summarise_at using different functions for different variables

Question

When I use group_by and summarise in dplyr, I can naturally apply different summary functions to different variables. For instance:

    library(tidyverse)

    df <- tribble(
      ~category,   ~x,  ~y,  ~z,
      #----------------------
          'a',      4,   6,   8,
          'a',      7,   3,   0,
          'a',      7,   9,   0,
          'b',      2,   8,   8,
          'b',      5,   1,   8,
          'b',      8,   0,   1,
          'c',      2,   1,   1,
          'c',      3,   8,   0,
          'c',      1,   9,   1
     )

    df %>% group_by(category) %>% summarize(
      x=mean(x),
      y=median(y),
      z=first(z)
    )

results in output:

    # A tibble: 3 x 4
      category     x     y     z
         <chr> <dbl> <dbl> <dbl>
    1        a     6     6     8
    2        b     5     1     8
    3        c     2     8     1

My question is, how would I do this with summarise_at? Obviously for this example it's unnecessary, but assume I have lots of variables that I want to take the mean of, lots of medians, etc.

Do I lose this functionality once I move to summarise_at? Do I have to use all functions on all groups of variables and then throw away the ones I don't want?

Perhaps I'm just missing something, but I can't figure it out, and I don't see any examples of this in the documentation. Any help is appreciated.

The base Map functionality can do this, Map(function(f,v) f(v), c(mean,median,first), df[c("x","y","z")]) for instance. Maybe purrr's map could do something similar? — thelatemail
Yes, I was wondering if purrr could offer us a way out of this. It's worth investigating. But in your example aren't you just applying all functions to all variables? And how would you use this with group_by? — David Pepper
Nope, I'm applying each function in turn to each variable with Map - see the results of mean(df$x); median(df$y); first(df$z) and compare to the Map code. — thelatemail
OK, I see what you mean, but my question here is the same as to ycw: what if I have three variables for the first function, ten for the second and one for the third? And this looks like a substitute for summarise_at rather than something to put inside it. I guess I'm asking for the complete code, because when I apply your suggestion to my sample data frame I don't get the answer I'm looking for. — David Pepper

www www · Accepted Answer · 2017-09-13T03:30:39

Here is one idea.

library(tidyverse)

df_mean <- df %>%
  group_by(category) %>%
  summarize_at(vars(x), funs(mean(.)))

df_median <- df %>%
  group_by(category) %>%
  summarize_at(vars(y), funs(median(.)))

df_first <- df %>%
  group_by(category) %>%
  summarize_at(vars(z), funs(first(.)))

df_summary <- reduce(list(df_mean, df_median, df_first), 
                     left_join, by = "category")

Like you said, there is no need to use summarise_at for this example. However, if you have a lot of columns need to be summarized by different functions, this strategy may work. You will need to specify the columns in the vars(...) for each summarize_at. The rule is the same as the dplyr::select function.

Update

Here is another idea. Define a function which modifies the summarise_at function, and then use map2 to apply this function with a look-up list showing variables and associated functions to apply. In this example, I applied mean to x and y column and median to z.

# Define a function
summarise_at_fun <- function(variable, func, data){
  data2 <- data %>%
    summarise_at(vars(variable), funs(get(func)(.)))
  return(data2)
}

# Group the data
df2 <- df %>% group_by(category)

# Create a look-up list with function names and variable to apply
look_list <- list(mean = c("x", "y"),
                  median = "z")

# Apply the summarise_at_fun
map2(look_list, names(look_list), summarise_at_fun, data = df2) %>%
  reduce(left_join, by = "category")

# A tibble: 3 x 4
  category     x     y     z
     <chr> <dbl> <dbl> <dbl>
1        a     6     6     0
2        b     5     3     8
3        c     2     6     1

summarise_at using different functions for different variables

2 Answers

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