perctable and proptable use the cross-classifying factors to build a
contingency table of the percents or proportions at each combination of factor levels.
perctable(...) proptable(...)
| … | arguments passed directly to |
|---|
a contingency table, an object of class "table", an array of percentage or proportion values. Note that unlike S the result is always an array, a 1D array if one factor is given.
See table().
#> #> tally> # tally() is short-hand for summarise() #> tally> mtcars %>% tally() #> n #> 1 32 #> #> tally> mtcars %>% group_by(cyl) %>% tally() #> # A tibble: 3 x 2 #> cyl n #> <dbl> <int> #> 1 4 11 #> 2 6 7 #> 3 8 14 #> #> tally> # count() is a short-hand for group_by() + tally() #> tally> mtcars %>% count(cyl) #> # A tibble: 3 x 2 #> cyl n #> <dbl> <int> #> 1 4 11 #> 2 6 7 #> 3 8 14 #> #> tally> # Note that if the data is already grouped, count() adds #> tally> # an additional group that is removed afterwards #> tally> mtcars %>% group_by(gear) %>% count(carb) #> # A tibble: 11 x 3 #> # Groups: gear [3] #> gear carb n #> <dbl> <dbl> <int> #> 1 3 1 3 #> 2 3 2 4 #> 3 3 3 3 #> 4 3 4 5 #> 5 4 1 4 #> 6 4 2 4 #> 7 4 4 4 #> 8 5 2 2 #> 9 5 4 1 #> 10 5 6 1 #> 11 5 8 1 #> #> tally> # add_tally() is short-hand for mutate() #> tally> mtcars %>% add_tally() #> # A tibble: 32 x 12 #> mpg cyl disp hp drat wt qsec vs am gear carb n #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 32 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 32 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 32 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 32 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 32 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 32 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 32 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 32 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 32 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 32 #> # … with 22 more rows #> #> tally> # add_count() is a short-hand for group_by() + add_tally() #> tally> mtcars %>% add_count(cyl) #> # A tibble: 32 x 12 #> mpg cyl disp hp drat wt qsec vs am gear carb n #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 7 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 7 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 11 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 7 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 14 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 7 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 14 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 11 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 11 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 7 #> # … with 22 more rows #> #> tally> # count() and tally() are designed so that you can call #> tally> # them repeatedly, each time rolling up a level of detail #> tally> species <- #> tally+ starwars %>% #> tally+ count(species, homeworld, sort = TRUE) #> #> tally> species #> # A tibble: 58 x 3 #> species homeworld n #> <chr> <chr> <int> #> 1 Human Tatooine 8 #> 2 Human NA 5 #> 3 Human Naboo 5 #> 4 Gungan Naboo 3 #> 5 Human Alderaan 3 #> 6 NA NA 2 #> 7 NA Naboo 2 #> 8 Droid NA 2 #> 9 Droid Tatooine 2 #> 10 Human Corellia 2 #> # … with 48 more rows #> #> tally> species %>% count(species, sort = TRUE) #> # A tibble: 38 x 2 #> species n #> <chr> <int> #> 1 Human 16 #> 2 NA 3 #> 3 Droid 3 #> 4 Zabrak 2 #> 5 Aleena 1 #> 6 Besalisk 1 #> 7 Cerean 1 #> 8 Chagrian 1 #> 9 Clawdite 1 #> 10 Dug 1 #> # … with 28 more rows #> #> tally> # Change the name of the newly created column: #> tally> species <- #> tally+ starwars %>% #> tally+ count(species, homeworld, sort = TRUE, name = "n_species_by_homeworld") #> #> tally> species #> # A tibble: 58 x 4 #> species homeworld name n #> <chr> <chr> <chr> <int> #> 1 Human Tatooine n_species_by_homeworld 8 #> 2 Human NA n_species_by_homeworld 5 #> 3 Human Naboo n_species_by_homeworld 5 #> 4 Gungan Naboo n_species_by_homeworld 3 #> 5 Human Alderaan n_species_by_homeworld 3 #> 6 NA NA n_species_by_homeworld 2 #> 7 NA Naboo n_species_by_homeworld 2 #> 8 Droid NA n_species_by_homeworld 2 #> 9 Droid Tatooine n_species_by_homeworld 2 #> 10 Human Corellia n_species_by_homeworld 2 #> # … with 48 more rows #> #> tally> species %>% #> tally+ count(species, sort = TRUE, name = "n_species") #> # A tibble: 38 x 3 #> species name n #> <chr> <chr> <int> #> 1 Human n_species 16 #> 2 NA n_species 3 #> 3 Droid n_species 3 #> 4 Zabrak n_species 2 #> 5 Aleena n_species 1 #> 6 Besalisk n_species 1 #> 7 Cerean n_species 1 #> 8 Chagrian n_species 1 #> 9 Clawdite n_species 1 #> 10 Dug n_species 1 #> # … with 28 more rows #> #> tally> # add_count() is useful for groupwise filtering #> tally> # e.g.: show details for species that have a single member #> tally> starwars %>% #> tally+ add_count(species) %>% #> tally+ filter(n == 1) #> # A tibble: 29 x 14 #> name height mass hair_color skin_color eye_color birth_year gender #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> #> 1 Gree… 173 74 NA green black 44 male #> 2 Jabb… 175 1358 NA green-tan… orange 600 herma… #> 3 Yoda 66 17 white green brown 896 male #> 4 Bossk 190 113 none green red 53 male #> 5 Ackb… 180 83 none brown mot… orange 41 male #> 6 Wick… 88 20 brown brown brown 8 male #> 7 Nien… 160 68 none grey black NA male #> 8 Nute… 191 90 none mottled g… red NA male #> 9 Watto 137 NA black blue, grey yellow NA male #> 10 Sebu… 112 40 none grey, red orange NA male #> # … with 19 more rows, and 6 more variables: homeworld <chr>, species <chr>, #> # films <list>, vehicles <list>, starships <list>, n <int>