count_data returns the number and percentage of observations for
categorical variables.
count_data(data, ..., na.rm = FALSE)
| data | A data frame. |
|---|---|
| ... | One or more unquoted (categorical) column names from the data frame, separated by commas. |
| na.rm | Logical. Should missing values (including NaN) be removed? |
The data frame can be grouped using dplyr's group_by
so that the number of observations will be calculated within each group
level.
# Load dplyr for access to the %>% operator and group_by() library(dplyr) # 1 variable count_data(quote_source, source)#> # A tibble: 2 x 3 #> source n pct #> <chr> <int> <dbl> #> 1 Bin Laden 3101 48.9 #> 2 Washington 3242 51.1# 2 variables count_data(quote_source, source, sex)#> # A tibble: 6 x 4 #> source sex n pct #> <chr> <chr> <int> <dbl> #> 1 Bin Laden female 2067 32.6 #> 2 Bin Laden male 1029 16.2 #> 3 Bin Laden NA 5 0.0788 #> 4 Washington female 2206 34.8 #> 5 Washington male 1031 16.3 #> 6 Washington NA 5 0.0788# Ignore missing values count_data(quote_source, source, sex, na.rm = TRUE)#> # A tibble: 4 x 4 #> source sex n pct #> <chr> <chr> <int> <dbl> #> 1 Bin Laden female 2067 32.6 #> 2 Bin Laden male 1029 16.2 #> 3 Washington female 2206 34.8 #> 4 Washington male 1031 16.3# Use group_by() to get percentages within each group quote_source %>% group_by(source) %>% count_data(sex)#> # A tibble: 6 x 4 #> # Groups: source [2] #> source sex n pct #> <chr> <chr> <int> <dbl> #> 1 Bin Laden female 2067 66.7 #> 2 Bin Laden male 1029 33.2 #> 3 Bin Laden NA 5 0.161 #> 4 Washington female 2206 68.0 #> 5 Washington male 1031 31.8 #> 6 Washington NA 5 0.154