![]() ![]() One way, would be to use the summarise function together with the n() function, which counts the number of rows in each group. But what if you want a really quick count of all the records in different groups, a frequency table. drop = FALSE ) #> # A tibble: 4 × 2 #> type n #> #> 1 a 3 #> 2 b 0 #> 3 c 1 #> 4 NA 1 # Or, using `group_by()`: df2 %>% group_by ( type. So far, we have created custom summary tables with means and standard deviations etc. ![]() This is useful # when the data has already been aggregated once df % count ( gender ) #> # A tibble: 2 × 2 #> gender n #> #> 1 female 2 #> 2 male 1 # counts runs: df %>% count ( gender, wt = runs ) #> # A tibble: 2 × 2 #> gender n #> #> 1 female 5 #> 2 male 10 # When factors are involved, `.drop = FALSE` can be used to retain factor # levels that don't appear in the data df2 % count ( type ) #> # A tibble: 3 × 2 #> type n #> #> 1 a 3 #> 2 c 1 #> 3 NA 1 df2 %>% count ( type. For example, if we have 5 bananas, 6 guava, 10 pomegranates then the relative frequency of banana would be 5 divided by the total sum of 5, 6, and 10 that is 21 hence it can be also called proportional frequency. # count() is a convenient way to get a sense of the distribution of # values in a dataset starwars %>% count ( species ) #> # A tibble: 38 × 2 #> species n #> #> 1 Aleena 1 #> 2 Besalisk 1 #> 3 Cerean 1 #> 4 Chagrian 1 #> 5 Clawdite 1 #> 6 Droid 6 #> 7 Dug 1 #> 8 Ewok 1 #> 9 Geonosian 1 #> 10 Gungan 3 #> # ℹ 28 more rows starwars %>% count ( species, sort = TRUE ) #> # A tibble: 38 × 2 #> species n #> #> 1 Human 35 #> 2 Droid 6 #> 3 NA 4 #> 4 Gungan 3 #> 5 Kaminoan 2 #> 6 Mirialan 2 #> 7 Twi'lek 2 #> 8 Wookiee 2 #> 9 Zabrak 2 #> 10 Aleena 1 #> # ℹ 28 more rows starwars %>% count ( sex, gender, sort = TRUE ) #> # A tibble: 6 × 3 #> sex gender n #> #> 1 male masculine 60 #> 2 female feminine 16 #> 3 none masculine 5 #> 4 NA NA 4 #> 5 hermaphroditic masculine 1 #> 6 none feminine 1 starwars %>% count (birth_decade = round ( birth_year, - 1 ) ) #> # A tibble: 15 × 2 #> birth_decade n #> #> 1 10 1 #> 2 20 6 #> 3 30 4 #> 4 40 6 #> 5 50 8 #> 6 60 4 #> 7 70 4 #> 8 80 2 #> 9 90 3 #> 10 100 1 #> 11 110 1 #> 12 200 1 #> 13 600 1 #> 14 900 1 #> 15 NA 44 # use the `wt` argument to perform a weighted count. How to create relative frequency table using dplyr in R - The relative frequency is the proportion of something out of total. ![]()
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