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final_report_figures.Rmd
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---
title: "2023 PSRC Report Tables"
author: "James Clark"
date: "`r Sys.Date()`"
output:
word_document
---
```{r, setup, echo = FALSE, warning = FALSE}
# Setup=========================================================================
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(message = FALSE)
# remotes::install_github('RSGInc/travelSurveyTools')
library(travelSurveyTools)
library(data.table)
library(stringr)
library(janitor)
library(readxl)
library(flextable)
user = Sys.info()['user']
data_dir = str_glue(
"C:/Users/{user}/Resource Systems Group, Inc/Transportation MR - Documents/",
"PSRC Survey Program/210252_PSRC_HTS/",
"2023 Puget Sound Travel Study/5.Deliverables/7_Data&Documentation/",
"0_Data_Inputs_From_PSRC/from_psrc_20240201"
)
hh = fread(file.path(data_dir, 'Household.csv'))
person = fread(file.path(data_dir, 'Person.csv'))
day = fread(file.path(data_dir, 'Day.csv'))
trip = fread(file.path(data_dir, 'Trip.csv'))
# load codebook
codebook_path = str_glue(
"C:/Users/{user}/Resource Systems Group, Inc/Transportation MR - Documents/",
"PSRC Survey Program/210252_PSRC_HTS/Internal/3.DataAnalysis/1.Data/Codebook/",
"PSRC_Combined_Codebook_2023_08162023_RSG.xlsx"
)
variable_list = read_excel(codebook_path, sheet = 'variable_list_2023')
value_labels = read_excel(codebook_path, sheet = 'value_labels_2023')
value_crosswalk = read_excel(codebook_path, sheet = 'value_crosswalk_2023')
setDT(variable_list)
setDT(value_labels)
setDT(value_crosswalk)
wt_dir = str_glue(
"C:/Users/{user}/Resource Systems Group, Inc/Transportation MR - Documents/",
"PSRC Survey Program/210252_PSRC_HTS/2023 Puget Sound Travel Study/",
"5.Deliverables/9_Weighting/2_RSG_Outputs"
)
# Read in weights
hh_weights = fread(file.path(wt_dir, 'hh_weights.csv'))
person_weights = fread(file.path(wt_dir, 'person_weights.csv'))
day_weights = fread(file.path(wt_dir, 'day_weights.csv'))
trip_weights = fread(file.path(wt_dir, 'trip_weights.csv'))
```
```{r data_prep, echo = FALSE, warning = FALSE}
# Prepare Data for tables=======================================================
# Prepare codebook--------------------------------------------------------------
# rename columns to match table names
old_table_names = names(variable_list)[str_detect(names(variable_list), '_final')]
new_table_names = str_remove(old_table_names, '_final')
setnames(variable_list, old_table_names, new_table_names)
# Rename other columns to match expected input for travelSurveyTools
old_names = c('psrc_variable', 'description_2023', 'data_type_2023', 'var_logic')
new_names = c('variable', 'description', 'data_type', 'logic')
setnames(variable_list, old_names, new_names)
# Only keep the important variables
keep_vars = c(new_table_names, new_names)
variable_list = variable_list[, ..keep_vars]
# Find checkbox variables i.e. have "-- " in the description
variable_list[, is_checkbox := 1 * str_detect(description, "--")]
# Create shared_name variable
variable_list[, shared_name :=
ifelse(is_checkbox == 1,
sub("_[^_]*$", "", variable),
variable
)]
# assigned tables, weights, and ids to lists to feed into functions
# Append the weights------------------------------------------------------------
# drop old weights (all placeholder 1s)
hh[, hh_weight_2023 := NULL]
person[, person_weight := NULL]
