Aggregation with dplyr: summarise and summarise_each

We use summarise() with aggregate functions, which take a vector of values and return a single number. Function summarise_each() offers an alternative approach to summarise() with identical results.

This post aims to compare the behavior of summarise() and summarise_each() considering two factors we can take under control:

1. How many variables to manipulate

• 1A. single variable
• 1B. more than a variable

2. How many functions to apply to each variable

• 2A. single function
• 2B. more than one function

resulting in the following four cases:

• Case 1: apply one function to one variable
• Case 2: apply many functions to one variable
• Case 3: apply one function to many variables
• Case 4: apply many functions to many variables

These four cases will be also tested with and without a group_by() option.

The mtcars data frame

For this article we will use the well known mtcars data frame.

We will first transform it into a tbl_df object; no change will occur to the standard data.frame object but a much better print method will be available.

Finally, to keep this article tidy and clean we will select only four variables of interest

mtcars % 
  tbl_df() %>% 
  select(cyl , mpg, disp)

# without group
mtcars %>% 
  summarise (mean_mpg = mean(mpg))

## Source: local data frame [1 x 1]
## 
##   mean_mpg
##      (dbl)
## 1 20.09062

# with  group
mtcars %>% 
  group_by(cyl) %>% 
  summarise (mean_mpg = mean(mpg))

## Source: local data frame [3 x 2]
## 
##     cyl mean_mpg
##   (dbl)    (dbl)
## 1     4 26.66364
## 2     6 19.74286
## 3     8 15.10000

# without group

mtcars %>%  ...read more
Source:: r-bloggers.com