Merge branch 'devel'

.Rbuildignore

@@ -23,3 +23,5 @@ revdep
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
+^doc$
+^Meta$

R/clean.R

@@ -191,7 +191,7 @@ data_rm_negatives <- function(data, cols) {
     cases <- sum(dplyr::select(data, {{ cols }}) < 0, na.rm=TRUE)
 
     if (cases > 0) {
-      data |>
+      data <- data |>
         labs_store() |>
         dplyr::mutate(dplyr::across({{ cols }}, ~ ifelse(. < 0, NA, .))) |>
         labs_restore()

README.Rmd

@@ -47,7 +47,7 @@ tab_metrics(data, starts_with("cg_adoption_social"))
 ```
 
 See further [examples in the introduction
-vignette](vignettes/introduction.Rmd).
+vignette](articles/introduction.html).
 
 Don't miss the template feature: Within RStudio, create a new Markdown
 document, select `From template`, choose and finally knit the **volkeR

README.md

@@ -30,7 +30,7 @@ High-level functions for tabulating, charting and reporting survey data.
     tab_metrics(data, starts_with("cg_adoption_social"))
 
 See further [examples in the introduction
-vignette](vignettes/introduction.Rmd).
+vignette](articles/introduction.html).
 
 Don’t miss the template feature: Within RStudio, create a new Markdown
 document, select `From template`, choose and finally knit the **volkeR

_pkgdown.yml

@@ -15,6 +15,9 @@ navbar:
     introduction:
       text: Introduction
       href: articles/introduction.html
+vignettes:
+- title: "Introduction"
+  name: "introduction"
 
 reference:
 - title: Analysis

tests/testthat/_snaps/clean.md

@@ -1,14 +1,26 @@
-# Negatives are cleaned
+# Residual negatives values are removes
 
     Code
-      data_clean(tibble::tibble(var1 = c(1, 2, -9)))
+      data_clean(tibble::tibble(var1 = c(1, 2, -1, -9)))
     Output
-      # A tibble: 3 x 1
+      # A tibble: 4 x 1
+         var1
+        <dbl>
+      1     1
+      2     2
+      3    -1
+      4    NA
+
+# All negatives are removed
+
+    Code
+      data_rm_negatives(tibble::tibble(var1 = c(1, 2, -1, -9)), var1)
+    Output
+      # A tibble: 2 x 1
          var1
         <dbl>
       1     1
       2     2
-      3    NA
 
 # Negatives are kept
 

tests/testthat/test-clean.R

@@ -8,15 +8,24 @@ library(volker)
 # Load and recode data
 data <- volker::chatgpt
 
-# Remove negative values
-test_that("Negatives are cleaned", {
 
-  tibble::tibble(var1 = c(1,2,-9)) |>
+# Remove residual negative values
+test_that("Residual negatives values are removes", {
+
+  tibble::tibble(var1 = c(1,2,-1,-9)) |>
     data_clean() |>
     expect_snapshot(cran= TRUE)
 
 })
 
+# Remove all negative values
+test_that("All negatives are removed", {
+
+  tibble::tibble(var1 = c(1,2,-1,-9)) |>
+    data_rm_negatives(var1) |>
+    expect_snapshot(cran= TRUE)
+})
+
 
 # Keep negative values
 test_that("Negatives are kept", {

vignettes/introduction.Rmd

@@ -259,9 +259,17 @@ ds %>%
 ```
 
 Alternatively, save the result of `codebook(ds)` to an Excel file,
-change the labels and then call `labs_apply()` with your new codebook.
+change the labels manually and then call `labs_apply()` with your new codebook.
 
+```{r, eval = FALSE}
 
+codes <- codebook(ds)
+write_xlsx(codes,"codebook.xlsx")
+
+codes <- read_xlsx("codebook-new.xlsx")
+ds <- labs_apply(codebook)
+
+```
 
 # Index calculation for item batteries