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 variablesselect()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 transformation chapter in R for data science.
# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just dplyr:
install.packages("dplyr")
# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("tidyverse/dplyr")If you encounter a clear bug, please file a minimal reproducible example on github. For questions and other discussion, please use the manipulatr mailing list.
library(dplyr)
starwars %>%
filter(species == "Droid")
#> # A tibble: 5 x 13
#> name height mass hair… skin… eye_… birt… gend… home… spec… films vehi…
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <lis> <lis>
#> 1 C-3PO 167 75.0 <NA> gold yell… 112 <NA> Tato… Droid <chr… <chr…
#> 2 R2-D2 96 32.0 <NA> "whi… red 33.0 <NA> Naboo Droid <chr… <chr…
#> 3 R5-D4 97 32.0 <NA> "whi… red NA <NA> Tato… Droid <chr… <chr…
#> 4 IG-88 200 140 none metal red 15.0 none <NA> Droid <chr… <chr…
#> 5 BB8 NA NA none none black NA none <NA> Droid <chr… <chr…
#> # ... with 1 more variable: 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 77.0 26.0
#> 2 C-3PO 167 75.0 26.9
#> 3 R2-D2 96 32.0 34.7
#> 4 "Darth Vader" 202 136 33.3
#> 5 "Leia Organa" 150 49.0 21.8
#> # ... with 82 more rows
starwars %>%
arrange(desc(mass))
#> # A tibble: 87 x 13
#> name heig… mass hair… skin… eye_… birt… gend… home… spec… films vehi…
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <lis> <lis>
#> 1 "Jabb… 175 1358 <NA> "gre… oran… 600 herm… "Nal… Hutt <chr… <chr…
#> 2 Griev… 216 159 none "bro… "gre… NA male Kalee Kale… <chr… <chr…
#> 3 IG-88 200 140 none metal red 15.0 none <NA> Droid <chr… <chr…
#> 4 "Dart… 202 136 none white yell… 41.9 male Tato… Human <chr… <chr…
#> 5 Tarff… 234 136 brown brown blue NA male Kash… Wook… <chr… <chr…
#> # ... with 82 more rows, and 1 more variable: 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 74.0
#> 3 Human 35 82.8
#> 4 Kaminoan 2 88.0
#> 5 Mirialan 2 53.1
#> # ... with 4 more rows