revise function doc 4

.Rbuildignore

@@ -9,3 +9,4 @@
 ^pkgdown$
 ^doc$
 ^Meta$
+^data-raw$

R/age.R

@@ -38,7 +38,7 @@ compute_age_in_months <- function (surv_date, birth_date) {
 }
 
 #'
-#' Process age
+#' Wrangle age
 #'
 #' @description
 #' `process_age()` helps you to get the variable age in the format needed for

R/case_definitions.R

@@ -1,6 +1,5 @@
 #'
-#' Define if an observation is wasted on the basis of the criteria
-#' of WFHZ, absolute MUAC values and combined case-definition
+#' Wasting case-definition based on WFHZ, MFAZ, MUAC and Combined criteria
 #'
 #' @param df A data frame containing the required variables.
 #'
@@ -223,8 +222,8 @@ define_wasting <- function(df, zscore = NULL, muac = NULL, edema = NULL,
 }
 
 #'
-#' Classify wasting into severe or moderate wasting for use in SMART MUAC tool
-#' weighting approach
+#' Classify wasting into severe or moderate wasting to be used in the
+#' SMART MUAC tool weighting approach
 #'
 #' @param muac A numeric vector holding absolute MUAC values (in mm).
 #'

R/data.R

@@ -2,9 +2,9 @@
 #' A sample data of district level SMART surveys with location anonymised
 #'
 #' @description
-#' `anthro.01` is a two-stage cluster-based survey with probability of the
-#' selection of the clusters proportional to the size of the population. The
-#' survey employed the SMART methodology. Data was anonymised for confidentiality.
+#' `anthro.01` is a two-stage cluster-based survey with probability of selection
+#' of clusters proportional to the size of the population. The survey employed
+#' the SMART methodology.
 #'
 #' @format A tibble of 1,191 rows and 11 columns.
 #'
@@ -22,6 +22,8 @@
 #' | *edema* | Edema, "n" = no, "y" = yes |
 #' | *muac* | Mid-upper arm circumference (mm) |
 #'
+#' @source Anonymous
+#'
 #' @examples
 #' anthro.01
 #'
@@ -33,20 +35,10 @@
 #'
 #' @description
 #' `anthro.02` is about a household budget survey conducted in Mozambique in
-#' 2019/2020, known as IOF (*Inquérito ao Orçamento Familiar* in Portuguese).
-#' The data is publicly available [here](https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-data_access).
-#' The survey had a module on nutrition with anthropometric measurements taken
-#' from children age 0-59 months for weight-for-height and 6-59 months for MUAC.
-#'
-#' *IOF* is a two-stage cluster-based survey, representative at
-#' province level (admin 2), with probability of the selection of the clusters
-#' proportional to the size of the population. Its data collection spans for a
-#' period of 12 months, with anthropometric measurements
-#' taken during that period too. Read the [Bureau of Statistic's website on
-#' IOF](https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-sampling) for
-#' more details.
-#'
-#' `anthro.02` has already been wrangled using this package's utilities.
+#' 2019/2020, known as IOF (*Inquérito ao Orçamento Familiar* in Portuguese).*IOF*
+#' is a two-stage cluster-based survey, representative at province level (admin 2),
+#'  with probability of the selection of the clusters proportional to the size of
+#'  the population. Its data collection spans for a period of 12 months.
 #'
 #' @format A tibble of 2,267 rows and 14 columns.
 #'
@@ -67,6 +59,11 @@
 #' | *mfaz* | MUAC-for-age z-scores with 3 decimal places |
 #' | *flag_mfaz* | Flagged observations. 1=flagged, 0=not flagged |
 #'
+#' @source Mozambique National Institute of Statistics. The data is publicly
+#' available at <https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-data_access>.
+#' Data was wrangled using this package's wranglers. Details about survey design
+#' can be gotten from: <https://mozdata.ine.gov.mz/index.php/catalog/88#metadata-sampling>
+#'
 #' @examples
 #' anthro.02
 #'
@@ -79,12 +76,12 @@
 #' @description
 #' `anthro.03` contains survey data of four districts. Each district's dataset
 #' presents distinct data quality scenarios that requires tailored prevalence
-#' analysis approach. Two districts show a problematic WFHZ standard deviation
+#' analysis approach: two districts show a problematic WFHZ standard deviation
 #' whilst the remaining are all within range.
 #'
 #' This sample data demonstrates the use of prevalence functions on multi-area
-#' survey data, where variations in the standard deviation ratings exist.
-#' As a result, different analytical approaches are required for each area
+#' survey data, where there is variations in the standard deviation rating.
+#' As a result, different analyses approaches are required for each area
 #' to ensure accurate estimation.
 #'
 #' @format A tibble of 943 x 9.
@@ -101,6 +98,8 @@
 #' | *edema* | Edema, "n" = no, "y" = yes |
 #' | *muac* | Mid-upper arm circumference (mm) |
 #'
+#' @source Anonymous
+#'
 #' @examples
 #' anthro.03
 #'
@@ -113,12 +112,12 @@
 #' A sample data of a community-based sentinel site from an anonymized location
 #'
 #' @description
-#' `anthro.04` was generated from a community-based sentinel site survey
-#' conducted across three provinces. Each province's dataset presents distinct
-#' data quality scenarios, requiring tailored prevalence analysis.
-#' "Province 3" has problematic MFAZ standard deviation and age ratio tests.
+#' `anthro.04` was generated from a community-based sentinel site conducted
+#' across three provinces. Each province's dataset presents distinct
+#' data quality scenarios, requiring tailored prevalence analysis:
+#' "Province 3" has problematic MFAZ standard deviation and age ratio tests;
 #' "Province 2" shows a problematic age ratio but acceptable MFAZ standard
-#' deviation. Lastly, "Province 1" has both tests within acceptable ranges.
+#' deviation; lastly, "Province 1" has both tests within acceptable ranges.
 #'
 #' This sample data demonstrates the use of prevalence functions on multi-area
 #' survey data, where variations in the standard deviation ratings exist.
@@ -138,6 +137,8 @@
 #' | *mfaz* | MUAC-for-age z-scores with 3 decimal places |
 #' | *flag_mfaz* | Flagged observations. 1=flagged, 0=not flagged |
 #'
+#' @source Anonymous
+#'
 #' @examples
 #' anthro.04
 #'
@@ -159,6 +160,8 @@
 #' | *wfhz* | MUAC-for-age z-scores with 3 decimal places |
 #' | *flag_wfhz* | Flagged observations. 1=flagged, 0=not flagged |
 #'
+#' @source Anonymous
+#'
 #' @examples
 #' wfhz.01
 #'
@@ -178,6 +181,8 @@
 #' | *edema* | Edema, "n" = no, "y" = yes |
 #' | *muac* | Mid-upper arm circumference (mm) |
 #'
+#' @source Anonymous
+#'
 #' @examples
 #' mfaz.01
 #'
@@ -197,6 +202,8 @@
 #' | *mfaz* | MUAC-for-age z-scores with 3 decimal places |
 #' | *flag_mfaz* | Flagged observations. 1=flagged, 0=not flagged |
 #'
+#' @source Anonymous
+#'
 #' @examples
 #' mfaz.02
 #'

