dplyr - summary table for multiple variables - r

Question

How to create simple summary statistics using dplyr from multiple variables? Using the summarise_each function seems to be the way to go, however, when applying multiple functions to multiple columns, the result is a wide, hard-to-read data frame.

Answer 1

Use dplyr in combination with tidyr to reshape the end result.
library(dplyr)
library(tidyr)
df <- tbl_df(mtcars)
df.sum <- df %>%
select(mpg, cyl, vs, am, gear, carb) %>% # select variables to summarise
summarise_each(funs(min = min,
q25 = quantile(., 0.25),
median = median,
q75 = quantile(., 0.75),
max = max,
mean = mean,
sd = sd))
# the result is a wide data frame
> dim(df.sum)
[1] 1 42
# reshape it using tidyr functions
df.stats.tidy <- df.sum %>% gather(stat, val) %>%
separate(stat, into = c("var", "stat"), sep = "_") %>%
spread(stat, val) %>%
select(var, min, q25, median, q75, max, mean, sd) # reorder columns
> print(df.stats.tidy)
var min q25 median q75 max mean sd
1 am 0.0 0.000 0.0 1.0 1.0 0.40625 0.4989909
2 carb 1.0 2.000 2.0 4.0 8.0 2.81250 1.6152000
3 cyl 4.0 4.000 6.0 8.0 8.0 6.18750 1.7859216
4 gear 3.0 3.000 4.0 4.0 5.0 3.68750 0.7378041
5 mpg 10.4 15.425 19.2 22.8 33.9 20.09062 6.0269481
6 vs 0.0 0.000 0.0 1.0 1.0 0.43750 0.5040161

Answer 2

A potentially easy solution could created with broom::tidy and purrr::map_df. broom::tidy summarises key objects from statistical ouput into a tibble. purrr::map_df applies function to each element, in this case a column and returns a tibble.
Example
library(tidyverse)
mtcars %>%
select(mpg, cyl, vs, am, gear, carb) %>%
map_df(.f = ~ broom::tidy(summary(.x)), .id = "variable")
Results
# A tibble: 6 x 7
# variable minimum q1 median mean q3 maximum
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 mpg 10.4 15.4 19.2 20.1 22.8 33.9
# 2 cyl 4 4 6 6.19 8 8
# 3 vs 0 0 0 0.438 1 1
# 4 am 0 0 0 0.406 1 1
# 5 gear 3 3 4 3.69 4 5
# 6 carb 1 2 2 2.81 4 8

Answer 3

I liked paljenczy's idea of just using dplyr/tidy and getting the table in a data.frame/tibble before formatting it. But I ran into robustness issues: Because it relies on parsing variable names it choked on columns with underscores in the names. After trying to fix this within the dplyr framework it seemed like it would always be somewhat fragile because it relied on string parsing.
So in the end I decided on using psych::describe() which is a function designed for exactly this thing. It doesn't do completely arbitrary functions, but pretty much anything one would realistically want to do. A full example duplicating the previous solutions is included below, combining psych::describe() with some tidyverse stuff to get the exact tibble we are looking for.
It is worth noting that this answer has been updated to reflect the changed behavior of as_tibble() with regards to how it handles rownames in data.frames:
library(psych)
library(tidyverse)
# Create an extended version with a bunch of stats
d.summary.extended <- mtcars %>%
select(mpg, cyl, vs, am, gear, carb) %>%
psych::describe(quant=c(.25,.75)) %>%
as_tibble(rownames="rowname") %>%
print()
<OUTPUT>
# A tibble: 6 x 16
rowname vars n mean sd median trimmed mad min max range skew kurtosis se Q0.25 Q0.75
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 1 32 20.09062 6.0269481 19.2 19.6961538 5.41149 10.4 33.9 23.5 0.6106550 -0.372766 1.06542396 15.425 22.8
2 cyl 2 32 6.18750 1.7859216 6.0 6.2307692 2.96520 4.0 8.0 4.0 -0.1746119 -1.762120 0.31570933 4.000 8.0
3 vs 3 32 0.43750 0.5040161 0.0 0.4230769 0.00000 0.0 1.0 1.0 0.2402577 -2.001938 0.08909831 0.000 1.0
4 am 4 32 0.40625 0.4989909 0.0 0.3846154 0.00000 0.0 1.0 1.0 0.3640159 -1.924741 0.08820997 0.000 1.0
5 gear 5 32 3.68750 0.7378041 4.0 3.6153846 1.48260 3.0 5.0 2.0 0.5288545 -1.069751 0.13042656 3.000 4.0
6 carb 6 32 2.81250 1.6152000 2.0 2.6538462 1.48260 1.0 8.0 7.0 1.0508738 1.257043 0.28552971 2.000 4.0
</OUTPUT>
# Select stats for comparison with other solutions
d.summary <- d.summary.extended %>%
select(var=rowname, min, q25=Q0.25, median, q75=Q0.75, max, mean, sd) %>%
print()
<OUTPUT>
# A tibble: 6 x 8
var min q25 median q75 max mean sd
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 mpg 10.4 15.425 19.2 22.8 33.9 20.09062 6.0269481
2 cyl 4.0 4.000 6.0 8.0 8.0 6.18750 1.7859216
3 vs 0.0 0.000 0.0 1.0 1.0 0.43750 0.5040161
4 am 0.0 0.000 0.0 1.0 1.0 0.40625 0.4989909
5 gear 3.0 3.000 4.0 4.0 5.0 3.68750 0.7378041
6 carb 1.0 2.000 2.0 4.0 8.0 2.81250 1.6152000
</OUTPUT>

