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 variablesselect()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.
See AlsoGROUP BY and FILTERsparklyr 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 rowsstarwars %>% 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 rowsstarwars %>% 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 rowsGetting 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.