day[, day_weight_2023 := NULL]
trip[, trip_weight := NULL]
# make all ids characters
hh[, hhid := as.character(hhid)]
hh_weights[, hh_id := as.character(hh_id)]
person[, person_id := as.character(person_id)]
person[, hhid := as.character(hhid)]
person_weights[, person_id := as.character(person_id)]
day[, day_id := as.character(day_id)]
day[, hhid := as.character(hhid)]
day[, person_id := as.character(person_id)]
day_weights[, day_id := as.character(day_id)]
trip[, tripid := as.character(tripid)]
trip[, hhid := as.character(hhid)]
trip[, person_id := as.character(person_id)]
trip[, day_id := as.character(day_id)]
trip_weights[, trip_id := as.character(trip_id)]
# attach new weights
hh[
hh_weights,
hh_weight_2023 := i.hh_weight,
on = c('hhid' = 'hh_id')
]
person[
person_weights,
person_weight := i.person_weight,
on = 'person_id'
]
day[
day_weights,
day_weight_2023 := i.day_weight,
on = 'day_id'
]
trip[
trip_weights,
trip_weight := i.trip_weight,
on = c('tripid' = 'trip_id')
]
#check the weights
stopifnot(sum(hh$hh_weight_2023, na.rm = T) == sum(hh_weights$hh_weight))
stopifnot(sum(person$person_weight, na.rm = T) == sum(person_weights$person_weight))
stopifnot(sum(day$day_weight_2023, na.rm = T) == sum(day_weights$day_weight))
stopifnot(sum(trip$trip_weight, na.rm = T) == sum(trip_weights$trip_weight))
tables = list('hh' = hh,
'person' = person,
'day' = day,
'trip' = trip)
# drop households that are no longer complete after re-calculating completes
tables = hts_filter_data(tables,
hh[!is.na(hh_weight_2023), hhid],
'hhid')
ids = c('hhid', 'person_id', 'day_id', 'tripid')
wts = c('hh_weight_2023', 'person_weight', 'day_weight_2023', 'trip_weight')
```
```{r define functions, echo = FALSE, warning = FALSE}
# Create functions to make tables using travelSurveyTools-----------------------
# functions makes flextable with weighted/unweighted counts + percents
make_reporting_table = function(summarize_var,
variable_list,
tables,
ids,
wts,
wtname,
label,
value_labels = NULL,
shared = FALSE){
# create input for hts_summary
prep = hts_prep_variable(
summarize_var = summarize_var,
variables_dt = variable_list,
data = tables,
id_cols = ids,
wt_cols = wts
)
#edit parameters for checkbox variables
if (shared){
checkbox_yes = 1
summarize_type = 'checkbox'
} else {
checkbox_yes = NULL
summarize_type = 'categorical'
}
# create summary
output = hts_summary(prep$cat,
summarize_var = summarize_var,
summarize_vartype = summarize_type,
id_cols = ids,
wtname = wtname,
checkbox_yesval = checkbox_yes)
unwtd = output$summary$unwtd
wtd = output$summary$wtd
# format outputs for table
setnames(unwtd, 'count', 'Unweighted Count')
unwtd[, `Unweighted %` := round(prop * 100, 2)]
setnames(wtd, 'est', 'Weighted Count')
wtd[, `Weighted Count` := round(`Weighted Count`)]
wtd[, `Weighted %` := round(prop * 100, 2)]
tab = merge(unwtd, wtd, all.x = TRUE, by = summarize_var)
#subset variables
keep_vars = c(summarize_var, 'Unweighted Count', 'Weighted Count', 'Unweighted %', 'Weighted %')
tab = tab[, ..keep_vars]
# label data
if (label){
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
}
# return as a formated flextable
tab %>%
adorn_totals(where = 'row') %>%
flextable() %>%
colformat_double(j = 4:5, suffix = '%')
}
# function to make table for trip rates-----------------------------------------
make_triprate_table = function(summarize_by,
variable_list,
tables,
