tidyverse/dplyr


dplyr: A grammar of data manipulation

https://dplyr.tidyverse.org

License: Other

Language: R

Keywords: data-manipulation, grammar, r


dplyr

CRAN status Travis build status AppVeyor build status Codecov test coverage

Overview

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

  • mutate() adds new variables that are functions of existing variables
  • select() picks variables based on their names.
  • filter() picks cases based on their values.
  • summarise() reduces multiple values down to a single summary.
  • arrange() changes the ordering of the rows.

These all combine naturally with group_by() which allows you to perform any operation “by group”. You can learn more about them in vignette("dplyr"). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette("two-table").

dplyr is designed to abstract over how the data is stored. That means as well as working with local data frames, you can also work with remote database tables, using exactly the same R code. Install the dbplyr package then read vignette("databases", package = "dbplyr").

If you are new to dplyr, the best place to start is the data import chapter in R for data science.

Installation

# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just dplyr:
install.packages("dplyr")

Development version

To get a bug fix, or use a feature from the development version, you can install dplyr from GitHub.

# install.packages("devtools")
devtools::install_github("tidyverse/dplyr")

Cheatsheet

Usage

library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 5 x 13
#>   name  height  mass hair_color skin_color eye_color birth_year gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
#> 1 C-3PO    167    75 <NA>       gold       yellow           112 <NA>  
#> 2 R2-D2     96    32 <NA>       white, bl… red               33 <NA>  
#> 3 R5-D4     97    32 <NA>       white, red red               NA <NA>  
#> 4 IG-88    200   140 none       metal      red               15 none  
#> 5 BB8       NA    NA none       none       black             NA none  
#> # … with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>

starwars %>% 
  select(name, ends_with("color"))
#> # A tibble: 87 x 4
#>   name           hair_color skin_color  eye_color
#>   <chr>          <chr>      <chr>       <chr>    
#> 1 Luke Skywalker blond      fair        blue     
#> 2 C-3PO          <NA>       gold        yellow   
#> 3 R2-D2          <NA>       white, blue red      
#> 4 Darth Vader    none       white       yellow   
#> 5 Leia Organa    brown      light       brown    
#> # … with 82 more rows

starwars %>% 
  mutate(name, bmi = mass / ((height / 100)  ^ 2)) %>%
  select(name:mass, bmi)
#> # A tibble: 87 x 4
#>   name           height  mass   bmi
#>   <chr>           <int> <dbl> <dbl>
#> 1 Luke Skywalker    172    77  26.0
#> 2 C-3PO             167    75  26.9
#> 3 R2-D2              96    32  34.7
#> 4 Darth Vader       202   136  33.3
#> 5 Leia Organa       150    49  21.8
#> # … with 82 more rows

starwars %>% 
  arrange(desc(mass))
#> # A tibble: 87 x 13
#>   name  height  mass hair_color skin_color eye_color birth_year gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
#> 1 Jabb…    175  1358 <NA>       green-tan… orange         600   herma…
#> 2 Grie…    216   159 none       brown, wh… green, y…       NA   male  
#> 3 IG-88    200   140 none       metal      red             15   none  
#> 4 Dart…    202   136 none       white      yellow          41.9 male  
#> 5 Tarf…    234   136 brown      brown      blue            NA   male  
#> # … with 82 more rows, and 5 more variables: homeworld <chr>,
#> #   species <chr>, films <list>, vehicles <list>, starships <list>

starwars %>%
  group_by(species) %>%
  summarise(
    n = n(),
    mass = mean(mass, na.rm = TRUE)
  ) %>%
  filter(n > 1,
         mass > 50)
#> # A tibble: 8 x 3
#>   species      n  mass
#>   <chr>    <int> <dbl>
#> 1 Droid        5  69.8
#> 2 Gungan       3  74  
#> 3 Human       35  82.8
#> 4 Kaminoan     2  88  
#> 5 Mirialan     2  53.1
#> # … with 3 more rows

Getting help

If you encounter a clear bug, please file a minimal reproducible example on github. For questions and other discussion, please use community.rstudio.com, or the manipulatr mailing list.


Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Project Statistics

Sourcerank 19
Repository Size 22.6 MB
Stars 2,915
Forks 1,072
Watchers 260
Open issues 143
Dependencies 37
Contributors 175
Tags 39
Created
Last updated
Last pushed

Top Contributors See all

Romain François Hadley Wickham Kirill Müller Lionel Henry Mara Averick lindbrook Jim Hester Hannes Mühleisen Hiroaki Yutani Jennifer (Jenny) Bryan Kevin Ushey Sergio Oller Sam Firke Stefan Fleck Ilari Scheinin Bill Denney Javier Luraschi Sergey Goder Leon du Toit Earo Wang

Packages Referencing this Repo

dplyr
A Grammar of Data Manipulation
Latest release 0.8.1 - Updated - 2.92K stars

Recent Tags See all

v0.8.0.1 February 15, 2019
v0.8.0 February 12, 2019
v0.7.8 November 10, 2018
v0.7.7 August 18, 2018
v0.7.6 June 30, 2018
v0.7.5 April 14, 2018
v0.7.4.9005 April 12, 2018
v0.7.4.9004 April 10, 2018
v0.7.4.9003 March 29, 2018
v0.7.4.9002 March 16, 2018
v0.7.4.9001 March 13, 2018
v0.7.4 September 16, 2017
v0.7.3 September 06, 2017
v0.7.2 July 19, 2017
v0.7.2 July 19, 2017

Interesting Forks See all

MhAmine/dplyr
Dplyr: A grammar of data manipulation
R - Updated - 1 stars

Something wrong with this page? Make a suggestion

Last synced: 2019-05-14 12:40:09 UTC

Login to resync this repository