How to make custom function in Tidyverse more readable?

Question

I have a data.frame with different variables that needs to be summarised with different measures.

I'm looking for a easy readable equivalent of,

baseline_table <- function(data,var) {
        
        data %>% 
                group_by(Species) %>% 
                summarise(
                        !!sym(paste(var, "_mean", sep = "")) := !!sym(var) %>% mean(na.rm = TRUE),
                        !!sym(paste(var, "_sd", sep = ""))   := !!sym(var) %>% sd(na.rm = TRUE)
                )
        
}

iris %>% 
        baseline_table(var = "Sepal.Length")

Answer 1

You can use glue syntax to make it more readable.

baseline_table <- function(data, var) {
        
        data %>% 
                group_by(Species) %>% 
                summarise(
                        "{{var}}_mean" := mean({{ var }}, na.rm = TRUE),
                        "{{var}}_sd"   := sd({{ var }}, na.rm = TRUE)
                )
        
}

iris %>% 
        baseline_table(var = Sepal.Length)

Answer 2

You can use across -

library(dplyr)

baseline_table <- function(data,var) {
  data %>% 
    group_by(Species) %>% 
    summarise(across(all_of(var), list(mean = mean, sd = sd)))
}

iris %>% baseline_table(var = "Sepal.Length")

#  Species    Sepal.Length_mean Sepal.Length_sd
#  <fct>                  <dbl>           <dbl>
#1 setosa                  5.01           0.352
#2 versicolor              5.94           0.516
#3 virginica               6.59           0.636

The benefit using across is that you can apply this to more than one column.

iris %>% baseline_table(var = c("Sepal.Length", "Sepal.Width"))

#  Species    Sepal.Length_mean Sepal.Length_sd Sepal.Width_mean Sepal.Width_sd
#  <fct>                  <dbl>           <dbl>            <dbl>          <dbl>
#1 setosa                  5.01           0.352             3.43          0.379
#2 versicolor              5.94           0.516             2.77          0.314
#3 virginica               6.59           0.636             2.97          0.322

If you want more customised column names take a look at .names parameter in ?across .