ids,
wts,
label = FALSE
){
# Prep unweighted data
triprate_prep = hts_prep_triprate(summarize_by = summarize_by,
variables_dt = variable_list,
hts_data = tables,
ids = ids,
wts = wts,
remove_outliers = FALSE,
weighted = FALSE)
# get unwtd counts
unwtd_counts = triprate_prep$num[, .(`Unweighted Days` = .N,
`Unweighted Trips` = sum(num_trips_unwtd)),
summarize_by]
# get unwtd trip rates
output = hts_summary(triprate_prep$num,
summarize_var = 'num_trips_unwtd',
summarize_by = summarize_by,
summarize_vartype = 'numeric',
id_cols = ids,
wtname = 'day_weight_2023')
unwtd_triprate = output$summary$unwtd[,
.(
summarize_by = get(summarize_by),
'Unweighted Trip Rate' = round(mean, 2)
)
]
unwtd_tab = merge(unwtd_counts, unwtd_triprate, by.x = summarize_by, by.y = 'summarize_by')
# prep weighted data
triprate_prep = hts_prep_triprate(summarize_by = summarize_by,
variables_dt = variable_list,
hts_data = tables,
ids = ids,
wts = wts,
remove_outliers = FALSE,
weighted = TRUE)
# get weighted counts
wtd_counts = triprate_prep$num[, .(`Weighted Days` = round(sum(day_weight_2023)),
`Weighted Trips` = round(sum(num_trips_wtd))),
summarize_by]
# get weighted trip rates
output = hts_summary(triprate_prep$num,
summarize_var = 'num_trips_wtd',
summarize_by = summarize_by,
summarize_vartype = 'numeric',
id_cols = ids,
wtname = 'day_weight_2023')
wtd_triprate = output$summary$wtd[,
.(
summarize_by = get(summarize_by),
'Weighted Trip Rate' = round(mean, 2)
)
]
wtd_tab = merge(wtd_counts, wtd_triprate, by.x = summarize_by, by.y = 'summarize_by')
#combined weighted and unweighted
tab = merge(unwtd_tab, wtd_tab, by = summarize_by)
tab = tab[!get(summarize_by) %in% c('NULL', 'NA')]
# label and order
if (label){
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
}
# Add total row
total = data.table(
summarize_by = 'Total',
'Unweighted Days' = sum(tab$`Unweighted Days`),
'Unweighted Trips' = sum(tab$`Unweighted Trips`),
'Weighted Days' = sum(tab$`Weighted Days`),
'Weighted Trips' = sum(tab$`Weighted Trips`)
)
setnames(total, 'summarize_by', summarize_by)
total[, `Unweighted Trip Rate` := `Unweighted Trips` / `Unweighted Days`]
total[, `Weighted Trip Rate` := `Weighted Trips` / `Weighted Days`]
tab = rbind(tab, total)
# round trip rates
tab[, `Unweighted Trip Rate` := round(`Unweighted Trip Rate`, 2)]
tab[, `Weighted Trip Rate` := round(`Weighted Trip Rate`, 2)]
setcolorder(tab,
c(summarize_by,
'Unweighted Days',
'Unweighted Trips',
'Weighted Days',
'Weighted Trips',
'Unweighted Trip Rate',
'Weighted Trip Rate')
)
#return as flextable
return(tab %>% flextable())
}
```
# Demographics by participation group-------------------------------------------
# Demographics
# Counts by diary platform
```{r}
rmove_ids = tables$hh[diary_platform == 'rmove', hhid]
browser_ids = tables$hh[diary_platform == 'browser', hhid]
call_ids = tables$hh[diary_platform == 'call', hhid]
diary_platform_counts = data.table(
table = c('hh', 'person', 'trip'),
rmove_counts = c(
tables$hh[hhid %in% rmove_ids, .N],
tables$person[hhid %in% rmove_ids, .N],
tables$trip[hhid %in% rmove_ids, .N]
),
browser_counts = c(
tables$hh[hhid %in% browser_ids, .N],
tables$person[hhid %in% browser_ids, .N],
tables$trip[hhid %in% browser_ids, .N]
),
call_counts = c(
tables$hh[hhid %in% call_ids, .N],
tables$person[hhid %in% call_ids, .N],
tables$trip[hhid %in% call_ids, .N]
)
)
diary_platform_counts %>%
adorn_totals(where = 'col') %>%
