1 Load packages
library(tidyverse) # data wrangling2 Problem
Consider the following situation:
mtcars |>
group_by(high_hp = hp > 1000) |>
count(high_hp)
#> # A tibble: 1 × 2
#> # Groups: high_hp [1]
#> high_hp n
#> <lgl> <int>
#> 1 FALSE 32The summary table does not show the level TRUE, as it is not occuring in the data.
This can be problematic: If the data is unknown before summarizing and you would expect that both/all levels (TRUE, FALSE) occur. Just imagine that a subsequent function will count the level TRUE and the level FALSE. If one level is missing, your system may brake down.
3 Solution
Using dplyr and count or summarize, one solution is to turn the grouping variable in a factor,
and then using the parameter .drop = FALSE in count or summarise.
Here’s an example for count:
mtcars |>
group_by(high_hp = hp > 1000) |>
mutate(high_hp = factor(high_hp, levels = c(FALSE, TRUE))) |>
count(high_hp, .drop = FALSE)
#> # A tibble: 2 × 2
#> # Groups: high_hp [2]
#> high_hp n
#> <fct> <int>
#> 1 FALSE 32
#> 2 TRUE 0And here a similar one for summarise:
mtcars %>%
group_by(hp_over_1000 = factor(hp > 1000, levels = c(FALSE, TRUE)), .drop = FALSE) %>%
summarise(mean_hp = mean(hp))
#> # A tibble: 2 × 2
#> hp_over_1000 mean_hp
#> <fct> <dbl>
#> 1 FALSE 147.
#> 2 TRUE NaNIf we would like to make sure that the resulting variable is not NaN, then we could use complete:
mtcars %>%
group_by(hp_over_1000 = factor(hp > 1000, levels = c(FALSE, TRUE)), .drop = FALSE) %>%
summarise(mean_hp = mean(hp)) %>%
complete(hp_over_1000, fill = list(mean_hp = NA))
#> # A tibble: 2 × 2
#> hp_over_1000 mean_hp
#> <fct> <dbl>
#> 1 FALSE 147.
#> 2 TRUE NA4 Reproducibility
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