.4 ¦         351.7 ¦ #> +---------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":null,"dir":"Reference","previous_headings":"","what":"US Population in 2019 — uspop2019","title":"US Population in 2019 — uspop2019","text":"Population estimates civilian non-institutional population United States July 1, 2019. Used calculating rates. usage examples, see *_rate functions.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"US Population in 2019 — uspop2019","text":"","code":"uspop2019"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"US Population in 2019 — uspop2019","text":"object class list length 7.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Are all the variables true? (Logical AND) — var_all","title":"Are all the variables true? (Logical AND) — var_all","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"var_all(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Are all the variables true? (Logical AND) — var_all","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Are all the variables true? (Logical AND) — var_all","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\")) tab(\"Medicare and Medicaid\") #>                          Medicare and Medicaid {NAMCS 2019 PUF}                          #> +--------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ FALSE ¦    1,016,202 ¦   47,395 ¦  927,389 ¦ 1,113,520 ¦      98 ¦ 0.5 ¦ 96.9 ¦ 98.9 ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ TRUE  ¦       20,282 ¦    5,177 ¦   12,120 ¦    33,941 ¦       2 ¦ 0.5 ¦  1.1 ¦  3.1 ¦ #> +--------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                   #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Is any variable true? (Logical OR) — var_any","title":"Is any variable true? (Logical OR) — var_any","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"var_any(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is any variable true? (Logical OR) — var_any","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is any variable true? (Logical OR) — var_any","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_any(\"Imaging services\" , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\" , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") #>                            Imaging services {NAMCS 2019 PUF}                            #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦      901,115 ¦   43,298 ¦  820,085 ¦  990,151 ¦    86.9 ¦ 1.1 ¦ 84.6 ¦ 89.1 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE  ¦      135,369 ¦   13,574 ¦  111,134 ¦  164,890 ¦    13.1 ¦ 1.1 ¦ 10.9 ¦ 15.4 ¦ #> +-------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert factor to logical — var_case","title":"Convert factor to logical — var_case","text":"Convert factor logical","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert factor to logical — var_case","text":"","code":"var_case(newvr, vr, cases)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert factor to logical — var_case","text":"newvr name new logical variable created vr factor variable cases one levels vr converted TRUE. levels converted FALSE.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert factor to logical — var_case","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert factor to logical — var_case","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") #>                         Preventive care visits {NAMCS 2019 PUF}                         #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦      812,861 ¦   45,220 ¦  728,841 ¦  906,566 ¦    78.4 ¦ 1.7 ¦ 74.9 ¦ 81.7 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE  ¦      223,624 ¦   18,520 ¦  190,068 ¦  263,103 ¦    21.6 ¦ 1.7 ¦ 18.3 ¦ 25.1 ¦ #> +-------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                  #>  var_case(\"Surgery-related visits\" , \"MAJOR\" , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") #>                         Surgery-related visits {NAMCS 2019 PUF}                          #> +--------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦  UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ FALSE ¦      969,451 ¦   47,976 ¦  879,793 ¦ 1,068,246 ¦    93.5 ¦ 0.8 ¦ 91.9 ¦ 94.9 ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ TRUE  ¦       67,034 ¦    7,810 ¦   53,273 ¦    84,348 ¦     6.5 ¦ 0.8 ¦  5.1 ¦  8.1 ¦ #> +--------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                   #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse factor levels — var_collapse","title":"Collapse factor levels — var_collapse","text":"Collapse two levels factor variable single level.