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 in R for data science.

Usage

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, blue 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 Jabba …    175  1358 <NA>       green-tan,… orange          600   herma…
#> 2 Grievo…    216   159 none       brown, whi… green, ye…       NA   male  
#> 3 IG-88      200   140 none       metal       red              15   none  
#> 4 Darth …    202   136 none       white       yellow           41.9 male  
#> 5 Tarfful    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)
#> # A tibble: 9 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 4 more rows

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