

# dplyr 

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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")`.

dplyr is designed to abstract over how the data is stored. That means as
well as working with local data frames, you can also work with remote
database tables, using exactly the same R code. Install the dbplyr
package then read `vignette("databases", package = "dbplyr")`.

If you are new to dplyr, the best place to start is the [data import
chapter](http://r4ds.had.co.nz/transform.html) in R for data science.

## 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 use a feature from the development version, you can
install dplyr from GitHub.

``` r
# install.packages("devtools")
devtools::install_github("tidyverse/dplyr")
```

## Cheatsheet

## Usage

``` r
library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 5 x 13
#>   name  height  mass hair_color skin_color eye_color birth_year gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
#> 1 C-3PO    167    75 <NA>       gold       yellow           112 <NA>  
#> 2 R2-D2     96    32 <NA>       white, bl… red               33 <NA>  
#> 3 R5-D4     97    32 <NA>       white, red red               NA <NA>  
#> 4 IG-88    200   140 none       metal      red               15 none  
#> 5 BB8       NA    NA none       none       black             NA none  
#> # … with 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 13
#>   name  height  mass hair_color skin_color eye_color birth_year gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
#> 1 Jabb…    175  1358 <NA>       green-tan… orange         600   herma…
#> 2 Grie…    216   159 none       brown, wh… green, y…       NA   male  
#> 3 IG-88    200   140 none       metal      red             15   none  
#> 4 Dart…    202   136 none       white      yellow          41.9 male  
#> 5 Tarf…    234   136 brown      brown      blue            NA   male  
#> # … 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        5  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 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/group/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.
