Can different parts of dplyr::summarize() be computed conditionally?

Question

Is it possible to have conditional statements operate on different parts of dplyr::summarize()?

Imagine I am working with the iris data and outputting a summary and I want to only include the mean of Sepal.Length when requested. So I could do something like:

data(iris)
include_length = T
if (include_length) {
  iris %>% 
    group_by(Species) %>%
    summarize(mean_sepal_width = mean(Sepal.Width), mean_sepal_length = mean(Sepal.Length))
} else {
  iris %>% 
    group_by(Species) %>%
    summarize(mean_sepal_width = mean(Sepal.Width))

}

But is there a way to implement the conditional within the pipeline so that it does not need to be duplicated?

Answer 1

You can use the .dots parameter of dplyr's SE functions to evauluate programmatically, e.g.

library(dplyr)

take_means <- function(include_length){
    iris %>% 
        group_by(Species) %>%
        summarize_(mean_sepal_width = ~mean(Sepal.Width), 
                   .dots = if(include_length){
                       list(mean_sepal_length = ~mean(Sepal.Length))
                   })
}

take_means(TRUE)
#> # A tibble: 3 × 3
#>      Species mean_sepal_width mean_sepal_length
#>       <fctr>            <dbl>             <dbl>
#> 1     setosa            3.428             5.006
#> 2 versicolor            2.770             5.936
#> 3  virginica            2.974             6.588

take_means(FALSE)
#> # A tibble: 3 × 2
#>      Species mean_sepal_width
#>       <fctr>            <dbl>
#> 1     setosa            3.428
#> 2 versicolor            2.770
#> 3  virginica            2.974