[r] Summarizing multiple columns with dplyr?

I'm struggling a bit with the dplyr-syntax. I have a data frame with different variables and one grouping variable. Now I want to calculate the mean for each column within each group, using dplyr in R.

df <- data.frame(
    a = sample(1:5, n, replace = TRUE), 
    b = sample(1:5, n, replace = TRUE), 
    c = sample(1:5, n, replace = TRUE), 
    d = sample(1:5, n, replace = TRUE), 
    grp = sample(1:3, n, replace = TRUE)
)
df %>% group_by(grp) %>% summarise(mean(a))

This gives me the mean for column "a" for each group indicated by "grp".

My question is: is it possible to get the means for each column within each group at once? Or do I have to repeat df %>% group_by(grp) %>% summarise(mean(a)) for each column?

What I would like to have is something like

df %>% group_by(grp) %>% summarise(mean(a:d)) # "mean(a:d)" does not work

This question is related to r dplyr aggregate

The answer is


We can summarize by using summarize_at, summarize_all and summarize_if on dplyr 0.7.4. We can set the multiple columns and functions by using vars and funs argument as below code. The left-hand side of funs formula is assigned to suffix of summarized vars. In the dplyr 0.7.4, summarise_each(and mutate_each) is already deprecated, so we cannot use these functions.

options(scipen = 100, dplyr.width = Inf, dplyr.print_max = Inf)

library(dplyr)
packageVersion("dplyr")
# [1] ‘0.7.4’

set.seed(123)
df <- data_frame(
  a = sample(1:5, 10, replace=T), 
  b = sample(1:5, 10, replace=T), 
  c = sample(1:5, 10, replace=T), 
  d = sample(1:5, 10, replace=T), 
  grp = as.character(sample(1:3, 10, replace=T)) # For convenience, specify character type
)

df %>% group_by(grp) %>% 
  summarise_each(.vars = letters[1:4],
                 .funs = c(mean="mean"))
# `summarise_each()` is deprecated.
# Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
# To map `funs` over a selection of variables, use `summarise_at()`
# Error: Strings must match column names. Unknown columns: mean

You should change to the following code. The following codes all have the same result.

# summarise_at
df %>% group_by(grp) %>% 
  summarise_at(.vars = letters[1:4],
               .funs = c(mean="mean"))

df %>% group_by(grp) %>% 
  summarise_at(.vars = names(.)[1:4],
               .funs = c(mean="mean"))

df %>% group_by(grp) %>% 
  summarise_at(.vars = vars(a,b,c,d),
               .funs = c(mean="mean"))

# summarise_all
df %>% group_by(grp) %>% 
  summarise_all(.funs = c(mean="mean"))

# summarise_if
df %>% group_by(grp) %>% 
  summarise_if(.predicate = function(x) is.numeric(x),
               .funs = funs(mean="mean"))
# A tibble: 3 x 5
# grp a_mean b_mean c_mean d_mean
# <chr>  <dbl>  <dbl>  <dbl>  <dbl>
# 1     1   2.80   3.00    3.6   3.00
# 2     2   4.25   2.75    4.0   3.75
# 3     3   3.00   5.00    1.0   2.00

You can also have multiple functions.

df %>% group_by(grp) %>% 
  summarise_at(.vars = letters[1:2],
               .funs = c(Mean="mean", Sd="sd"))
# A tibble: 3 x 5
# grp a_Mean b_Mean      a_Sd     b_Sd
# <chr>  <dbl>  <dbl>     <dbl>    <dbl>
# 1     1   2.80   3.00 1.4832397 1.870829
# 2     2   4.25   2.75 0.9574271 1.258306
# 3     3   3.00   5.00        NA       NA

All the examples are great, but I figure I'd add one more to show how working in a "tidy" format simplifies things. Right now the data frame is in "wide" format meaning the variables "a" through "d" are represented in columns. To get to a "tidy" (or long) format, you can use gather() from the tidyr package which shifts the variables in columns "a" through "d" into rows. Then you use the group_by() and summarize() functions to get the mean of each group. If you want to present the data in a wide format, just tack on an additional call to the spread() function.


library(tidyverse)

# Create reproducible df
set.seed(101)
df <- tibble(a   = sample(1:5, 10, replace=T), 
             b   = sample(1:5, 10, replace=T), 
             c   = sample(1:5, 10, replace=T), 
             d   = sample(1:5, 10, replace=T), 
             grp = sample(1:3, 10, replace=T))

# Convert to tidy format using gather
df %>%
    gather(key = variable, value = value, a:d) %>%
    group_by(grp, variable) %>%
    summarize(mean = mean(value)) %>%
    spread(variable, mean)
#> Source: local data frame [3 x 5]
#> Groups: grp [3]
#> 
#>     grp        a     b        c        d
#> * <int>    <dbl> <dbl>    <dbl>    <dbl>
#> 1     1 3.000000   3.5 3.250000 3.250000
#> 2     2 1.666667   4.0 4.666667 2.666667
#> 3     3 3.333333   3.0 2.333333 2.333333

You can simply pass more arguments to summarise:

df %>% group_by(grp) %>% summarise(mean(a), mean(b), mean(c), mean(d))

Source: local data frame [3 x 5]

  grp  mean(a)  mean(b)  mean(c) mean(d)
1   1 2.500000 3.500000 2.000000     3.0
2   2 3.800000 3.200000 3.200000     2.8
3   3 3.666667 3.333333 2.333333     3.0

For completeness: with dplyr v0.2 ddply with colwise will also do this:

> ddply(df, .(grp), colwise(mean))
  grp        a    b        c        d
1   1 4.333333 4.00 1.000000 2.000000
2   2 2.000000 2.75 2.750000 2.750000
3   3 3.000000 4.00 4.333333 3.666667

but it is slower, at least in this case:

> microbenchmark(ddply(df, .(grp), colwise(mean)), 
                  df %>% group_by(grp) %>% summarise_each(funs(mean)))
Unit: milliseconds
                                            expr      min       lq     mean
                ddply(df, .(grp), colwise(mean))     3.278002 3.331744 3.533835
 df %>% group_by(grp) %>% summarise_each(funs(mean)) 1.001789 1.031528 1.109337

   median       uq      max neval
 3.353633 3.378089 7.592209   100
 1.121954 1.133428 2.292216   100

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