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse factor levels — var_collapse","text":"","code":"var_collapse(vr, newlevel, oldlevels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse factor levels — var_collapse","text":"vr factor variable newlevel name new level oldlevels vector old levels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse factor levels — var_collapse","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse factor levels — var_collapse","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  tab(\"PRIMCARE\") #>                  Are you the patient's primary care provider? {NAMCS 2019 PUF}                   #> +----------------------------------------------------------------------------------------------+ #> ¦ Level   ¦      Number ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ Flags ¦ #> ¦         ¦       (000) ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦       ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Blank   ¦       1,150 ¦      478 ¦      440 ¦    3,005 ¦     0.1 ¦ 0   ¦  0   ¦  0.2 ¦ Cx    ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Unknown ¦      39,519 ¦    9,507 ¦   24,520 ¦   63,692 ¦     3.8 ¦ 0.9 ¦  2.3 ¦  6   ¦       ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Yes     ¦     383,481 ¦   28,555 ¦  331,362 ¦  443,798 ¦    37   ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦       ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ No      ¦     612,335 ¦   43,282 ¦  533,050 ¦  703,413 ¦    59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦       ¦ #> +----------------------------------------------------------------------------------------------+ #>   Cx: suppress count (and rate)                                                                  #>  var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Blank\", \"Unknown\")) tab(\"PRIMCARE\") #>                  Are you the patient's primary care provider? {NAMCS 2019 PUF}                  #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Unknown if    ¦       40,669 ¦    9,479 ¦   25,619 ¦   64,560 ¦     3.9 ¦ 0.9 ¦  2.4 ¦  6.1 ¦ #> ¦ PCP           ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Yes           ¦      383,481 ¦   28,555 ¦  331,362 ¦  443,798 ¦    37   ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ No            ¦      612,335 ¦   43,282 ¦  533,050 ¦  703,413 ¦    59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Copy a variable — var_copy","title":"Copy a variable — var_copy","text":"Create new variable copy another variable. can modify copy, original remains unchanged. See examples.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Copy a variable — var_copy","text":"","code":"var_copy(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Copy a variable — var_copy","text":"newvr name new variable created vr variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Copy a variable — var_copy","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Copy a variable — var_copy","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_copy(\"Age group\", \"AGER\") var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\") #>                               Patient age recode {NAMCS 2019 PUF}                               #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years   ¦      170,271 ¦   13,966 ¦  144,925 ¦  200,049 ¦    16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years   ¦      309,506 ¦   23,290 ¦  266,994 ¦  358,787 ¦    29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years   ¦      206,866 ¦   14,366 ¦  180,481 ¦  237,109 ¦    20   ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦      167,069 ¦   15,179 ¦  139,746 ¦  199,735 ¦    16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>  #>                                   Age group {NAMCS 2019 PUF}                                    #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦      117,917 ¦   14,097 ¦   93,229 ¦  149,142 ¦    11.4 ¦ 1.3 ¦  8.9 ¦ 14.2 ¦ #> ¦ years         ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years   ¦       64,856 ¦    7,018 ¦   52,387 ¦   80,292 ¦     6.3 ¦ 0.6 ¦  5.1 ¦  7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-64         ¦      479,777 ¦   32,175 ¦  420,624 ¦  547,247 ¦    46.3 ¦ 1.8 ¦ 42.7 ¦ 49.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65+           ¦      373,935 ¦   24,523 ¦  328,777 ¦  425,296 ¦    36.1 ¦ 1.9 ¦ 32.3 ¦ 40   ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross or interact two variables — var_cross","title":"Cross or interact two variables — var_cross","text":"Create new variable interaction two variables. Also see tab_cross().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross or interact two variables — var_cross","text":"","code":"var_cross(newvr, vr, vrby)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross or interact two variables — var_cross","text":"newvr name new variable created vr first variable vrby second variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cross or interact two variables — var_cross","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross or interact two variables — var_cross","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"Age x Sex\") #>                      (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}                      #> +---------------------------------------------------------------------------------------------+ #> ¦ Level         ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       59,958 ¦    7,206 ¦   47,318 ¦   75,974 ¦     