flextable()
```
# TABLE 11: PARTICIPANT AGE BY PARTICIPATION MODE
```{r table 11, echo=FALSE}
prep = hts_prep_variable(summarize_var = 'age',
summarize_by = 'diary_platform',
variables_dt = variable_list,
data = tables,
id_cols = ids,
wt_cols = wts)
output = hts_summary(prep$cat,
summarize_var = 'age',
summarize_by = 'diary_platform',
summarize_vartype = 'categorical',
id_cols = ids,
wtname = 'person_weight')
tab = output$summary$wtd
tab$pct = round(tab$prop * 100, 2)
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
dcast(tab, age ~ diary_platform, value.var = 'pct') %>%
adorn_totals() %>%
flextable() %>%
colformat_double(suffix = '%')
```
# TABLE 12: PARTICIPANT HOUSEHOLD INCOME BY PARTICIPATION MODE
```{r table 12, echo=FALSE}
prep = hts_prep_variable(summarize_var = 'hhincome_broad',
summarize_by = 'diary_platform',
variables_dt = variable_list,
data = tables,
id_cols = ids,
wt_cols = wts)
output = hts_summary(prep$cat,
summarize_var = 'hhincome_broad',
summarize_by = 'diary_platform',
summarize_vartype = 'categorical',
id_cols = ids,
wtname = 'hh_weight_2023')
tab = output$summary$wtd
tab$pct = round(tab$prop * 100, 2)
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
dcast(tab, hhincome_broad ~ diary_platform, value.var = 'pct') %>%
adorn_totals() %>%
flextable() %>%
colformat_double(suffix = '%')
```
# TABLE 13: PARTICIPANT HOUSEHOLD SIZE BY PARTICIPATION MODE
```{r table 13, echo=FALSE}
# create a binned variable for household size
tables$hh[, hhsize_binned := ifelse(hhsize > 5, 5, hhsize)]
# Write new variable to variable list
variable_list = rbind(
variable_list,
data.table(
variable = 'hhsize_binned',
hh = 1,
person = 0,
vehicle = 0,
day = 0,
trip = 0,
location = 0,
description = 'binned household size',
data_type = 'integer/categorical',
logic = NA,
is_checkbox = 0,
shared_name = 'hhsize_binned'
)
)
prep = hts_prep_variable(summarize_var = 'hhsize_binned',
summarize_by = 'diary_platform',
variables_dt = variable_list,
data = tables,
id_cols = ids,
wt_cols = wts)
output = hts_summary(prep$cat,
summarize_var = 'hhsize_binned',
summarize_by = 'diary_platform',
summarize_vartype = 'categorical',
id_cols = ids,
wtname = 'hh_weight_2023')
tab = output$summary$wtd
tab$pct = round(tab$prop * 100, 2)
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
dcast(tab, hhsize_binned ~ diary_platform, value.var = 'pct') %>%
adorn_totals() %>%
flextable() %>%
colformat_double(suffix = '%')
```
# TABLE 14: PARTICIPANT RACE BY PARTICIPATION MODE
```{r table 14, echo=FALSE}
# Recode person table so each person is in only one race category
# Exclude Hispanic because it was asked in ethnicity
race_vars = variable_list[shared_name == 'race' & variable != 'race_hisp', variable]
#count responses for race
tables$person[, num_races := rowSums(tables$person[, ..race_vars] == 1)]
ethnicity_vars = variable_list[shared_name == 'ethnicity', variable]
# count responses for ethnicity
tables$person[, num_ethnicities := rowSums(tables$person[, ..ethnicity_vars] == 1)]
# code race_hisp
tables$person[, race_hisp := as.integer(ifelse(race_white == 1 & ethnicity_1 == 0, 1, 0))]
tables$person[ race_hisp == 1, race_white := 0]
# Create variables for multiple races
tables$person[, race_multiple := as.integer(ifelse(num_races > 1 | num_ethnicities > 1, 1, 0))]
tables$person[race_multiple == 1, `:=`
(race_afam = 0,
race_aiak = 0,
race_asian = 0,
