# dplyr [](https://cran.r-project.org/package=dplyr) [](https://github.com/tidyverse/dplyr/actions/workflows/R-CMD-check.yaml) [](https://app.codecov.io/gh/tidyverse/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](https://r4ds.hadley.nz/data-transform) 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](https://arrow.apache.org/docs/r/) for larger-than-memory datasets, including on remote cloud storage like AWS S3, using the Apache Arrow C++ engine, [Acero](https://arrow.apache.org/docs/cpp/streaming_execution.html). - [dbplyr](https://dbplyr.tidyverse.org/) for data stored in a relational database. Translates your dplyr code to SQL. - [dtplyr](https://dtplyr.tidyverse.org/) for large, in-memory datasets. Translates your dplyr code to high performance [data.table](https://rdatatable.gitlab.io/data.table/) code. - [duckplyr](https://duckplyr.tidyverse.org/) for large, in-memory datasets. Translates your dplyr code to high performance [duckdb](https://duckdb.org) queries with zero extra copies and an automatic R fallback when translation isn’t possible. - [sparklyr](https://spark.rstudio.com) for very large datasets stored in [Apache Spark](https://spark.apache.org). ## Installation ``` r # 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. ``` r # install.packages("pak") pak::pak("tidyverse/dplyr") ``` ## Cheat Sheet ## Usage ``` r library(dplyr) starwars |> filter(species == "Droid") #> # A tibble: 6 × 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> #> 1 C-3PO 167 75 gold yellow 112 none masculi… #> 2 R2-D2 96 32 white, blue red 33 none masculi… #> 3 R5-D4 97 32 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 , species , films , #> # vehicles , starships starwars |> select(name, ends_with("color")) #> # A tibble: 87 × 4 #> name hair_color skin_color eye_color #> #> 1 Luke Skywalker blond fair blue #> 2 C-3PO gold yellow #> 3 R2-D2 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 #> #> 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 #> #> 1 Jabba De… 175 1358 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 , species , films , #> # vehicles , starships 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 #> #> 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](https://github.com/tidyverse/dplyr/issues). For questions and other discussion, please use [forum.posit.co](https://forum.posit.co/). ## Code of conduct Please note that this project is released with a [Contributor Code of Conduct](https://dplyr.tidyverse.org/CODE_OF_CONDUCT). By participating in this project you agree to abide by its terms.