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# dplyr <a href="https://dplyr.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" /></a>
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## 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/acero/overview.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.posit.co/) 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
<a href="https://github.com/rstudio/cheatsheets/blob/main/data-transformation.pdf"><img src="https://raw.githubusercontent.com/rstudio/cheatsheets/main/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png" width="630" height="252"/></a>
## 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
#> <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](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.