race_hapi = 0,
race_noanswer = 0,
race_other = 0,
race_white = 0,
race_hisp = 0)]
variable_list = rbind(
variable_list,
data.table(
variable = 'race_multiple',
hh = 1,
person = 0,
vehicle = 0,
day = 0,
trip = 0,
location = 0,
description = 'Race -- Multiple races or ethnicities',
data_type = 'integer/categorical',
logic = NA,
is_checkbox = 0,
shared_name = 'race'
)
)
prep = hts_prep_variable(summarize_var = 'race',
summarize_by = 'diary_platform',
variables_dt = variable_list,
data = tables,
id_cols = ids,
wt_cols = wts)
output = hts_summary(prep$cat,
summarize_var = 'race',
summarize_by = 'diary_platform',
summarize_vartype = 'checkbox',
id_cols = ids,
wtname = 'person_weight',
checkbox_yesval = 1)
tab = output$summary$wtd
tab$pct = round(tab$prop * 100, 2)
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
dcast(tab, race ~ diary_platform, value.var = 'pct') %>%
adorn_totals() %>%
flextable() %>%
colformat_double(suffix = '%')
```
# TABLE 15: TABLE 15: HOUSEHOLD SIZE (WEIGHTED)
```{r table 15, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'hhsize_binned',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'hh_weight_2023',
label = FALSE)
```
# TABLE 16: HOUSEHOLD INCOME (WEIGHTED)
```{r table 16, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'hhincome_broad',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'hh_weight_2023',
label = TRUE,
value_labels = value_labels)
```
# TABLE 17: HOUSEHOLD VEHICLES (WEIGHTED)
```{r table 17, echo = FALSE, warning = FALSE}
# create a binned variable for num_vehicles
tables$hh[, vehicle_count_binned := ifelse(vehicle_count > 3, 3, vehicle_count)]
# Write new variable to variable list
variable_list = rbind(
variable_list,
data.table(
variable = 'vehicle_count_binned',
hh = 1,
person = 0,
vehicle = 0,
day = 0,
trip = 0,
location = 0,
description = 'binned number of vehicles',
data_type = 'integer/categorical',
logic = NA,
is_checkbox = 0,
shared_name = 'vehicle_count_binned'
)
)
make_reporting_table(summarize_var = 'vehicle_count_binned',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'hh_weight_2023',
label = FALSE)
```
# TABLE 18: PERSON AGE (WEIGHTED)
```{r table 18, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'age',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'person_weight',
label = TRUE,
value_labels = value_labels)
```
# TABLE 19: PERSON GENDER (WEIGHTED)
```{r table 19, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'gender',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'person_weight',
label = TRUE,
value_labels = value_labels)
```
# TABLE 20: PERSON RACE OR ETHNICITY
```{r table 20, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'race',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'person_weight',
label = TRUE,
value_labels = value_labels,
shared = TRUE)
```
# TABLE 21: PERSON STUDENT STATUS
```{r table 21, echo = FALSE, warning = FALSE}
# get values from codebook to classify
part_time = value_labels[
psrc_variable == 'student' &
str_detect(final_label, 'Part-time'),
psrc_value]
full_time = value_labels[
psrc_variable == 'student' &
str_detect(final_label, 'Full-time'),
psrc_value]
# make a binned student variable
tables$person[, student_binned := fcase(
student %in% full_time, 'Full-time',
student %in% part_time, 'Part-time',
default = 'Not a student'
)]
# add to variable list
variable_list = rbind(
variable_list,
data.table(