R/prevalence_combined.R

@@ -1,5 +1,5 @@
 #'
-#' Compute combined prevalence of acute malnutrition
+#' Compute combined prevalence of wasting
 #'
 #' @rdname combined_prevalence
 #'
@@ -66,14 +66,13 @@ compute_pps_based_combined_prevalence <- function(df,
 #' Compute prevalence of wasting on the basis of the combined case-definition
 #'
 #' @description
-#' `compute_combined_prevalence()` is a handy function for calculating the
-#' combined prevalence of wasting also in with the complex sample design
-#' properties inherent to surveys.
+#' `compute_combined_prevalence()` is a handy function for calculating the prevalence
+#'  of combined wasting in accordance with the complex sample design properties
+#'  inherent to surveys.
 #'
 #' @param df A data frame object returned by [process_muac_data()] and [process_wfhz_data()].
-#' Both wranglers need to be used to prepare data to be used
-#' `compute_combined_prevalence()`. The order of which comes first does not matter,
-#' however, since the muac data processor transforms MUAC values into centimeters, those
+#' Both wranglers need to be used sequentially. The order of use does not matter,
+#' however, since muac wrangler transforms MUAC values into centimeters, those
 #' need to be put back into millimeter. This can be achieved my using [recode_muac()] inside
 #' [dplyr::mutate()] or [base::transform()].
 #'
@@ -91,7 +90,7 @@ compute_pps_based_combined_prevalence <- function(df,
 #' @returns A table with the descriptive statistics about wasting.
 #'
 #' @details
-#' The concept of "combined flags" is introduced in this function. It consists of
+#' A concept of "combined flags" is introduced in this function. It consists of
 #' taking the `flag_wfhz` and `flag_mfaz` vectors, generated from the MUAC and
 #' WFHZ wranglers, and checking if any value in either vector is flagged. If flagged,
 #' the value is marked as a flag in the "cflags" vector; otherwise, it is not flagged

R/prevalence_muac.R

@@ -3,8 +3,8 @@
 #' basis of age ratio and standard deviation test results
 #'
 #' @description
-#' This is a helper function that gives instruction to the main prevalence
-#' analysis function on the analysis approach to follow in a given area of
+#' This is a helper function that gives instruction, to the main MUAC prevalence
+#' analysis function, on the analysis approach to follow in a given area of
 #' analysis on the basis of the quality of the age ratio test and the standard
 #' deviation.
 #'

R/prevalence_wfhz.R

@@ -1,5 +1,5 @@
 #'
-#' Compute the prevalence of wasting on the basis of WFHZ or MFAZ or MUAC
+#' Compute the prevalence of wasting on the basis of WFHZ, MFAZ and MUAC
 #'
 #' @description
 #' The prevalence is calculated in accordance with the complex sample design

data-raw/DATASET.R

@@ -0,0 +1,2 @@
+# Internal data ----
+usethis::use_data(wfhz.01, mfaz.01, mfaz.02, internal = TRUE, overwrite = TRUE)