Dev to main

.Rbuildignore

@@ -1,4 +1,3 @@
-^ipccheckr\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$
 ^README\.Rmd$
@@ -14,6 +13,9 @@
 ^README_files$
 ^mwana\.Rproj$
 ^vignettes/plausibility_files$
+ˆvingettes/plausibility.html$
 ^vignettes/prevalence_files$
+ˆvignettes/prevalence.html$
 ^vignettes/sample_size_files$
+ˆvignettes/sample_size.html$
 ^CODE_OF_CONDUCT\.md$

.github/workflows/R-CMD-check.yaml

@@ -2,9 +2,9 @@
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches: [main, master]
+    branches: [main, dev]
   pull_request:
-    branches: [main, master]
+    branches: [main, dev]
 
 name: R-CMD-check
 

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
-Package: ipccheckr
+Package: mwana
 Title: Utilities for Analysing Children's Nutritional Status 
-Version: 0.0.0.9000
+Version: 0.1.0.9000
 Authors@R: c(
     person("Tomás", "Zaba", , "tomas.zaba@outlook.com", role = c("aut", "cre", "cph"),
            comment = c(ORCID = "0000-0002-7079-3574")),
@@ -12,7 +12,7 @@ Description: A streamlined and comprehensive implementation of the Standardized
   Monitoring and Assessment of Relief and Transition (SMART) Methodology
   <https://smartmethodology.org/> guidelines for data quality checks and 
   prevalence estimation, with enhanced programmable process particularly when 
-  handling large multiple datasets.
+  handling multiple area datasets.
 License: GPL (>= 3)
 URL: https://github.com/nutriverse/mwana, https://nutriverse.io/mwana
 Imports: 
@@ -24,7 +24,8 @@ Imports:
     srvyr,
     stats,
     zscorer,
-    tibble
+    tibble,
+    methods
 Suggests:
     knitr,
     rmarkdown,

NAMESPACE

@@ -1,24 +1,24 @@
 # Generated by roxygen2: do not edit by hand
 
-export(age_ratio_test)
-export(check_plausibility_mfaz)
-export(check_plausibility_muac)
-export(check_plausibility_wfhz)
-export(check_sample_size)
-export(classify_overall_quality)
 export(compute_combined_prevalence)
 export(compute_muac_prevalence)
-export(compute_quality_score)
 export(compute_wfhz_prevalence)
 export(define_wasting)
 export(flag_outliers)
-export(generate_pretty_table_mfaz)
-export(generate_pretty_table_muac)
-export(generate_pretty_table_wfhz)
-export(process_age)
-export(process_muac_data)
-export(process_wfhz_data)
+export(get_age_months)
+export(mw_check_ipcamn_ssreq)
+export(mw_neat_output_mfaz)
+export(mw_neat_output_muac)
+export(mw_neat_output_wfhz)
+export(mw_plausibility_check_mfaz)
+export(mw_plausibility_check_muac)
+export(mw_plausibility_check_wfhz)
+export(mw_stattest_ageratio)
+export(mw_wrangle_age)
+export(mw_wrangle_muac)
+export(mw_wrangle_wfhz)
 export(recode_muac)
+export(remove_flags)
 importFrom(dplyr,across)
 importFrom(dplyr,case_when)
 importFrom(dplyr,ends_with)
@@ -31,12 +31,15 @@ importFrom(dplyr,n_distinct)
 importFrom(dplyr,rename)
 importFrom(dplyr,summarise)
 importFrom(lubridate,ymd)
+importFrom(methods,is)
 importFrom(nipnTK,ageRatioTest)
 importFrom(nipnTK,digitPreference)
 importFrom(nipnTK,greensIndex)
 importFrom(nipnTK,sexRatioTest)
 importFrom(nipnTK,skewKurt)
 importFrom(rlang,.data)
+importFrom(rlang,enquo)
+importFrom(rlang,eval_tidy)
 importFrom(rlang,sym)
 importFrom(scales,label_percent)
 importFrom(scales,label_pvalue)
@@ -47,4 +50,5 @@ importFrom(stats,pnorm)
 importFrom(stats,prop.test)
 importFrom(stats,sd)
 importFrom(stats,setNames)
+importFrom(tibble,as_tibble)
 importFrom(zscorer,addWGSR)

NEWS.md

@@ -1,3 +1,3 @@
-# ipccheckr v0.0.0.9000 (development version)
+# mwana v0.0.0.9000 (development version)
 
 * Initial pre-release version for alpha-testing.