Answer 4

If you want to create a summary table for publication (not for further calculations) you may want to look at the excellent stargazer package.
df <- data.frame(mtcars)
cols <- c('mpg', 'cyl', 'vs', 'am', 'gear', 'carb')
stargazer(
df[, cols], type = "text",
summary.stat = c("min", "p25", "median", "p75", "max", "median", "sd")
)
================================================================
Statistic Min Pctl(25) Median Pctl(75) Max Median St. Dev.
----------------------------------------------------------------
mpg 10.400 15.430 19.200 22.800 33.900 19.200 6.027
cyl 4 4 6 8 8 6 1.786
vs 0 0 0 1 1 0 0.504
am 0 0 0 1 1 0 0.499
gear 3 3 4 4 5 4 0.738
carb 1 2 2 4 8 2 1.615
----------------------------------------------------------------
You can change type to 'latex' and 'html' as well and save it to file with specifying the file giving 'out' argument.

Answer 5

There's a "new" package called skimr that has a function called skim() that gives wonderful output describing individual variables in a data.fame/tibble.
Try:
skimr::skim(mtcars)
and you'll get:
it is customizable and works well with pipes etc.
see ?skimr::skim()
and vignette("Using_skimr", package = "skimr")

Answer 6

You can achieve the same result using data.table as well. You might consider using it if your table is big.
dt <- data.table(mtcars)
cols <- c('mpg', 'cyl', 'vs', 'am', 'gear', 'carb')
functions <- c('min', 'q25', 'median', 'q75', 'max', 'mean', 'sd')
dt.sum <- dt[
,
lapply(
.SD,
function(x) list(
min(x), quantile(x, 0.25), median(x),
quantile(x, 0.75), max(x), mean(x), sd(x)
)
),
.SDcols = cols
]
dt.sum
mpg cyl vs am gear carb
1: 10.4 4 0 0 3 1
2: 15.43 4 0 0 3 2
3: 19.2 6 0 0 4 2
4: 22.8 8 1 1 4 4
5: 33.9 8 1 1 5 8
6: 20.09 6.188 0.4375 0.4062 3.688 2.812
7: 6.027 1.786 0.504 0.499 0.7378 1.615
# transpose and provide meaningful names
dt.sum.t <- as.data.table(t(sum))[]
setnames(dt.sum.t, names(dt.sum.t), functions)
dt.sum.t[, var := cols]
setcolorder(dt.sum.t, c("var", functions))
dt.sum.t
var min q25 median q75 max mean sd
1: mpg 10.4 15.43 19.2 22.8 33.9 20.09 6.027
2: cyl 4 4 6 8 8 6.188 1.786
3: vs 0 0 0 1 1 0.4375 0.504
4: am 0 0 0 1 1 0.4062 0.499
5: gear 3 3 4 4 5 3.688 0.7378
6: carb 1 2 2 4 8 2.812 1.615

Answer 7

Similar to the accepted answer, but tidied up a bit into a function:
summarise_continuous = function(d, cvars) {
d %>%
select(all_of(cvars)) %>%
mutate_all(as.numeric) %>%
summarise(across(all_of(cvars), list(N = ~sum(!is.na(.)),
mean = ~mean(., na.rm=T),
sd = ~sd(., na.rm=T),
median = ~median(., na.rm=T),
min = ~min(., na.rm=T),
max = ~max(., na.rm=T)))) %>%
pivot_longer(everything(),
names_to = c("variable",".value"),
names_pattern = "(.+)_(.+)") # %>%
# knitr::kable()
# uncomment these bits if you want a nicely formatted table in a .Rmd document
}
summarise_continuous(mtcars, c("mpg", "cyl", "vs", "am", "gear", "carb"))