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 transformation 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.
duckdb or arrow for large datasets that are still small enough to fit on your computer.
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 to use a feature from the development version, you can install the development version of dplyr from GitHub.

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

Cheat Sheet

Usage

library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 6 × 14
#>   name   height  mass hair_color skin_color eye_c…¹ birth…² sex   gender homew…³
#>   <chr>   <int> <dbl> <chr>      <chr>      <chr>     <dbl> <chr> <chr>  <chr>  
#> 1 C-3PO     167    75 <NA>       gold       yellow      112 none  mascu… Tatooi…
#> 2 R2-D2      96    32 <NA>       white, bl… red          33 none  mascu… Naboo  
#> 3 R5-D4      97    32 <NA>       white, red red          NA none  mascu… Tatooi…
#> 4 IG-88     200   140 none       metal      red          15 none  mascu… <NA>   
#> 5 R4-P17     96    NA none       silver, r… red, b…      NA none  femin… <NA>   
#> # … with 1 more row, 4 more variables: species <chr>, films <list>,
#> #   vehicles <list>, starships <list>, and abbreviated variable names
#> #   ¹eye_color, ²birth_year, ³homeworld
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

starwars %>% 
  select(name, ends_with("color"))
#> # A tibble: 87 × 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
#> # ℹ Use `print(n = ...)` to see more rows

starwars %>% 
  mutate(name, bmi = mass / ((height / 100)  ^ 2)) %>%
  select(name:mass, bmi)
#> # A tibble: 87 × 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
#> # ℹ Use `print(n = ...)` to see more rows

starwars %>% 
  arrange(desc(mass))
#> # A tibble: 87 × 14
#>   name         height  mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex   gender homew…⁵
#>   <chr>         <int> <dbl> <chr>   <chr>   <chr>     <dbl> <chr> <chr>  <chr>  
#> 1 Jabba Desil…    175  1358 <NA>    green-… orange    600   herm… mascu… Nal Hu…
#> 2 Grievous        216   159 none    brown,… green,…    NA   male  mascu… Kalee  
#> 3 IG-88           200   140 none    metal   red        15   none  mascu… <NA>   
#> 4 Darth Vader     202   136 none    white   yellow     41.9 male  mascu… Tatooi…
#> 5 Tarfful         234   136 brown   brown   blue       NA   male  mascu… Kashyy…
#> # … with 82 more rows, 4 more variables: species <chr>, films <list>,
#> #   vehicles <list>, starships <list>, and abbreviated variable names
#> #   ¹hair_color, ²skin_color, ³eye_color, ⁴birth_year, ⁵homeworld
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

starwars %>%
  group_by(species) %>%
  summarise(
    n = n(),
    mass = mean(mass, na.rm = TRUE)
  ) %>%
  filter(
    n > 1,
    mass > 50
  )
#> # A tibble: 8 × 3
#>   species      n  mass
#>   <chr>    <int> <dbl>
#> 1 Droid        6  69.8
#> 2 Gungan       3  74  
#> 3 Human       35  82.8
#> 4 Kaminoan     2  88  
#> 5 Mirialan     2  53.1
#> # … with 3 more rows
#> # ℹ Use `print(n = ...)` to see more rows

Getting help

If you encounter a clear bug, please file an issue with 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.

apreshill / dplyr