nutriverse · ernestguevarra · Dec 9, 2024 · Dec 10, 2024 · Dec 10, 2024 · Dec 11, 2024
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -19,3 +19,4 @@
 ^vignettes/sample_size_files$
 ˆvignettes/sample_size.html$
 ^CODE_OF_CONDUCT\.md$
+^vignettes/\.quarto$
diff --git a/.github/workflows/netlify.yaml b/.github/workflows/netlify.yaml
@@ -0,0 +1,53 @@
+on:
+  pull_request:
+    branches: [main, master]
+
+name: pkgdown-pr
+
+jobs:
+  netlify:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-tinytex@v2
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Install package
+        run: R CMD INSTALL .
+
+      - name: Create website
+        run: |
+          pkgdown::build_site()
+        shell: Rscript {0}
+
+      - name: Create index file
+        run: |
+          echo '<!DOCTYPE html><html><head><meta http-equiv="refresh" content="0;URL=/dev/index.html" />  <script language="javascript"> window.location.replace('/dev/index.html')</script></head></html>' > ./docs/index.html
+
+      - name: Deploy to Netlify
+        uses: nwtgck/[email protected]
+        with:
+          publish-dir: './docs'
+          production-branch: main
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          deploy-message:
+            'Deploy from GHA: ${{ github.event.pull_request.title || github.event.head_commit.message }} (${{ github.sha }})'
+          # these default to 'true'
+          enable-commit-comment: false
+          enable-github-deployment: false
+        env:
+          NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
+          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
@@ -15,7 +15,6 @@ jobs:
     runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-      CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
     steps:
       - uses: actions/checkout@v4

diff --git a/.gitignore b/.gitignore
@@ -15,8 +15,10 @@ docs
 /vignettes/plausibility_files/
 /vignettes/prevalence_files/
 /vignettes/sample_size_files/
+/vignettes/.quarto/
 
 /.quarto/
 
 
-data-raw/ENA_generated_zscores.csv
+data-raw/ENA_generated_zscores.csv
+/doc/
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,7 +2,7 @@ Type: Package
 Package: mwana
 Title: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of
     Wasting in R
-Version: 0.2.1
+Version: 0.2.1.9000
 Authors@R: c(
     person("Tomás", "Zaba", , "[email protected]", 
            role = c("aut", "cre", "cph"),

diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R
@@ -41,7 +41,10 @@ complex_survey_estimates_combined <- function(df,
     )
   }
 
