dplyr training 3
David Hodge
June 2018
1. Purpose.
The purpose of this noteboook is to illustrate how the dplyr package can be used to perform advanced data manipulation tasks.
2. Load libraries and view practice dataset.
library(tidyverse)
starwars## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>3. Apply select and rename _all, _if and _at functions on quoted variables.
starwars## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>select_if(starwars, funs(is.numeric))## # A tibble: 87 x 3
## height mass birth_year
## <int> <dbl> <dbl>
## 1 172 77 19
## 2 167 75 112
## 3 96 32 33
## 4 202 136 41.9
## 5 150 49 19
## 6 178 120 52
## 7 165 75 47
## 8 97 32 NA
## 9 183 84 24
## 10 182 77 57
## # ... with 77 more rowsselect_at(starwars, vars("name", "height"))## # A tibble: 87 x 2
## name height
## <chr> <int>
## 1 Luke Skywalker 172
## 2 C-3PO 167
## 3 R2-D2 96
## 4 Darth Vader 202
## 5 Leia Organa 150
## 6 Owen Lars 178
## 7 Beru Whitesun lars 165
## 8 R5-D4 97
## 9 Biggs Darklighter 183
## 10 Obi-Wan Kenobi 182
## # ... with 77 more rowsrename_all(starwars, funs(paste0("sw_", .)))## # A tibble: 87 x 13
## sw_name sw_height sw_mass sw_hair_color sw_skin_color sw_eye_color
## <chr> <int> <dbl> <chr> <chr> <chr>
## 1 Luke Skywal… 172 77 blond fair blue
## 2 C-3PO 167 75 <NA> gold yellow
## 3 R2-D2 96 32 <NA> white, blue red
## 4 Darth Vader 202 136 none white yellow
## 5 Leia Organa 150 49 brown light brown
## 6 Owen Lars 178 120 brown, grey light blue
## 7 Beru Whites… 165 75 brown light blue
## 8 R5-D4 97 32 <NA> white, red red
## 9 Biggs Darkl… 183 84 black light brown
## 10 Obi-Wan Ken… 182 77 auburn, white fair blue-gray
## # ... with 77 more rows, and 7 more variables: sw_birth_year <dbl>,
## # sw_gender <chr>, sw_homeworld <chr>, sw_species <chr>,
## # sw_films <list>, sw_vehicles <list>, sw_starships <list>rename_if(starwars, funs(is.numeric), funs(str_to_upper))## # A tibble: 87 x 13
## name HEIGHT MASS hair_color skin_color eye_color BIRTH_YEAR gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>rename_at(starwars, vars("name", "height"), funs(str_to_upper))## # A tibble: 87 x 13
## NAME HEIGHT mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>starwars %>%
select(1:3) %>%
group_by_all() %>%
mutate(sum(height, mass, na.rm=T))## # A tibble: 87 x 4
## # Groups: name, height, mass [87]
## name height mass `sum(height, mass, na.rm = T)`
## <chr> <int> <dbl> <dbl>
## 1 Luke Skywalker 172 77 249
## 2 C-3PO 167 75 242
## 3 R2-D2 96 32 128
## 4 Darth Vader 202 136 338
## 5 Leia Organa 150 49 199
## 6 Owen Lars 178 120 298
## 7 Beru Whitesun lars 165 75 240
## 8 R5-D4 97 32 129
## 9 Biggs Darklighter 183 84 267
## 10 Obi-Wan Kenobi 182 77 259
## # ... with 77 more rows4. Apply filter _all, _if and _at functions on quoted variables.
filter_at(starwars, vars(contains("color")), all_vars(. == "brown"))## # A tibble: 1 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Wicket S… 88 20 brown brown brown 8 male
## # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>filter_at(starwars, vars(contains("color")), any_vars(. == "brown"))## # A tibble: 31 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Leia Or… 150 49 brown light brown 19 female
## 2 Beru Wh… 165 75 brown light blue 47 female
## 3 Biggs D… 183 84 black light brown 24 male
## 4 Chewbac… 228 112 brown unknown blue 200 male
## 5 Han Solo 180 80 brown fair brown 29 male
## 6 Wedge A… 170 77 brown fair hazel 21 male
## 7 Jek Ton… 180 110 brown fair blue NA male
## 8 Yoda 66 17 white green brown 896 male
## 9 Boba Fe… 183 78.2 black fair brown 31.5 male
## 10 Lando C… 177 79 black dark brown 31 male
## # ... with 21 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>filter_if(starwars, is.numeric, all_vars(. > 100))## # A tibble: 2 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Chewbac… 228 112 brown unknown blue 200 male
## 2 Jabba D… 175 1358 <NA> green-tan,… orange 600 herma…
## # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>filter_if(starwars, is.character, any_vars(. > 100))## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>5. Apply mutate _all, _if and _at functions on quoted variables.
