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
#
# 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
#
# 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
