18 dplyr
18.1 Introduction
For more help PLEASE check out Introduction to dplyr introducing the key functionality of the dplyr package.
https://dplyr.tidyverse.org/articles/dplyr.html
Your life is about to change. For the better, even.
18.2 A Neat Resource
- RStudio’s Data Wrangling Cheat Sheet for dplyr and tidyr
18.3 Single table verbs
dplyr aims to provide a function for each basic verb of data manipulation. These verbs can be organised into three categories based on the component of the dataset that they work with:
Rows:
-
filter()chooses rows based on column values. -
slice()chooses rows based on location. -
arrange()changes the order of the rows.
Columns:
-
select()changes whether or not a column is included. -
rename()changes the name of columns.mutate()changes the values of columns and creates new columns. -
relocate()changes the order of the columns. Groups of rows: -
summarise()collapses a group into a single row. It’s not that useful until we learn thegroup_by()verb below.
18.4 The pipe
All of the dplyr functions take a data frame (or tibble) as the first argument. You can use the pipe to rewrite multiple operations that you can read left-to-right, top-to-bottom (reading the pipe operator as “then”).
What is this:
%>%?
18.5 Loading dplyr
# You should already have done this but you'll need it
install.packages("dplyr")
18.6 starwars examples
library(dplyr)
starwars %>%
filter(species == "Droid")
#> # A tibble: 6 × 14
#> name height mass hair_color skin_color eye_color
#> <chr> <int> <dbl> <chr> <chr> <chr>
#> 1 C-3PO 167 75 <NA> gold yellow
#> 2 R2-D2 96 32 <NA> white, blue red
#> 3 R5-D4 97 32 <NA> white, red red
#> 4 IG-88 200 140 none metal red
#> 5 R4-P17 96 NA none silver, red red, blue
#> 6 BB8 NA NA none none black
#> # … with 8 more variables: birth_year <dbl>, sex <chr>,
#> # gender <chr>, 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
#> 6 Owen Lars brown, grey light blue
#> 7 Beru Whitesun lars brown light blue
#> 8 R5-D4 <NA> white, red red
#> 9 Biggs Darklighter black light brown
#> 10 Obi-Wan Kenobi auburn, white fair blue-gray
#> # … with 77 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 26.0
#> 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
#> 6 Owen Lars 178 120 37.9
#> 7 Beru Whitesun lars 165 75 27.5
#> 8 R5-D4 97 32 34.0
#> 9 Biggs Darklighter 183 84 25.1
#> 10 Obi-Wan Kenobi 182 77 23.2
#> # … with 77 more rows
starwars %>%
arrange(desc(mass))
#> # A tibble: 87 × 14
#> name height mass hair_color skin_color eye_color
#> <chr> <int> <dbl> <chr> <chr> <chr>
#> 1 Jabba Desil… 175 1358 <NA> green-tan… orange
#> 2 Grievous 216 159 none brown, wh… green, y…
#> 3 IG-88 200 140 none metal red
#> 4 Darth Vader 202 136 none white yellow
#> 5 Tarfful 234 136 brown brown blue
#> 6 Owen Lars 178 120 brown, gr… light blue
#> 7 Bossk 190 113 none green red
#> 8 Chewbacca 228 112 brown unknown blue
#> 9 Jek Tono Po… 180 110 brown fair blue
#> 10 Dexter Jett… 198 102 none brown yellow
#> # … with 77 more rows, and 8 more variables:
#> # birth_year <dbl>, sex <chr>, 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,
mass > 50
)
#> # A tibble: 8 × 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
#> 6 Twi'lek 2 55
#> 7 Wookiee 2 124
#> 8 Zabrak 2 80