5.8 ¦ 0.7 ¦  4.5 ¦  7.3 ¦ #> ¦ years :       ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       41,128 ¦    4,532 ¦   33,066 ¦   51,156 ¦     4   ¦ 0.4 ¦  3.2 ¦  4.9 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦      113,708 ¦   11,461 ¦   93,256 ¦  138,646 ¦    11   ¦ 1   ¦  9   ¦ 13.2 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      175,978 ¦   16,009 ¦  147,153 ¦  210,450 ¦    17   ¦ 1.1 ¦ 14.9 ¦ 19.3 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦      120,099 ¦   11,066 ¦  100,171 ¦  143,992 ¦    11.6 ¦ 1   ¦  9.7 ¦ 13.7 ¦ #> ¦ Female        ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       94,173 ¦   11,085 ¦   74,682 ¦  118,751 ¦     9.1 ¦ 0.9 ¦  7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15      ¦       57,959 ¦    7,728 ¦   44,570 ¦   75,371 ¦     5.6 ¦ 0.7 ¦  4.3 ¦  7.2 ¦ #> ¦ years : Male  ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦       23,728 ¦    4,344 ¦   16,457 ¦   34,210 ¦     2.3 ¦ 0.4 ¦  1.6 ¦  3.2 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦       56,562 ¦    7,277 ¦   43,861 ¦   72,942 ¦     5.5 ¦ 0.6 ¦  4.3 ¦  6.8 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦      133,528 ¦   12,956 ¦  110,319 ¦  161,619 ¦    12.9 ¦ 1   ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦       86,766 ¦    6,767 ¦   74,409 ¦  101,176 ¦     8.4 ¦ 0.6 ¦  7.2 ¦  9.7 ¦ #> ¦ Male          ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and  ¦       72,896 ¦    6,661 ¦   60,872 ¦   87,296 ¦     7   ¦ 0.6 ¦  5.9 ¦  8.3 ¦ #> ¦ over : Male   ¦              ¦          ¦          ¦          ¦         ¦     ¦      ¦      ¦ #> +---------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert numeric to factor — var_cut","title":"Convert numeric to factor — var_cut","text":"Create new categorical variable based numeric variable.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert numeric to factor — var_cut","text":"","code":"var_cut(newvr, vr, breaks, labels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert numeric to factor — var_cut","text":"newvr name new factor variable created vr numeric variable breaks see cut() labels see cut()","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert numeric to factor — var_cut","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert numeric to factor — var_cut","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_cut(\"Age group\" , \"AGE\" , c(-Inf, 0, 4, 14, 64, Inf) , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\")) tab(\"Age group\") #>                                  Age group {NAMCS 2019 PUF}                                   #> +-------------------------------------------------------------------------------------------+ #> ¦ Level       ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦  SE ¦   LL ¦   UL ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 1     ¦       31,148 ¦    5,282 ¦   22,269 ¦   43,566 ¦     3   ¦ 0.5 ¦  2.1 ¦  4.1 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 1-4         ¦       38,240 ¦    5,444 ¦   28,864 ¦   50,662 ¦     3.7 ¦ 0.5 ¦  2.7 ¦  4.8 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 5-14        ¦       48,529 ¦    5,741 ¦   38,430 ¦   61,282 ¦     4.7 ¦ 0.5 ¦  3.7 ¦  5.9 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-64       ¦      544,632 ¦   36,082 ¦  478,254 ¦  620,223 ¦    52.5 ¦ 2   ¦ 48.6 ¦ 56.5 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65 and over ¦      373,935 ¦   24,523 ¦  328,777 ¦  425,296 ¦    36.1 ¦ 1.9 ¦ 32.3 ¦ 40   ¦ #> +-------------------------------------------------------------------------------------------+ #>   (Checked presentation standards. Nothing to report.)                                        #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":null,"dir":"Reference","previous_headings":"","what":"List variables in a survey. — var_list","title":"List variables in a survey. — var_list","text":"List variables survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List variables in a survey. — var_list","text":"","code":"var_list(sw = \"\", all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List variables in a survey. — var_list","text":"sw starting characters variable name (case insensitive) print variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List variables in a survey. — var_list","text":"table","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List variables in a survey. — var_list","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_list(\"age\") #> Variables beginning with 'age' {NAMCS 2019 PUF} #> +-------------------------------------------+ #> ¦ Variable ¦ Class   ¦ Long name            ¦ #> +----------+---------+----------------------¦ #> ¦ AGE      ¦ numeric ¦ Patient age in years ¦ #> +----------+---------+----------------------¦ #> ¦ AGER     ¦ factor  ¦ Patient age recode   ¦ #> +-------------------------------------------+ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical NOT — var_not","title":"Logical NOT — var_not","text":"Logical ","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical NOT — var_not","text":"","code":"var_not(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical NOT — var_not","text":"newvr name new variable created vr logical variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logical NOT — var_not","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical NOT — var_not","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> +---------------------------------------------------------------------------+ #> ¦ Variables ¦ Observations ¦ Design                                         ¦ #> +-----------+--------------+------------------------------------------------¦ #> ¦        33 ¦        8,250 ¦ Stratified 1 - level Cluster Sampling design   ¦ #> ¦           ¦              ¦ (with replacement)                             ¦ #> ¦           ¦              ¦ With (398) clusters.                           ¦ #> ¦           ¦              ¦ survey::svydesign(ids = ~CPSUM, strata =       ¦ #> ¦           ¦              ¦ ~CSTRATM, weights = ~PATWT,                    ¦ #> ¦           ¦              ¦ data = namcs2019sv_df)                         ¦ #> +---------------------------------------------------------------------------+ #>  var_not(\"Private insurance not used\", \"PAYPRIV\")"},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-development-version","dir":"Changelog","previous_headings":"","what":"surveytable (development version)","title":"surveytable (development version)","text":"codebook() Improved output. Allows unweighted survey data.frame. Can set certain options using argument.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-092","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.2","title":"surveytable 0.9.2","text":"CRAN release: 2024-01-18 Addressed CRAN comments.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-091","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.1","title":"surveytable 0.9.1","text":"Initial CRAN submission.","code":""}]
diff --git a/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd b/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
index 3e9ed47..eefabc8 100644
--- a/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
+++ b/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
@@ -14,9 +14,9 @@ knitr::opts_chunk$set(
 )
 ```
 
-This example uses the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) to replicate certain tables from the [National Ambulatory Medical Care Survey: 2019 National Summary Tables](https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf). NAMCS is "an annual nationally representative sample survey of visits to non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists." Note that the unit of observation is visits, not patients – this distinction is important since a single patient can make multiple visits.
+This example uses the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) to replicate certain tables from the [National Ambulatory Medical Care Survey: 2019 National Summary Tables](https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf). NAMCS is "an annual nationally representative sample survey of visits to non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists." Note that the unit of observation is visits, not patients -- this distinction is important since a single patient can make multiple visits.
 
-Selected variables from NAMCS 2019 come with the `surveytable` package, for use in examples, in an object called `namcs2019sv`. To make the below code more reusable, we'll call the survey by a generic name, namely, `mysurvey`.
+Selected variables from NAMCS 2019 come with the `surveytable` package, for use in examples, in an object called `namcs2019sv`.
 
 # Begin
 
@@ -29,8 +29,7 @@ library(surveytable)
 Now, specify the survey that you'd like to analyze.
 
 ```{r, results='asis'}
-mysurvey = namcs2019sv
-set_survey(mysurvey)
+set_survey(namcs2019sv)
 ```
 
 Check the survey name, survey design variables, and the number of observations to verify that it all looks correct.
@@ -67,15 +66,9 @@ Once we have the overall population estimate, the overall rate is:
 total_rate(uspop2019$total)
 ```
 
-Recall that survey objects have an element called `variables`, which is a data frame that contains the survey variables. Let's examine the levels of `MSA`.
+To calculate the rates for a particular variable, we need to provide a data frame with a variable called `Level` that matches the levels of the variable in the survey, and a variable called `Population` that gives the population size (which is assumed to be a constant rather than a random variable).
 
-```{r}
-levels(mysurvey$variables$MSA)
-```
-
-To calculate the rates for a particular variable, we need to provide a data frame with a variable called `Level` that matches the levels of the variable in the survey. `Population` gives the population estimate.
-
-For example, for `MSA`, we need a data frame as follows:
+For `MSA`, we can see the levels of the variables just by using the `tab()` command, just as we did above. Thus, to calculate rates, we need a data frame as follows:
 
 ```{r}
 uspop2019$MSA