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dplyr

CRAN status R-CMD-check 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").

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:

  • arrow for larger-than-memory datasets, including on remote cloud storage like AWS S3, using the Apache Arrow C++ engine, Acero.

  • 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.

  • duckplyr for using duckdb on large, in-memory datasets with zero extra copies. Translates your dplyr code to high performance duckdb queries with an automatic R fallback when translation isn’t possible.

  • duckdb 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("pak")
pak::pak("tidyverse/dplyr")

Cheat Sheet

Usage

library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 6 × 14
#>   name   height  mass hair_color skin_color  eye_color birth_year sex   gender  
#>   <chr>   <int> <dbl> <chr>      <chr>       <chr>          <dbl> <chr> <chr>   
#> 1 C-3PO     167    75 <NA>       gold        yellow           112 none  masculi…
#> 2 R2-D2      96    32 <NA>       white, blue red               33 none  masculi…
#> 3 R5-D4      97    32 <NA>       white, red  red               NA none  masculi…
#> 4 IG-88     200   140 none       metal       red               15 none  masculi…
#> 5 R4-P17     96    NA none       silver, red red, blue         NA none  feminine
#> # ℹ 1 more row
#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>

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    
#> # ℹ 82 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
#> # ℹ 82 more rows

starwars %>% 
  arrange(desc(mass))
#> # A tibble: 87 × 14
#>   name      height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Jabba De…    175  1358 <NA>       green-tan… orange         600   herm… mascu…
#> 2 Grievous     216   159 none       brown, wh… green, y…       NA   male  mascu…
#> 3 IG-88        200   140 none       metal      red             15   none  mascu…
#> 4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
#> 5 Tarfful      234   136 brown      brown      blue            NA   male  mascu…
#> # ℹ 82 more rows
#> # ℹ 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: 9 × 3
#>   species      n  mass
#>   <chr>    <int> <dbl>
#> 1 Droid        6  69.8
#> 2 Gungan       3  74  
#> 3 Human       35  81.3
#> 4 Kaminoan     2  88  
#> 5 Mirialan     2  53.1
#> # ℹ 4 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 forum.posit.co.

Code of conduct

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.