Merge branch 'main' into document

DESCRIPTION

@@ -7,7 +7,8 @@ Authors@R: c(
     person("Chris", "Haid", , "[email protected]", role = "ctb"),
     person("Ryan", "Knight", , "[email protected]", role = "ctb"),
     person("Malte", "Grosser", , "[email protected]", role = "ctb"),
-    person("Jonathan", "Zadra", , "[email protected]", role = "ctb")
+    person("Jonathan", "Zadra", , "[email protected]", role = "ctb"),
+    person("Olivier", "Roy", role = "ctb")
   )
 Description: The main janitor functions can: perfectly format data.frame
     column names; provide quick counts of variable combinations (i.e.,

R/tabyl.R

@@ -223,6 +223,7 @@ tabyl_2way <- function(dat, var1, var2, show_na = TRUE, show_missing_levels = TR
       names_from = !!var2,
       values_from = "tabyl_2way_n",
       values_fn = ~ dplyr::coalesce(.x, 0L),
+      values_fill = 0L,
       names_sort = TRUE
     )
   if ("emptystring_" %in% names(result)) {

README.Rmd

@@ -14,11 +14,13 @@ knitr::opts_chunk$set(
 options(width = 110)
 ```
 
+# janitor <img src="man/figures/logo_small.png" align="right" />
+
+
 > Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.
 > 
 > -- ["For Big-Data Scientists, 'Janitor Work' Is Key Hurdle to Insight"](https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html) *(New York Times, 2014)*
 
-# janitor <img src="man/figures/logo_small.png" align="right" />
 
 ***********************
 
@@ -41,25 +43,27 @@ The main janitor functions:
 
 The tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel.
 
-janitor is a [#tidyverse]( https://CRAN.R-project.org/package=tidyverse/vignettes/manifesto.html)-oriented package.  Specifically, it plays nicely with the `%>%` pipe and is optimized for cleaning data brought in with the [readr](https://github.com/tidyverse/readr) and [readxl](https://github.com/tidyverse/readxl) packages.
+janitor is a [#tidyverse]( https://cran.r-project.org/package=tidyverse/vignettes/manifesto.html)-oriented package.  Specifically, it plays nicely with the `%>%` pipe and is optimized for cleaning data brought in with the [readr](https://github.com/tidyverse/readr) and [readxl](https://github.com/tidyverse/readxl) packages.
 
 
-### Installation
+## <i class="fa fa-cog" aria-hidden="true"></i> Installation
 
 You can install:
 
 * the most recent officially-released version from CRAN with
 
-    ```R
-    install.packages("janitor")
-    ````
+```r
+install.packages("janitor")
+```
 
 * the latest development version from GitHub with
 
-    ```R
-    install.packages("devtools")
-    devtools::install_github("sfirke/janitor")
-    ```
+```R
+# install.packages("remotes")
+remotes::install_github("sfirke/janitor")
+# or from r-universe
+install.packages("janitor", repos = c("https://sfirke.r-universe.dev", "https://cloud.r-project.org"))
+```
 
 ## Using janitor
 
@@ -143,11 +147,11 @@ Yes, some teachers appear twice.  We ought to address this before counting emplo
 A variable (or combinations of two or three variables) can be tabulated with `tabyl()`.  The resulting data.frame can be tweaked and formatted
 with the suite of `adorn_` functions for quick analysis and printing of pretty results in a report.  `adorn_` functions can be helpful with non-tabyls, too.
 
-#### tabyl()
+#### `tabyl()`
 
 Like `table()`, but pipe-able, data.frame-based, and fully featured.
 
-`tabyl` can be called two ways:
+`tabyl()` can be called two ways:
 
 * On a vector, when tabulating a single variable: `tabyl(roster$subject)`
 * On a data.frame, specifying 1, 2, or 3 variable names to tabulate: `roster %>% tabyl(subject, employee_status)`.
@@ -172,7 +176,7 @@ roster %>%
   tabyl(full_time, subject, employee_status, show_missing_levels = FALSE)
 ```
 
