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# dplyr <a href='https://dplyr.tidyverse.org'><img src='man/figures/logo.png' align="right" height="139" /></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.had.co.nz/transform.html) 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:

  - [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.

  - [dbplyr](https://dbplyr.tidyverse.org/): for data stored in a
    relational database. Translates your dplyr code to SQL.

  - [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("devtools")
devtools::install_github("tidyverse/dplyr")
```

## Cheatsheet

<a href="https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf"><img src="https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png" width="630" height="252"/></a>

## Usage

``` r
library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 6 x 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  mascu…
#> 2 R2-D2     96    32 <NA>       white, bl… red               33 none  mascu…
#> 3 R5-D4     97    32 <NA>       white, red red               NA none  mascu…
#> 4 IG-88    200   140 none       metal      red               15 none  mascu…
#> 5 R4-P…     96    NA none       silver, r… red, blue         NA none  femin…
#> # … with 1 more row, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

starwars %>% 
  select(name, ends_with("color"))
#> # A tibble: 87 x 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    
#> # … with 82 more rows

starwars %>% 
  mutate(name, bmi = mass / ((height / 100)  ^ 2)) %>%
  select(name:mass, bmi)
#> # A tibble: 87 x 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
#> # … with 82 more rows

starwars %>% 
  arrange(desc(mass))
#> # A tibble: 87 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Jabb…    175  1358 <NA>       green-tan… orange         600   herm… mascu…
#> 2 Grie…    216   159 none       brown, wh… green, y…       NA   male  mascu…
#> 3 IG-88    200   140 none       metal      red             15   none  mascu…
#> 4 Dart…    202   136 none       white      yellow          41.9 male  mascu…
#> 5 Tarf…    234   136 brown      brown      blue            NA   male  mascu…
#> # … with 82 more rows, and 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: 8 x 3
#>   species      n  mass
#>   <chr>    <int> <dbl>
#> 1 Droid        6  69.8
#> 2 Gungan       3  74  
#> 3 Human       35  82.8
#> 4 Kaminoan     2  88  
#> 5 Mirialan     2  53.1
#> # … with 3 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
[community.rstudio.com](https://community.rstudio.com/) or the
[manipulatr mailing list](https://groups.google.com/d/forum/manipulatr).

-----

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.