Prepare for CRAN release

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: visdat
 Title: Preliminary Visualisation of Data
-Version: 0.6.0.9000
+Version: 0.6.1
 Authors@R: c(
   person(
     given = "Nicholas", 

NEWS.md

@@ -1,4 +1,4 @@
-# visdat 0.6.1 (2026/08/10) "Don't I Hold You, Wheat"
+# visdat 0.6.1 (2026/08/12) "Don't I Hold You, Wheat"
 
 ## New features
 
@@ -10,8 +10,8 @@
 
 ## Misc
 
-* Remove magrittr; Use `|>` internally over `%>%`
-* Depend on R 4.2.0
+* Remove magrittr; Use `|>` internally over `%>%` (#176)
+* Depend on R 4.2.0 (#176)
 
 # visdat 0.6.0 (2023/02/01) "Superman, Lazlo Bane"
 

README.Rmd

@@ -47,7 +47,7 @@ you visualise a dataframe and "get a look at the data" by displaying the
 variable classes in a dataframe as a plot with `vis_dat`, and getting a brief
 look into missing data patterns using `vis_miss`.
 
-`visdat` has 6 functions:
+`visdat` has 7 functions:
 
 - `vis_dat()` visualises a dataframe showing you what the classes of the columns
 are, and also displaying the missing data.
@@ -69,6 +69,7 @@ Unfortunately `missing.pattern.plot` is no longer in the `mi` package (as of
 - `vis_guess()` visualise the individual class of each value in your data
 - `vis_value()` visualise the value class of each cell in your data
 - `vis_binary()` visualise the occurrence of binary values in your data
+- `vis_histogram()` visualises a histogram of each numeric variable in your data
 
 You can read more about visdat in the vignette, ["using visdat"]https://docs.ropensci.org/visdat/articles/using_visdat.html).
 
@@ -314,6 +315,17 @@ vis_dat(messy_df)
 So here we see that there are many different kinds of data in your dataframe. As
 an analyst this might be a depressing finding. We can see this comparison above.
 
+
+## `vis_histogram()`
+
+`vis_histogram()` visualises the distribution of every numeric column in a 
+dataframe and displays it using a faceted ggplot object.
+
+```{r vis-histogram}
+vis_histogram(airquality, bins = 30)
+```
+
+
 # Thank yous
 
 Thank you to Ivan Hanigan who [first

README.md

@@ -43,7 +43,7 @@ by displaying the variable classes in a dataframe as a plot with
 `vis_dat`, and getting a brief look into missing data patterns using
 `vis_miss`.
 
-`visdat` has 6 functions:
+`visdat` has 7 functions:
 
 - `vis_dat()` visualises a dataframe showing you what the classes of the
   columns are, and also displaying the missing data.
@@ -70,6 +70,9 @@ by displaying the variable classes in a dataframe as a plot with
 
 - `vis_binary()` visualise the occurrence of binary values in your data
 
+- `vis_histogram()` visualises a histogram of each numeric variable in
+  your data
+
 You can read more about visdat in the vignette, \[“using
 visdat”\]<https://docs.ropensci.org/visdat/articles/using_visdat.html>).
 
@@ -348,6 +351,17 @@ So here we see that there are many different kinds of data in your
 dataframe. As an analyst this might be a depressing finding. We can see
 this comparison above.
 
+## `vis_histogram()`
+
+`vis_histogram()` visualises the distribution of every numeric column in
+a dataframe and displays it using a faceted ggplot object.
+
+``` r
+vis_histogram(airquality, bins = 30)
+```
+
+![](man/figures/README-vis-histogram-1.png)<!-- -->
+
 # Thank yous
 
 Thank you to Ivan Hanigan who [first

_pkgdown.yml

@@ -53,6 +53,11 @@ reference:
       Potentially reveal hidden patterns by seeing scaled values
     contents:
       - vis_value
+  - title: "Display the distribution of numeric variables"
+    desc: >
+      Quickly explore distributions, creating a histogram for each variable
+    contents:
+      - vis_histogram
   - title: "Example data provided with visdat"
     desc: >
       Data is provided for exploring different functions in visdat

cran-comments.md

@@ -1,5 +1,5 @@
 ## Test environments
-* local OS X install, R 4.2.2
+* local OS X install, R 4.6.1
 * github actions testing for devel, release, and ubuntu, windows, and macOSX
 * win-builder (devel and release)
 
@@ -10,7 +10,7 @@ There were no ERRORs or WARNINGs or NOTEs
 
 ## revdepcheck results
 
-We checked 3 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 6 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages

revdep/README.md

@@ -1,26 +1,25 @@
 # Platform
 
-|field    |value                                                                                       |
-|:--------|:-------------------------------------------------------------------------------------------|
-|version  |R version 4.2.2 (2022-10-31)                                                                |
-|os       |macOS Monterey 12.3.1                                                                       |
-|system   |aarch64, darwin20                                                                           |
-|ui       |RStudio                                                                                     |
-|language |(EN)                                                                                        |
-|collate  |en_US.UTF-8                                                                                 |
-|ctype    |en_US.UTF-8                                                                                 |
-|tz       |Australia/Hobart                                                                            |
-|date     |2023-02-01                                                                                  |
-|rstudio  |2022.12.0+353 Elsbeth Geranium (desktop)                                                    |
-|pandoc   |2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) |
+|field    |value                                     |
+|:--------|:-----------------------------------------|
+|version  |R version 4.6.1 (2026-06-24)              |
+|os       |macOS Tahoe 26.5.2                        |
+|system   |aarch64, darwin23                         |
+|ui       |RStudio                                   |
+|language |(EN)                                      |
+|collate  |en_US.UTF-8                               |
+|ctype    |en_US.UTF-8                               |
+|tz       |Australia/Hobart                          |
+|date     |2026-08-12                                |
+|rstudio  |2026.07.1+147 Pacific Dogwood (desktop)   |
+|pandoc   |3.10 @ /opt/homebrew/bin/ (via rmarkdown) |
+|quarto   |1.9.36 @ /usr/local/bin/quarto            |
 
 # Dependencies
 
 |package |old   |new   |Δ  |
 |:-------|:-----|:-----|:--|
-|visdat  |0.5.3 |0.6.0 |*  |
-|forcats |NA    |1.0.0 |*  |
-|utf8    |NA    |1.2.3 |*  |
+|visdat  |0.6.0 |0.6.1 |*  |
 
 # Revdeps
 

revdep/cran.md

@@ -1,6 +1,6 @@
 ## revdepcheck results
 
-We checked 3 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 6 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages

vignettes/using_visdat.Rmd

@@ -337,6 +337,15 @@ Here, you might just assume your data is weird because it's all factors - or wor
 
 At the moment `vis_guess` is very slow. Please take this into consideration when you are using it on data with more than 1000 rows. We're looking into ways of making it faster, potentially using methods from the `parallel` package, or extending the c++ code from `readr:::collectorGuess`.
 
+## `vis_histogram()`
+
+`vis_histogram()` visualises the distribution of every numeric column in a 
+dataframe and displays it using a faceted ggplot object.
+
+```{r vis-histogram}
+vis_histogram(airquality, bins = 30)
+```
+
 # Interactivity
 
 You can make the plots in visdat by wrapping them in `plotly::ggplotly`: