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)

          


        

Answer 1

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

Answer 2

Since your question is about "summarise_at";

Here is what my idea is:

df %>% group_by(category) %>% 
 summarise_at(vars(x, y, z),
      funs(mean = mean, sd = sd, min = min),
      na.rm = TRUE)