Count the number of observations — count

count_data returns the number and percentage of observations for categorical variables.

count_data(data, ..., na.rm = FALSE)

Arguments

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?

Details

The data frame can be grouped using dplyr's group_by so that the number of observations will be calculated within each group level.

Examples

# Load dplyr for access to the %>% operator and group_by()
library(dplyr)

# 1 variable
count_data(quote_source, source)
#> # A tibble: 2 × 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 × 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 × 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 × 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