-  ## Create survey object ----
+  ## Filter out flags ----
+  df <- dplyr::filter(.data = df, .data$cflags == 0)
+
+  ## Create a survey object for a weighted analysis ----
   if (!quo_is_null(wt)) {
     srvy <- srvyr::as_survey_design(
       .data = df,
@@ -51,6 +54,7 @@ complex_survey_estimates_combined <- function(df,
       weights = !!wt
     )
   } else {
+    ## Create a survey object for an unweighted analysis ----
     srvy <- srvyr::as_survey_design(
       .data = df,
       ids = .data$cluster,
@@ -59,13 +63,12 @@ complex_survey_estimates_combined <- function(df,
     )
   }
   ## Summarise prevalence ----
-  p <- dplyr::group_by(.data = srvy, {{ .by }}) |>
-    dplyr::filter(.data$cflags == 0) |>
-    dplyr::summarise(
-      dplyr::across(
+  p <- srvyr::group_by(.data = srvy, {{ .by }}) |>
+    srvyr::summarise(
+      srvyr::across(
         .data$cgam:.data$cmam,
         list(
-          n = \(.) sum(., na.rm = TRUE),
+          n = ~sum(.x, na.rm = TRUE),
           p = \(.) srvyr::survey_mean(
             .,
             vartype = "ci",
@@ -87,44 +90,44 @@ complex_survey_estimates_combined <- function(df,
 #'
 #' @description
 #' Estimate the prevalence of wasting based on the combined case-definition of
-#' weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows 
-#' users to estimate prevalence in accordance with complex sample design 
-#' properties such as accounting for survey sample weights when needed or 
-#' applicable. The quality of the data is first evaluated by calculating and 
-#' rating the standard deviation of WFHZ and MFAZ and the p-value of the age 
-#' ratio test. Prevalence is calculated only when all tests are rated as not 
-#' problematic. If any of the tests rate as problematic, no estimation is done 
-#' and an NA value is returned. Outliers are detected in both WFHZ and MFAZ 
-#' datasets based on SMART flagging criteria. Identified outliers are then 
+#' weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows
+#' users to estimate prevalence in accordance with complex sample design
+#' properties such as accounting for survey sample weights when needed or
+#' applicable. The quality of the data is first evaluated by calculating and
+#' rating the standard deviation of WFHZ and MFAZ and the p-value of the age
+#' ratio test. Prevalence is calculated only when all tests are rated as not
+#' problematic. If any of the tests rate as problematic, no estimation is done
+#' and an NA value is returned. Outliers are detected in both WFHZ and MFAZ
+#' datasets based on SMART flagging criteria. Identified outliers are then
 #' excluded before prevalence estimation is performed.
 #'
 #' @param df A `tibble` object produced by sequential application of the
-#' [mw_wrangle_wfhz()] and [mw_wrangle_muac()]. Note that MUAC values in `df` 
+#' [mw_wrangle_wfhz()] and [mw_wrangle_muac()]. Note that MUAC values in `df`
 #' must be in millimeters unit after using [mw_wrangle_muac()]. Also, `df`
 #' must have a variable called `cluster` which contains the primary sampling
 #' unit identifiers.
 #'
-#' @param wt A vector of class `double` of the survey sampling weights. Default 
-#' is NULL which assumes a self-weighted survey as is the case for a survey 
+#' @param wt A vector of class `double` of the survey sampling weights. Default
+#' is NULL which assumes a self-weighted survey as is the case for a survey
 #' sample selected proportional to population size (i.e., SMART survey sample).
 #' Otherwise, a weighted analysis is implemented.
 #'
 #' @param edema A `character` vector for presence of nutritional edema coded as
-#' "y" for presence of nutritional edema and "n" for absence of nutritional 
+#' "y" for presence of nutritional edema and "n" for absence of nutritional
 #' edema. Default is NULL.
 #'
 #' @param .by A `character` or `numeric` vector of the geographical areas
 #' or identifiers for where the data was collected and for which the analysis
 #' should be summarised for.
 #'
-#' @returns A summary `tibble` for the descriptive statistics about combined 
+#' @returns A summary `tibble` for the descriptive statistics about combined
 #' wasting.
 #'
 #' @details
 #' A concept of *combined flags* is introduced in this function. Any observation
 #' that is flagged for either `flag_wfhz` or `flag_mfaz` is flagged under a new
-#' variable named `cflags` added to `df`. This ensures that all flagged 
-#' observations from both WFHZ and MFAZ data are excluded from the prevalence 
+#' variable named `cflags` added to `df`. This ensures that all flagged
+#' observations from both WFHZ and MFAZ data are excluded from the prevalence
 #' analysis.
 #'
 #' | **flag_wfhz** | **flag_mfaz** | **cflags** |
@@ -174,7 +177,7 @@ mw_estimate_prevalence_combined <- function(df,
       .data = df,
       std_wfhz = rate_std(
         stats::sd(
-          remove_flags(as.numeric(.data$wfhz), "zscores"), 
+          remove_flags(as.numeric(.data$wfhz), "zscores"),
           na.rm = TRUE
         )
       ),
@@ -183,7 +186,7 @@ mw_estimate_prevalence_combined <- function(df,
       ),
       std_mfaz = rate_std(
         stats::sd(
-          remove_flags(as.numeric(.data$mfaz), "zscores"), 
+          remove_flags(as.numeric(.data$mfaz), "zscores"),
           na.rm = TRUE)
       ),
       muac_analysis_path = set_analysis_path(.data$age_ratio, .data$std_mfaz),
@@ -195,7 +198,7 @@ mw_estimate_prevalence_combined <- function(df,
       .data = df,
       std_wfhz = rate_std(
         stats::sd(
-          remove_flags(as.numeric(.data$wfhz), "zscores"), 
+          remove_flags(as.numeric(.data$wfhz), "zscores"),
           na.rm = TRUE
         )
       ),
@@ -204,7 +207,7 @@ mw_estimate_prevalence_combined <- function(df,
       ),
       std_mfaz = rate_std(
         stats::sd(
-          remove_flags(as.numeric(.data$mfaz), "zscores"), 
+          remove_flags(as.numeric(.data$mfaz), "zscores"),
           na.rm = TRUE
         )
       ),

diff --git a/R/prev_wasting_mfaz.R b/R/prev_wasting_mfaz.R
@@ -26,7 +26,10 @@ complex_survey_estimates_mfaz <- function(df,
     )
   }
 
