1818Status] ( https://travis-ci.org/sfirke/janitor.svg?branch=master )] ( https://travis-ci.org/sfirke/janitor )
1919[ ![ Coverage
2020Status] ( https://img.shields.io/codecov/c/github/sfirke/janitor/master.svg )] ( https://codecov.io/github/sfirke/janitor?branch=master )
21- [ ![ lifecycle] ( https://img.shields.io/badge/lifecycle-stable-brightgreen.svg )] ( https://www.tidyverse .org/lifecycle/ #stable )
21+ [ ![ lifecycle] ( https://img.shields.io/badge/lifecycle-stable-brightgreen.svg )] ( https://lifecycle.r-lib .org/articles/stages.html #stable )
2222[ ![ CRAN_Status_Badge] ( https://www.r-pkg.org/badges/version-ago/janitor )] ( https://cran.r-project.org/package=janitor )
2323![ !Monthly Downloads] ( https://cranlogs.r-pkg.org/badges/janitor )
2424![ !Downloads] ( https://cranlogs.r-pkg.org/badges/grand-total/janitor )
@@ -31,10 +31,10 @@ their thinking for the fun stuff.
3131
3232The main janitor functions:
3333
34- - perfectly format data.frame column names;
35- - create and format frequency tables of one, two, or three variables -
36- think an improved ` table() ` ; and
37- - provide other tools for cleaning and examining data.frames.
34+ - perfectly format data.frame column names;
35+ - create and format frequency tables of one, two, or three variables -
36+ think an improved ` table() ` ; and
37+ - provide other tools for cleaning and examining data.frames.
3838
3939The tabulate-and-report functions approximate popular features of SPSS
4040and Microsoft Excel.
@@ -50,24 +50,24 @@ optimized for cleaning data brought in with the
5050
5151You can install:
5252
53- - the most recent officially-released version from CRAN with
53+ - the most recent officially-released version from CRAN with
5454
55- ``` r
56- install.packages(" janitor" )
57- ```
55+ ``` r
56+ install.packages(" janitor" )
57+ ```
5858
59- - the latest development version from GitHub with
59+ - the latest development version from GitHub with
6060
61- `` ` r
62- install.packages(" devtools" )
63- devtools :: install_github(" sfirke/janitor" )
64- ```
61+ ``` r
62+ install.packages(" devtools" )
63+ devtools :: install_github(" sfirke/janitor" )
64+ ```
6565
6666## Using janitor
6767
6868A full description of each function, organized by topic, can be found in
6969janitor’s [ catalog of functions
70- vignette ](http : // sfirke.github.io / janitor / articles / janitor.html ). There
70+ vignette] ( https ://sfirke.github.io/janitor/articles/janitor.html) . There
7171you will find functions not mentioned in this README, like
7272` compare_df_cols() ` which provides a summary of differences in column
7373names and types when given a set of data.frames.
@@ -83,19 +83,19 @@ in the Microsoft Excel file
8383
8484Dirtiness includes:
8585
86- - A header at the top
87- - Dreadful column names
88- - Rows and columns containing Excel formatting but no data
89- - Dates in two different formats in a single column (MM / DD / YYYY and
90- numbers )
91- - Values spread inconsistently over the “Certification ” columns
86+ - A header at the top
87+ - Dreadful column names
88+ - Rows and columns containing Excel formatting but no data
89+ - Dates in two different formats in a single column (MM/DD/YYYY and
90+ numbers)
91+ - Values spread inconsistently over the “Certification” columns
9292
9393Here’s that data after being read in to R:
9494
9595``` r
9696library(readxl ); library(janitor ); library(dplyr ); library(here )
9797
98- roster_raw <- read_excel(here(" dirty_data.xlsx" )) # available at http ://github.com/sfirke/janitor
98+ roster_raw <- read_excel(here(" dirty_data.xlsx" )) # available at https ://github.com/sfirke/janitor
9999glimpse(roster_raw )
100100# > Rows: 14
101101# > Columns: 11
@@ -212,12 +212,12 @@ Like `table()`, but pipe-able, data.frame-based, and fully featured.
212212
213213` tabyl ` can be called two ways:
214214
215- - On a vector, when tabulating a single variable:
216- ` tabyl(roster$subject) `
217- - On a data.frame, specifying 1, 2, or 3 variable names to tabulate:
218- ` roster %>% tabyl(subject, employee_status) ` .
219- - Here the data.frame is passed in with the ` %>% ` pipe; this
220- allows ` tabyl ` to be used in an analysis pipeline
215+ - On a vector, when tabulating a single variable:
216+ ` tabyl(roster$subject) `
217+ - On a data.frame, specifying 1, 2, or 3 variable names to tabulate:
218+ ` roster %>% tabyl(subject, employee_status) ` .
219+ - Here the data.frame is passed in with the ` %>% ` pipe; this allows
220+ ` tabyl ` to be used in an analysis pipeline
221221
222222One variable:
223223
@@ -294,15 +294,15 @@ Pipe that right into `knitr::kable()` in your RMarkdown report.
294294These modular adornments can be layered to reduce R’s deficit against
295295Excel and SPSS when it comes to quick, informative counts. Learn more
296296about ` tabyl() ` and the ` adorn_ ` functions from the [ tabyls
297- vignette] ( http ://sfirke.github.io/janitor/articles/tabyls.html) .
297+ vignette] ( https ://sfirke.github.io/janitor/articles/tabyls.html) .
298298
299299## <i class =" fa fa-bullhorn " aria-hidden =" true " ></i > Contact Me
300300
301301You are welcome to:
302302
303- - submit suggestions and report bugs:
304- < https://github.com/sfirke/janitor/issues >
305- - let me know what you think on Mastodon:
306- [ @samfirke @a2mi.social] ( https://a2mi.social/@samfirke )
307- - compose a friendly e-mail to:
308- <img src = " http ://samfirke.com/wp-content/uploads/2016/07/email_address_whitespace_top.png" alt = " samuel.firke AT gmail " width = " 210 " />
303+ - submit suggestions and report bugs:
304+ < https://github.com/sfirke/janitor/issues >
305+ - let me know what you think on Mastodon:
306+ [ @samfirke @a2mi.social] ( https://a2mi.social/@samfirke )
307+ - compose a friendly e-mail to:
308+ <img src = " https ://samfirke.com/wp-content/uploads/2016/07/email_address_whitespace_top.png" alt = " samuel.firke AT gmail " width = " 210 " />
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