-##### Adorning tabyls
+#### Adorning tabyls
 The `adorn_` functions dress up the results of these tabulation calls for fast, basic reporting.  Here are some of the functions that augment a summary table for reporting:
 
 ```{r}
@@ -189,7 +193,7 @@ Pipe that right into `knitr::kable()` in your RMarkdown report.
 
 These modular adornments can be layered to reduce R's deficit against Excel and SPSS when it comes to quick, informative counts.  Learn more about `tabyl()` and the `adorn_` functions from the [tabyls vignette](https://sfirke.github.io/janitor/articles/tabyls.html).
 
-## Contact me
+## <i class="fa fa-bullhorn" aria-hidden="true"></i> Contact me
 
 You are welcome to:
 

README.md

@@ -1,6 +1,8 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
+# janitor <img src="man/figures/logo_small.png" align="right" />
+
 > Data scientists, according to interviews and expert estimates, spend
 > from 50 percent to 80 percent of their time mired in this more mundane
 > labor of collecting and preparing unruly digital data, before it can
@@ -10,8 +12,6 @@
 > Insight”](https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html)
 > *(New York Times, 2014)*
 
-# janitor <img src="man/figures/logo_small.png" align="right" />
-
 ------------------------------------------------------------------------
 
 <!-- badges: start -->
@@ -42,28 +42,30 @@ The tabulate-and-report functions approximate popular features of SPSS
 and Microsoft Excel.
 
 janitor is a
-[\#tidyverse](https://CRAN.R-project.org/package=tidyverse/vignettes/manifesto.html)-oriented
+[\#tidyverse](https://cran.r-project.org/package=tidyverse/vignettes/manifesto.html)-oriented
 package. Specifically, it plays nicely with the `%>%` pipe and is
 optimized for cleaning data brought in with the
 [readr](https://github.com/tidyverse/readr) and
 [readxl](https://github.com/tidyverse/readxl) packages.
 
-### Installation
+## <i class="fa fa-cog" aria-hidden="true"></i> Installation
 
 You can install:
 
 - the most recent officially-released version from CRAN with
 
-  ``` r
-  install.packages("janitor")
-  ```
+``` r
+install.packages("janitor")
+```
 
 - the latest development version from GitHub with
 
-  ``` r
-  install.packages("devtools")
-  devtools::install_github("sfirke/janitor")
-  ```
+``` r
+# install.packages("remotes")
+remotes::install_github("sfirke/janitor")
+# or from r-universe
+install.packages("janitor", repos = c("https://sfirke.r-universe.dev", "https://cloud.r-project.org"))
+```
 
 ## Using janitor
 
@@ -216,11 +218,11 @@ with the suite of `adorn_` functions for quick analysis and printing of
 pretty results in a report. `adorn_` functions can be helpful with
 non-tabyls, too.
 
-#### tabyl()
+#### `tabyl()`
 
 Like `table()`, but pipe-able, data.frame-based, and fully featured.
 
-`tabyl` can be called two ways:
+`tabyl()` can be called two ways:
 
 - On a vector, when tabulating a single variable:
   `tabyl(roster$subject)`
@@ -278,7 +280,7 @@ roster %>%
 #>        Yes         1        1       0     1  1       1       0   1
 ```
 
-##### Adorning tabyls
+#### Adorning tabyls
 
 The `adorn_` functions dress up the results of these tabulation calls
 for fast, basic reporting. Here are some of the functions that augment a
@@ -306,7 +308,7 @@ Excel and SPSS when it comes to quick, informative counts. Learn more
 about `tabyl()` and the `adorn_` functions from the [tabyls
 vignette](https://sfirke.github.io/janitor/articles/tabyls.html).
 
-## Contact me
+## <i class="fa fa-bullhorn" aria-hidden="true"></i> Contact me
 
 You are welcome to: