Dplyr Recode Continuous as Discrete Variable
Overview
dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:
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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").
If you are new to dplyr, the best place to start is the data transformation chapter in R for data science.
Backends
In addition to data frames/tibbles, dplyr makes working with other computational backends accessible and efficient. Below is a list of alternative backends:
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dtplyr: for large, in-memory datasets. Translates your dplyr code to high performance data.table code.
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dbplyr: for data stored in a relational database. Translates your dplyr code to SQL.
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sparklyr: for very large datasets stored in Apache Spark.
Installation
Development version
To get a bug fix or to use a feature from the development version, you can install the development version of dplyr from GitHub.
# install.packages("devtools") devtools :: install_github ( "tidyverse/dplyr" ) Cheat Sheet
Usage
library ( dplyr ) starwars %>% filter ( species == "Droid" ) #> # A tibble: 6 × 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> #> 1 C-3PO 167 75 <NA> gold yellow 112 none masculi… #> 2 R2-D2 96 32 <NA> white, blue red 33 none masculi… #> 3 R5-D4 97 32 <NA> white, red red NA none masculi… #> 4 IG-88 200 140 none metal red 15 none masculi… #> 5 R4-P17 96 NA none silver, red red, blue NA none feminine #> # … with 1 more row, and 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 26.0 #> 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 × 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> #> 1 Jabba De… 175 1358 <NA> green-tan… orange 600 herm… mascu… #> 2 Grievous 216 159 none brown, wh… green, y… NA male mascu… #> 3 IG-88 200 140 none metal red 15 none mascu… #> 4 Darth Va… 202 136 none white yellow 41.9 male mascu… #> 5 Tarfful 234 136 brown brown blue NA male mascu… #> # … 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 × 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 #> # … with 3 more rows Source: https://dplyr.tidyverse.org/
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