Count observations for multiple variables

This variant of dplyr::count() returns frequencies and (optionally) proportions for each column passed to ....

Usage

count_multiple(
  .data,
  ...,
  .pct = TRUE,
  wt = NULL,
  sort = FALSE,
  name = NULL,
  na.rm = FALSE,
  .by = NULL,
  .drop = TRUE
)

Arguments

.data

A data frame.

...

Columns to count frequencies for. Can be named expressions.

.pct

If TRUE (default), include percentages.

sort

If TRUE, sort output by frequency.

name

Name for the frequency column. Default is \"n\".

na.rm

If TRUE, remove rows with NA values.

.by

a selection of columns to group by for just this operation, functioning as an alternative to dplyr::group_by(). Percentages will be computed within each group rather than for the grand total. See examples.

.drop

If TRUE (default), drop unused factor levels.

Value

A data frame with columns:

• Any grouping variables from the input data or specified in .by.

• Variable: The name of the column being counted.

• Value: The unique values from the counted column.

• n: The frequency of each unique value.

• pct: (If .pct = TRUE) The percentage each count represents within its variable.

Examples

iris %>%
  count_multiple(Species, Sepal.Length > 5)
#> # A tibble: 5 × 4
#>   Variable         Value          n   pct
#>   <chr>            <fct>      <int> <dbl>
#> 1 Species          setosa        50 0.333
#> 2 Species          versicolor    50 0.333
#> 3 Species          virginica     50 0.333
#> 4 Sepal.Length > 5 FALSE         32 0.213
#> 5 Sepal.Length > 5 TRUE         118 0.787

## note effects of grouping
# no grouping
ggplot2::mpg %>%
  count_multiple(year, drv, cyl)
#> # A tibble: 9 × 4
#>   Variable Value     n    pct
#>   <chr>    <fct> <int>  <dbl>
#> 1 year     1999    117 0.5   
#> 2 year     2008    117 0.5   
#> 3 drv      4       103 0.440 
#> 4 drv      f       106 0.453 
#> 5 drv      r        25 0.107 
#> 6 cyl      4        81 0.346 
#> 7 cyl      5         4 0.0171
#> 8 cyl      6        79 0.338 
#> 9 cyl      8        70 0.299 

# grouping w `group_by()`: counts and % nested within groups, output is grouped
ggplot2::mpg %>%
  dplyr::group_by(year) %>%
  count_multiple(drv, cyl)
#> # A tibble: 13 × 5
#> # Groups:   year [2]
#>     year Variable Value     n    pct
#>    <int> <chr>    <fct> <int>  <dbl>
#>  1  1999 drv      4        49 0.419 
#>  2  1999 drv      f        57 0.487 
#>  3  1999 drv      r        11 0.0940
#>  4  2008 drv      4        54 0.462 
#>  5  2008 drv      f        49 0.419 
#>  6  2008 drv      r        14 0.120 
#>  7  1999 cyl      4        45 0.385 
#>  8  1999 cyl      6        45 0.385 
#>  9  1999 cyl      8        27 0.231 
#> 10  2008 cyl      4        36 0.308 
#> 11  2008 cyl      5         4 0.0342
#> 12  2008 cyl      6        34 0.291 
#> 13  2008 cyl      8        43 0.368 

# grouping w `.by`: counts and % nested within groups, output isn't grouped
ggplot2::mpg %>%
  count_multiple(drv, cyl, .by = year)
#> # A tibble: 13 × 5
#>     year Variable Value     n    pct
#>    <int> <chr>    <fct> <int>  <dbl>
#>  1  1999 drv      4        49 0.419 
#>  2  1999 drv      f        57 0.487 
#>  3  1999 drv      r        11 0.0940
#>  4  2008 drv      4        54 0.462 
#>  5  2008 drv      f        49 0.419 
#>  6  2008 drv      r        14 0.120 
#>  7  1999 cyl      4        45 0.385 
#>  8  1999 cyl      6        45 0.385 
#>  9  1999 cyl      8        27 0.231 
#> 10  2008 cyl      4        36 0.308 
#> 11  2008 cyl      5         4 0.0342
#> 12  2008 cyl      6        34 0.291 
#> 13  2008 cyl      8        43 0.368