The tab function provides a frequency table for a categorical variable. Many options are available.
Creating a frequency table
The cardata data frame contains information on 11,914 vehicles, including make, model, and features and price. First, let’s tabulate the number of automobiles by drive type.
tab(cardata, driven_wheels)
#> level n percent
#> all wheel drive 2353 19.75%
#> four wheel drive 1403 11.78%
#> front wheel drive 4787 40.18%
#> rear wheel drive 3371 28.29%Next, lets add a Total category.
tab(cardata, driven_wheels, total=TRUE)
#> level n percent
#> all wheel drive 2353 19.75%
#> four wheel drive 1403 11.78%
#> front wheel drive 4787 40.18%
#> rear wheel drive 3371 28.29%
#> Total 11914 100%Sorting by category
Next, we’ll tabulate the cars by driven_wheels and sort the results in descending order.
tab(cardata, driven_wheels, total=TRUE, sort=TRUE)
#> level n percent
#> front wheel drive 4787 40.18%
#> rear wheel drive 3371 28.29%
#> all wheel drive 2353 19.75%
#> four wheel drive 1403 11.78%
#> Total 11914 100%Collapsing categories
Next, let’s tabulate the automobiles by make, sorting from largest number to smallest number. We’ll also remove all missing observations from the data set, add a total row, and limit the makes to the 10 most frequent, plus an “Other” category.
tab(cardata, make, sort = TRUE, na.rm = TRUE, total = TRUE, maxcat=10)
#> level n percent
#> Chevrolet 1115 9.44%
#> Ford 868 7.35%
#> Volkswagen 805 6.81%
#> Toyota 743 6.29%
#> Dodge 626 5.3%
#> Nissan 548 4.64%
#> GMC 515 4.36%
#> Honda 447 3.78%
#> Mazda 403 3.41%
#> Cadillac 397 3.36%
#> Other 5348 45.26%
#> Total 11815 100%Finally, let’s list the makes that have at least 5% of the cars, combining the rest into an “Other” category.
tab(cardata, make, minp=0.05)
#> level n percent
#> Chevrolet 1123 9.43%
#> Dodge 626 5.25%
#> Ford 881 7.39%
#> Toyota 746 6.26%
#> Volkswagen 809 6.79%
#> Other 7729 64.87%