-  ## Create a survey object ----
+  ## Filter out flags ----
+  df <- dplyr::filter(.data = df, .data$flag_mfaz == 0)
+
+  ## Create a survey object for a weighted analysis ----
   if (!is.null(wt)) {
     srvy <- srvyr::as_survey_design(
       .data = df,
@@ -36,6 +39,7 @@ complex_survey_estimates_mfaz <- function(df,
       weights = {{ wt }}
     )
   } else {
+    ## Create a survey object for an unweighted analysis ----
     srvy <- srvyr::as_survey_design(
       .data = df,
       ids = .data$cluster,
@@ -45,10 +49,9 @@ complex_survey_estimates_mfaz <- function(df,
   }
 
   ## Summarise prevalence ----
-  p <- dplyr::group_by(.data = srvy, {{ .by }}) |>
-    dplyr::filter(.data$flag_mfaz == 0) |>
-    dplyr::summarise(
-      dplyr::across(
+  p <- srvyr::group_by(.data = srvy, {{ .by }}) |>
+    srvyr::summarise(
+      srvyr::across(
         .data$gam:.data$mam,
         list(
           n = \(.) sum(., na.rm = TRUE),
@@ -72,29 +75,29 @@ complex_survey_estimates_mfaz <- function(df,
 #' Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)
 #'
 #' @description
-#' Calculate the prevalence estimates of wasting based on z-scores of 
-#' MUAC-for-age and/or bilateral edema. The function allows users to estimate 
-#' prevalence in accordance with complex sample design properties such as 
-#' accounting for survey sample weights when needed or applicable. The quality 
-#' of the data is first evaluated by calculating and rating the standard 
-#' deviation of MFAZ. Standard approach to prevalence estimation is calculated 
-#' only when the standard deviation of MFAZ is rated as not problematic. If 
+#' Calculate the prevalence estimates of wasting based on z-scores of
+#' MUAC-for-age and/or bilateral edema. The function allows users to estimate
+#' prevalence in accordance with complex sample design properties such as
+#' accounting for survey sample weights when needed or applicable. The quality
+#' of the data is first evaluated by calculating and rating the standard
+#' deviation of MFAZ. Standard approach to prevalence estimation is calculated
+#' only when the standard deviation of MFAZ is rated as not problematic. If
 #' the standard deviation is problematic, prevalence is estimated using the
-#' PROBIT estimator. Outliers are detected based on SMART flagging criteria. 
-#' Identified outliers are then excluded before prevalence estimation is 
+#' PROBIT estimator. Outliers are detected based on SMART flagging criteria.
+#' Identified outliers are then excluded before prevalence estimation is
 #' performed.
 #'
 #' @param df A `data.frame` object that has been produced by the
 #' [mw_wrangle_age()] and [mw_wrangle_muac()] functions. The `df` should have a
 #' variable named `cluster` for the primary sampling unit identifiers.
 #'
-#' @param wt A vector of class `double` of the survey sampling weights. Default 
-#' is NULL which assumes a self-weighted survey as is the case for a survey 
+#' @param wt A vector of class `double` of the survey sampling weights. Default
+#' is NULL which assumes a self-weighted survey as is the case for a survey
 #' sample selected proportional to population size (i.e., SMART survey sample).
 #' Otherwise, a weighted analysis is implemented.
 #'
 #' @param edema A `character` vector for presence of nutritional edema coded as
-#' "y" for presence of nutritional edema and "n" for absence of nutritional 
+#' "y" for presence of nutritional edema and "n" for absence of nutritional
 #' edema. Default is NULL.
 #'
 #' @param .by A `character` or `numeric` vector of the geographical areas