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tidyverse/dplyr

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tidyverse / dplyr

R

Dplyr: A grammar of data manipulation


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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 in R for data science.

Installation

# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just dplyr:
install.packages("dplyr")

# Or the 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.

Usage

library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 5 × 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 × 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 × 4
#>             name height  mass      bmi
#>            <chr>  <int> <dbl>    <dbl>
#> 1 Luke Skywalker    172    77 26.02758
#> 2          C-3PO    167    75 26.89232
#> 3          R2-D2     96    32 34.72222
#> 4    Darth Vader    202   136 33.33007
#> 5    Leia Organa    150    49 21.77778
#> # ... with 82 more rows

starwars %>% 
  arrange(desc(mass))
#> # A tibble: 87 × 13
#>                    name height  mass hair_color       skin_color
#>                   <chr>  <int> <dbl>      <chr>            <chr>
#> 1 Jabba Desilijic Tiure    175  1358       <NA> green-tan, brown
#> 2              Grievous    216   159       none     brown, white
#> 3                 IG-88    200   140       none            metal
#> 4           Darth Vader    202   136       none            white
#> 5               Tarfful    234   136      brown            brown
#> # ... with 82 more rows, and 8 more variables: eye_color <chr>,
#> #   birth_year <dbl>, gender <chr>, 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 × 3
#>    species     n     mass
#>      <chr> <int>    <dbl>
#> 1    Droid     5 69.75000
#> 2   Gungan     3 74.00000
#> 3    Human    35 82.78182
#> 4 Kaminoan     2 88.00000
#> 5 Mirialan     2 53.10000
#> # ... with 4 more rows