R/ipc_amn_check.R

@@ -0,0 +1,72 @@
+#'
+#' Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met
+#'
+#' @description
+#' Evidence on the prevalence of acute malnutrition used in the IPC AMN
+#' can come from different sources: surveys, screenings or community-based
+#' surveillance system. The IPC set minimum sample size requirements
+#' for each source. This function helps in verifying whether the requirements
+#' were met or not depending on the source.
+#'
+#' @param df A dataset object of class `data.frame` to check.
+#'
+#' @param cluster A vector of class `integer` or `character` of unique cluster or
+#' screening or sentinel site IDs. If a `character` vector, ensure that names are
+#' correct and each name represents one location for accurate counts. If the class
+#' does not match the above expected type, the function will stop execution and
+#' return an error message indicating the type of mismatch.
+#'
+#' @param .source The source of evidence. A choice between "survey" for
+#' representative survey data at the area of analysis; "screening" for
+#' screening data; "ssite" for community-based sentinel site data.
+#'
+#' @returns A summary table of class `data.frame`, of length 3 and width 1, for
+#' the check results. `n_clusters` is for the total number of unique clusters or
+#' screening or site IDs; `n_obs` for the correspondent total number of children
+#' in the dataset; and `meet_ipc` for whether the IPC AMN requirements were met.
+#'
+#' @references
+#' IPC Global Partners. 2021. *Integrated Food Security Phase Classification*
+#' *Technical Manual Version 3.1.Evidence and Standards for Better Food Security*
+#' *and Nutrition Decisions*. Rome. Available at:
+#' <https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/>.
+#'
+#' @examples
+#' mw_check_ipcamn_ssreq(
+#'   df = anthro.01,
+#'   cluster = cluster,
+#'   .source = "survey"
+#' )
+#'
+#' @export
+#'
+mw_check_ipcamn_ssreq <- function(df,
+                                  cluster,
+                                  .source = c("survey", "screening", "ssite")) {
+  ## Difuse and evaluate arguments ----
+  cluster <- eval_tidy(enquo(cluster), df)
+
+  ## Enforce the options in `.source` ----
+  .source <- match.arg(.source)
+
+  ## Enforce the class of `cluster` ----
+  if (!(class(cluster) %in% c("integer", "character"))) {
+    stop(
+      "`cluster` must be of class `integer` or `character`; not ", shQuote(class(cluster)), ". Please try again."
+    )
+  }
+
+  ## Summarize ----
+  df <- df |>
+    summarise(
+      n_clusters = n_distinct({{ cluster }}),
+      n_obs = n(),
+      meet_ipc = case_when(
+        .source == "survey" & n_clusters >= 25 ~ "yes",
+        .source == "screening" & n_clusters >= 3 & n_obs >= 600 ~ "yes",
+        .source == "ssite" & n_clusters >= 5 & n_obs >= 200 ~ "yes",
+        .default = "no"
+      )
+    )
+  as_tibble(df)
+}

R/ipccheckr-package.R → R/mwana-package.R

@@ -3,13 +3,17 @@
 
 ## usethis namespace: start
 #' @importFrom dplyr across case_when group_by mutate n n_distinct rename summarise
-#' @importFrom dplyr ends_with everything filter
+#' @importFrom dplyr ends_with everything filter mutate
+#' @importFrom dplyr group_by
+#' @importFrom dplyr summarise
 #' @importFrom lubridate ymd
+#' @importFrom methods is
 #' @importFrom nipnTK ageRatioTest digitPreference sexRatioTest skewKurt greensIndex
-#' @importFrom rlang .data sym
+#' @importFrom rlang .data sym enquo eval_tidy
 #' @importFrom scales label_percent label_pvalue
 #' @importFrom srvyr as_survey_design survey_mean
 #' @importFrom stats na.omit prop.test sd pnorm setNames
+#' @importFrom tibble as_tibble
 #' @importFrom zscorer addWGSR
 ## usethis namespace: end
 NULL