Pass multiple columns in dataframe into function at once in R

Question

After much searching, I can't seem to figure this out. Trying to write a function that:

• takes a data frame, db
• groups the data frame by var1
• returns the mean and sd by group on several different columns

Here is my function,

myfun <- function(db,var1, ...) {

  var1 <- enquo(var1)
  var2 <- quos(...)

  for (i in var2) {

  db %>% 
    group_by(!!var1) %>%       
    summarise(mean_var = mean(!!!var2))

}}

when I pass the following, nothing returns

myfun(data, group, age, bmi)

Ideally, I would like to group both age and bmi by group and return the mean and sd for each. In the future, I would like to pass many more columns from data into the function...

The output would be similar to summaryBy from doby package, but on many columns at once and would look like:

Group   age.mean    age.sd
0
1
        bmi.mean    bmi.sd
0
1

Answer 1

Your loop appears to be unnecessary (you aren't doing anything with i ). Instead, you could use summarize_at to achieve the effect you want:

myfun <- function(db,var1, ...) {

  var1 <- enquo(var1)
  var2 <- quos(...)

    db %>% 
      group_by(!!var1) %>%       
      summarise_at(vars(!!!var2), c(mean = mean, sd = sd))

  }

And if we test it out with diamonds dataset:

myfun(diamonds, cut, x, z)

  cut       x_mean z_mean  x_sd  z_sd
  <ord>      <dbl>  <dbl> <dbl> <dbl>
1 Fair        6.25   3.98 0.964 0.652
2 Good        5.84   3.64 1.06  0.655
3 Very Good   5.74   3.56 1.10  0.730
4 Premium     5.97   3.65 1.19  0.731
5 Ideal       5.51   3.40 1.06  0.658

To get the formatting closer to what you had in mind in your original post, we can use a bit of tidyr magic:

myfun <- function(db,var1, ...) {

  var1 <- enquo(var1)
  var2 <- quos(...)

  db %>% 
    group_by(!!var1) %>%       
    summarise_at(vars(!!!var2), c(mean = mean, sd = sd)) %>% 
    gather(variable, value, -(!!var1)) %>% 
    separate(variable, c('variable', 'measure'), sep = '_') %>% 
    spread(measure, value) %>% 
    arrange(variable, !!var1)

}

   cut       variable  mean    sd
   <ord>     <chr>    <dbl> <dbl>
 1 Fair      x         6.25 0.964
 2 Good      x         5.84 1.06 
 3 Very Good x         5.74 1.10 
 4 Premium   x         5.97 1.19 
 5 Ideal     x         5.51 1.06 
 6 Fair      z         3.98 0.652
 7 Good      z         3.64 0.655
 8 Very Good z         3.56 0.730
 9 Premium   z         3.65 0.731
10 Ideal     z         3.40 0.658