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
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
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
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
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
Source: Stackoverflow.com