mutate_all(starwars, as.character)## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red <NA> <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <chr>, vehicles <chr>, starships <chr>mutate_if(starwars, funs(is.character), funs(as.factor))## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <fct> <int> <dbl> <fct> <fct> <fct> <dbl> <fct>
## 1 Luke Sk… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 202 136 none white yellow 41.9 male
## 5 Leia Or… 150 49 brown light brown 19 female
## 6 Owen La… 178 120 brown, gr… light blue 52 male
## 7 Beru Wh… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Biggs D… 183 84 black light brown 24 male
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <fct>,
## # species <fct>, films <list>, vehicles <list>, starships <list>mutate_at(starwars, vars("height"), funs(. / 10))## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke Sk… 17.2 77 blond fair blue 19 male
## 2 C-3PO 16.7 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 9.6 32 <NA> white, bl… red 33 <NA>
## 4 Darth V… 20.2 136 none white yellow 41.9 male
## 5 Leia Or… 15 49 brown light brown 19 female
## 6 Owen La… 17.8 120 brown, gr… light blue 52 male
## 7 Beru Wh… 16.5 75 brown light blue 47 female
## 8 R5-D4 9.7 32 <NA> white, red red NA <NA>
## 9 Biggs D… 18.3 84 black light brown 24 male
## 10 Obi-Wan… 18.2 77 auburn, w… fair blue-gray 57 male
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>starwars %>%
select(1:3) %>%
mutate(sum(height, mass, na.rm=T))## # A tibble: 87 x 4
## name height mass `sum(height, mass, na.rm = T)`
## <chr> <int> <dbl> <dbl>
## 1 Luke Skywalker 172 77 19864.
## 2 C-3PO 167 75 19864.
## 3 R2-D2 96 32 19864.
## 4 Darth Vader 202 136 19864.
## 5 Leia Organa 150 49 19864.
## 6 Owen Lars 178 120 19864.
## 7 Beru Whitesun lars 165 75 19864.
## 8 R5-D4 97 32 19864.
## 9 Biggs Darklighter 183 84 19864.
## 10 Obi-Wan Kenobi 182 77 19864.
## # ... with 77 more rows6. Apply group_by and summarise _all, _if and _at functions on quoted variables.
starwars %>%
select(1:3) %>%
group_by_if(is.character) %>%
count()## # A tibble: 87 x 2
## # Groups: name [87]
## name n
## <chr> <int>
## 1 Ackbar 1
## 2 Adi Gallia 1
## 3 Anakin Skywalker 1
## 4 Arvel Crynyd 1
## 5 Ayla Secura 1
## 6 Bail Prestor Organa 1
## 7 Barriss Offee 1
## 8 BB8 1
## 9 Ben Quadinaros 1
## 10 Beru Whitesun lars 1
## # ... with 77 more rowsgroup_by_at(starwars, vars("eye_color", "hair_color")) %>%
count()## # A tibble: 35 x 3
## # Groups: eye_color, hair_color [35]
## eye_color hair_color n
## <chr> <chr> <int>
## 1 black none 9
## 2 black <NA> 1
## 3 blue auburn 1
## 4 blue auburn, grey 1
## 5 blue black 2
## 6 blue blond 3
## 7 blue brown 7
## 8 blue brown, grey 1
## 9 blue none 3
## 10 blue white 1
## # ... with 25 more rowsstarwars %>%
select_if(funs(is.numeric)) %>%
summarise_all(funs(mean), na.rm = T)## # A tibble: 1 x 3
## height mass birth_year
## <dbl> <dbl> <dbl>
## 1 174. 97.3 87.6summarise_if(starwars, funs(is.numeric), funs(min, median, mean, sd, max), na.rm = T) ## # A tibble: 1 x 15
