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.
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")
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 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.