variable = 'student_binned',
hh = 0,
person = 1,
vehicle = 0,
day = 0,
trip = 0,
location = 0,
description = 'Student values binned',
data_type = 'categorical',
logic = NA,
is_checkbox = 0,
shared_name = 'student_binned'
)
)
make_reporting_table(summarize_var = 'student_binned',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'person_weight',
label = TRUE,
value_labels = value_labels)
```
# TABLE 22: PERSON EMPLOYMENT STATUS (AGE 18+)
```{r table 22, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'employment',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'person_weight',
label = TRUE,
value_labels = value_labels)
```
# TABLE 23: PERSON SEXUALITY
```{r table 23, echo = FALSE, warning = FALSE}
make_reporting_table(summarize_var = 'sexuality',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
wtname = 'person_weight',
label = TRUE,
value_labels = value_labels)
```
# Trip Rates--------------------------------------------------------------------
# TABLE 24: PERSON TRIP RATE, BY DAY OF WEEK
```{r table 24, echo = FALSE, warning = FALSE}
make_triprate_table(summarize_by = 'travel_dow',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
label = TRUE)
```
# TABLE 25: PERSON TRIP RATE, BY HOUSEHOLD INCOME
```{r table 25, echo = FALSE, warning = FALSE}
make_triprate_table(summarize_by = 'hhincome_broad',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
label = TRUE)
```
# TABLE 26: PERSON TRIP RATE, BY AGE GROUP
```{r table 26, echo = FALSE, warning = FALSE}
make_triprate_table(summarize_by = 'age',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
label = TRUE)
```
# TABLE 27: PERSON TRIP RATE, BY EMPLOYMENT STATUS (AGE 18+)
```{r table 27, echo = FALSE, warning = FALSE}
make_triprate_table(summarize_by = 'employment',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
label = TRUE)
```
# TABLE 28: PERSON TRIP RATE, BY TRAVEL MODE
```{r table 28, echo = FALSE, warning = FALSE}
make_triprate_table(summarize_by = 'mode_type',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
label = TRUE)
```
# TABLE 29: PERSON TRIP RATE, BY TRIP PURPOSE
```{r table 29, echo = FALSE, warning = FALSE}
make_triprate_table(summarize_by = 'dest_purpose_cat',
variable_list = variable_list,
tables = tables,
ids = ids,
wts = wts,
label = TRUE)
```
# Percent of trips by travel mode-----------------------------------------------
# TABLE 30: TRAVEL MODE, BY HOUSEHOLD INCOME (WEIGHTED)
```{r table 30, echo = FALSE, warning = FALSE}
prep = hts_prep_variable(summarize_var = 'mode_type',
summarize_by = 'hhincome_broad',
variables_dt = variable_list,
data = tables,
id_cols = ids,
wt_cols = wts,
missing_values = c("995", "NULL"))
output = hts_summary(prep$cat,
summarize_var = 'mode_type',
summarize_by = 'hhincome_broad',
summarize_vartype = 'categorical',
id_cols = ids,
wtname = 'trip_weight')
tab = output$summary$wtd
tab$pct = round(tab$prop * 100, 2)
# tab = tab[mode_type != 'NULL']
tab = factorize_df(
tab,
value_labels,
variable_colname = 'psrc_variable',
value_colname = 'psrc_value',
value_order_colname = 'psrc_value',
value_label_colname = "final_label")
dcast(tab, mode_type ~ hhincome_broad, value.var = 'pct') %>%
adorn_totals() %>%
flextable() %>%
colformat_double(suffix = '%')
```
# TABLE 31: TRAVEL MODE, BY AGE GROUP (WEIGHTED)
```{r table 31, echo = FALSE, warning = FALSE}
prep = hts_prep_variable(summarize_var = 'mode_type',
summarize_by = 'age',