## height_min mass_min birth_year_min height_median mass_median
## <dbl> <dbl> <dbl> <int> <dbl>
## 1 66 15 8 180 79
## # ... with 10 more variables: birth_year_median <dbl>, height_mean <dbl>,
## # mass_mean <dbl>, birth_year_mean <dbl>, height_sd <dbl>,
## # mass_sd <dbl>, birth_year_sd <dbl>, height_max <dbl>, mass_max <dbl>,
## # birth_year_max <dbl>starwars %>%
summarise_if(funs(is.numeric), funs(min, median, mean, sd, max), na.rm = T) %>%
gather() %>%
arrange(key)## # A tibble: 15 x 2
## key value
## <chr> <dbl>
## 1 birth_year_max 896
## 2 birth_year_mean 87.6
## 3 birth_year_median 52
## 4 birth_year_min 8
## 5 birth_year_sd 155.
## 6 height_max 264
## 7 height_mean 174.
## 8 height_median 180
## 9 height_min 66
## 10 height_sd 34.8
## 11 mass_max 1358
## 12 mass_mean 97.3
## 13 mass_median 79
## 14 mass_min 15
## 15 mass_sd 169.summarise_at(starwars, vars("height", "mass"), funs(sum, mean), na.rm = T)## # A tibble: 1 x 4
## height_sum mass_sum height_mean mass_mean
## <int> <dbl> <dbl> <dbl>
## 1 14123 5741. 174. 97.3starwars %>%
summarise_all(funs(sum(is.na(.)))) %>%
select_if(any_vars(. > 0)) ## # A tibble: 1 x 7
## height mass hair_color birth_year gender homeworld species
## <int> <int> <int> <int> <int> <int> <int>
## 1 6 28 5 44 3 10 57. Work with tidy evaluation programming to unquote.
select(iris, !!sym(c("Sepal.Length"))) %>% as_tibble()## # A tibble: 150 x 1
## Sepal.Length
## <dbl>
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5
## 6 5.4
## 7 4.6
## 8 5
## 9 4.4
## 10 4.9
## # ... with 140 more rowsselect(iris, !!!syms(c("Sepal.Width", "Sepal.Length"))) %>% as_tibble()## # A tibble: 150 x 2
## Sepal.Width Sepal.Length
## <dbl> <dbl>
## 1 3.5 5.1
## 2 3 4.9
## 3 3.2 4.7
## 4 3.1 4.6
## 5 3.6 5
## 6 3.9 5.4
## 7 3.4 4.6
## 8 3.4 5
## 9 2.9 4.4
## 10 3.1 4.9
## # ... with 140 more rowsfilter(iris, !!sym("Sepal.Width") > 4) %>% as_tibble()## # A tibble: 3 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.7 4.4 1.5 0.4 setosa
## 2 5.2 4.1 1.5 0.1 setosa
## 3 5.5 4.2 1.4 0.2 setosafilter(iris, !!sym("Sepal.Width") == "virginica") %>% as_tibble()## # A tibble: 0 x 5
## # ... with 5 variables: Sepal.Length <dbl>, Sepal.Width <dbl>,
## # Petal.Length <dbl>, Petal.Width <dbl>, Species <fct>mutate(iris, ratio=!!sym("Sepal.Length") / !!sym("Sepal.Width")) %>% as_tibble()## # A tibble: 150 x 6
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ratio
## <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
## 1 5.1 3.5 1.4 0.2 setosa 1.46
## 2 4.9 3 1.4 0.2 setosa 1.63
## 3 4.7 3.2 1.3 0.2 setosa 1.47
## 4 4.6 3.1 1.5 0.2 setosa 1.48
## 5 5 3.6 1.4 0.2 setosa 1.39
## 6 5.4 3.9 1.7 0.4 setosa 1.38
## 7 4.6 3.4 1.4 0.3 setosa 1.35
## 8 5 3.4 1.5 0.2 setosa 1.47
## 9 4.4 2.9 1.4 0.2 setosa 1.52
## 10 4.9 3.1 1.5 0.1 setosa 1.58
## # ... with 140 more rowsarrange(iris, !!sym("Sepal.Length")) %>% as_tibble()## # A tibble: 150 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 4.3 3 1.1 0.1 setosa
## 2 4.4 2.9 1.4 0.2 setosa
## 3 4.4 3 1.3 0.2 setosa
## 4 4.4 3.2 1.3 0.2 setosa
## 5 4.5 2.3 1.3 0.3 setosa
## 6 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 4.6 3.6 1 0.2 setosa
## 9 4.6 3.2 1.4 0.2 setosa
## 10 4.7 3.2 1.3 0.2 setosa
## # ... with 140 more rowsarrange(iris, desc(!!sym("Sepal.Length"))) %>% as_tibble()## # A tibble: 150 x 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 7.9 3.8 6.4 2 virginica
## 2 7.7 3.8 6.7 2.2 virginica
## 3 7.7 2.6 6.9 2.3 virginica
## 4 7.7 2.8 6.7 2 virginica
## 5 7.7 3 6.1 2.3 virginica
## 6 7.6 3 6.6 2.1 virginica
## 7 7.4 2.8 6.1 1.9 virginica
## 8 7.3 2.9 6.3 1.8 virginica
## 9 7.2 3.6 6.1 2.5 virginica
## 10 7.2 3.2 6 1.8 virginica
## # ... with 140 more rowssummarise(iris, mean(!!sym("Sepal.Length"), na.rm = T))## mean(Sepal.Length, na.rm = T)
## 1 5.843333ggplot(iris, aes(x = !!sym("Sepal.Width"), y = !!sym("Sepal.Length"), colour = !!sym("Species"))) +
geom_point()