dplyr

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").

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

Backends

In addition to data frames/tibbles, dplyr makes working with other computational backends accessible and efficient. Below is a list of alternative backends:

dtplyr: for large, in-memory datasets. Translates your dplyr code to high performance data.table code.
dbplyr: for data stored in a relational database. Translates your dplyr code to SQL.
sparklyr: for very large datasets stored in Apache Spark.

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 homeworld
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>  <chr>    
#> 1 C-3PO    167    75 <NA>       gold       yellow           112 <NA>   Tatooine 
#> 2 R2-D2     96    32 <NA>       white, bl… red               33 <NA>   Naboo    
#> 3 R5-D4     97    32 <NA>       white, red red               NA <NA>   Tatooine 
#> 4 IG-88    200   140 none       metal      red               15 none   <NA>     
#> 5 BB8       NA    NA none       none       black             NA none   <NA>     
#> # … with 4 more variables: 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 homeworld
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>  <chr>    
#> 1 Jabb…    175  1358 <NA>       green-tan… orange         600   herma… Nal Hutta
#> 2 Grie…    216   159 none       brown, wh… green, y…       NA   male   Kalee    
#> 3 IG-88    200   140 none       metal      red             15   none   <NA>     
#> 4 Dart…    202   136 none       white      yellow          41.9 male   Tatooine 
#> 5 Tarf…    234   136 brown      brown      blue            NA   male   Kashyyyk 
#> # … with 82 more rows, and 4 more variables: 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.

dfalbel/dplyr