dplyr summarise_all with quantile and other functions - r

Question

I have a dataframe PatientA
Height Weight Age BMI
<dbl> <dbl> <dbl> <dbl>
1 161 72.2 27 27.9
2 164 61.0 21 22.8
3 171 72.0 30 24.6
4 169. 63.9 25 22.9
5 174. 64.4 27 21.1
6 160 50.9 22 19.9
7 172 77.5 22 26.3
8 165 54.5 22 20
9 173 82.4 29 27.5
10 169 76.6 22 26.9
and I would like to get some statistics for each column. I have the next working code which deals only with quantiles
genStat <- PatientsA %>%
summarise_all(funs(list(quantile(., probs = c(0.25, 0.5, 0.75))))) %>%
unnest %>%
transpose %>%
setNames(., c('25%', '50%', '75%')) %>%
map_df(unlist) %>%
bind_cols(data.frame(vars = names(PatientsA)), .)
and I need to add mean and sd to summarise_all like this
genStat <- PatientsA %>%
summarise_all(funs(mean,sd,list(quantile(., probs = c(0.25, 0.5, 0.75))))) %>%
unnest %>%
transpose %>%
setNames(., c('mean','sd','25%', '50%', '75%')) %>%
map_df(unlist) %>%
bind_cols(data.frame(vars = names(PatientsA)), .)
This straightforward approach fails returning the next error:
Error in names(object) <- nm : 'names' attribute [5] must be the
same length as the vector [3]
I'm a newbie in R, so what is the right syntax for completing this task?

Answer 1

This is what I would suggest. There is a little repetition in the code (calling quantile three times) but overall I think it is easier to understand and debug.
library(tidyverse)
PatientsA %>%
gather("variable", "value") %>%
group_by(variable) %>%
summarize(mean_val = mean(value),
sd_val = sd(value),
q25 = quantile(value, probs = .25),
q50 = quantile(value, probs = .5),
q75 = quantile(value, probs = .75))
## A tibble: 4 x 6
# variable mean_val sd_val q25 q50 q75
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 Age 24.7 3.33 22 23.5 27
#2 BMI 24.0 3.08 21.5 23.8 26.7
#3 Height 168. 5.01 164. 169 172.
#4 Weight 67.5 10.3 61.7 68.2 75.5

Answer 2

We could also place the quantile output in a list and then unnest
library(tidyverse)
PatientsA %>%
gather %>%
group_by(key) %>%
summarise_at(vars('value'),
funs(mean,
sd,
quantile = list(as.tibble(as.list(quantile(.,
probs = c(0.25, 0.5, 0.75))))))) %>%
unnest
# A tibble: 4 x 6
# key mean sd `25%` `50%` `75%`
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 Age 24.7 3.33 22 23.5 27
#2 BMI 24.0 3.08 21.5 23.8 26.7
#3 Height 168. 5.01 164. 169 172.
#4 Weight 67.5 10.3 61.7 68.2 75.5
Or using pivot_longer
PatientsA %>%
pivot_longer(cols = everything()) %>%
group_by(name) %>%
summarise(across(value, list(mean= ~ mean(., na.rm = TRUE),
sd = ~ sd(., na.rm = TRUE),
quantile = ~ list(as_tibble(as.list(quantile(.,
probs = c(0.25, 0.5, 0.75)))))))) %>%
unnest(c(value_quantile))
# A tibble: 4 x 6
name value_mean value_sd `25%` `50%` `75%`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Age 24.7 3.33 22 23.5 27
2 BMI 24.0 3.08 21.5 23.8 26.7
3 Height 168. 5.01 164. 169 172.
4 Weight 67.5 10.3 61.7 68.2 75.5
###data
PatientsA <- structure(list(Height = c(161, 164, 171, 169, 174, 160, 172,
165, 173, 169), Weight = c(72.2, 61, 72, 63.9, 64.4, 50.9, 77.5,
54.5, 82.4, 76.6), Age = c(27L, 21L, 30L, 25L, 27L, 22L, 22L,
22L, 29L, 22L), BMI = c(27.9, 22.8, 24.6, 22.9, 21.1, 19.9, 26.3,
20, 27.5, 26.9)), class = "data.frame", row.names = c("1", "2",
"3", "4", "5", "6", "7", "8", "9", "10"))