![]() ![]() ![]() The basics of working with data. R Programming Cheat Sheet advanced CreatedBy: arianneColton andSeanChen environments Access any environment on the search list as.environment('package:base') Find the environment where a name is defined pryr::where('func1') Function environments There are 4 environments for functions. It works by converting Râs native data frame objects into data.tables with new and enhanced functionality. Run runExample() etc. By default, R adds an extra 4 to the plotting range (see the dark green region on the figure) so that points right up on the edges of your plot do not get partially clipped. Use it as a handy, high-level reference for a quick start with R. Data Transformation with data.table :: CHEAT SHEET Manipulate columns with j Functions for data.tables data.table is an extremely fast and memory efficient package for transforming data in R. R/ (optional) directory of files to share with web browsers (images, runExample(). An R Cheat Sheet Introduction CC BY Steve Simon, P.Mean Consulting https.Library ( dplyr ) starwars %>% filter ( species = "Droid" ) #> # A tibble: 6 à 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> #> 1 C-3PO 167 75 gold yellow 112 none masculi⦠#> 2 R2-D2 96 32 white, blue red 33 none masculi⦠#> 3 R5-D4 97 32 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 #> # â¹ 1 more row #> # â¹ 5 more variables: homeworld, species, films, #> # vehicles, starships starwars %>% select ( name, ends_with ( "color" ) ) #> # A tibble: 87 à 4 #> name hair_color skin_color eye_color #> #> 1 Luke Skywalker blond fair blue #> 2 C-3PO gold yellow #> 3 R2-D2 white, blue red #> 4 Darth Vader none white yellow #> 5 Leia Organa brown light brown #> # â¹ 82 more rows starwars %>% mutate ( name, bmi = mass / ( ( height / 100 ) ^ 2 ) ) %>% select ( name : mass, bmi ) #> # A tibble: 87 à 4 #> name height mass bmi #> #> 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 #> # â¹ 82 more rows starwars %>% arrange ( desc ( mass ) ) #> # A tibble: 87 à 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> #> 1 Jabba De⦠175 1358 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⦠#> # â¹ 82 more rows #> # â¹ 5 more variables: homeworld, species, films, #> # vehicles, starships starwars %>% group_by ( species ) %>% summarise ( n = n ( ), mass = mean ( mass, na.rm = TRUE ) ) %>% filter ( n > 1, mass > 50 ) #> # A tibble: 8 à 3 #> species n mass #> #> 1 Droid 6 69.8 #> 2 Gungan 3 74 #> 3 Human 35 82.8 #> 4 Kaminoan 2 88 #> 5 Mirialan 2 53. This cheat sheet will cover an overview of getting started with R. ![]()
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