reformatting dplyr summarise_at() output - r

Question

I am using summarise_at() to obtain the mean and standard error of multiple variables by group.
The output has 1 row for each group, and 1 column for each calculated quantity, per group. I'd like to have a table with 1 row for each variable, and 1 column for each calculated quantity:
data <- mtcars
data$condition <- as.factor(c(rep("control", 16), rep("treat", 16)))
data %>%
group_by(condition) %>%
summarise_at(vars(mpg, cyl, wt),
funs(mean = mean, se=sd(.)/sqrt(n())))
# A tibble: 2 x 7
condition mpg_mean cyl_mean wt_mean mpg_se cyl_se wt_se
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 control 18.2 6.5 3.56 1.04 0.387 0.204
2 treat 22.0 5.88 2.87 1.77 0.499 0.257
Here's what I think would be more useful (the numbers are not meaningful):
# MEAN.control, MEAN.treat, SE.control, SE.treat
# mpg 1.5 2.4 .30 .45
# cyl 3.2 1.9 .20 .60
# disp 12.3 17.8 .20 .19
Any ideas? New to the tidyverse, so sorry if this is too basic.

Answer 1

The funs is getting deprecated in dplyr. Instead use list in summarise_at/mutate_at. After the summarise step, gather the data into 'long' format, separate the 'key' column into two by splitting at the delimiter _, then unite the 'cond' and 'key2' (after changing the case of 'key2'), spread it to 'wide' format and if needed, change the row names with the column 'key1'
library(tidyverse)
data %>%
group_by(condition) %>%
summarise_at(vars(mpg, cyl, wt), list(MEAN = ~ mean(.),
SE = ~sd(.)/sqrt(n()))) %>%
gather(key, val, -condition) %>%
separate(key, into = c("key1", "key2")) %>%
unite(cond, key2, condition, sep=".") %>%
spread(cond, val) %>%
column_to_rownames('key1')
# MEAN.control MEAN.treat SE.control SE.treat
#cyl 6.500000 5.875000 0.3872983 0.4989572
#mpg 18.200000 21.981250 1.0369024 1.7720332
#wt 3.560875 2.873625 0.2044885 0.2571034

Answer 2

A different possibility could be:
data %>%
group_by(condition) %>%
summarise_at(vars(mpg, cyl, wt), list(mean = ~ mean(.),
se = ~ sd(.)/sqrt(n()))) %>%
gather(var, val, -condition) %>%
separate(var, c("vars", "var2")) %>%
mutate(var2 = paste(toupper(var2), as.character(condition), sep = "_")) %>%
select(-condition) %>%
spread(var2, val)
vars MEAN_control MEAN_treat SE_control SE_treat
<chr> <dbl> <dbl> <dbl> <dbl>
1 cyl 6.5 5.88 0.387 0.499
2 mpg 18.2 22.0 1.04 1.77
3 wt 3.56 2.87 0.204 0.257
Here, after your initial steps, it performs a wide-to-long data transformation, excluding the "condition" column. Second, it separates the variable names into two columns. Third, it combines the metric and the condition, with the metric being upper case. Finally, it removes the redundant variable and returns it to the desired format.
Or you can avoid separate() by using some regex:
data %>%
group_by(condition) %>%
summarise_at(vars(mpg, cyl, wt), list(mean = ~ mean(.),
se = ~ sd(.)/sqrt(n()))) %>%
gather(var, val, -condition) %>%
mutate(vars = gsub("_.*$", "", var),
var2 = gsub(".*\\_", "", var)) %>%
mutate(var2 = paste(toupper(var2), as.character(condition), sep = "_")) %>%
select(-condition, -var) %>%
spread(var2, val)
Or with strsplit():
data %>%
group_by(condition) %>%
summarise_at(vars(mpg, cyl, wt), list(mean = ~ mean(.),
se = ~ sd(.)/sqrt(n()))) %>%
gather(var, val, -condition) %>%
mutate(vars = sapply(strsplit(var, "_"), function(x) x[1]),
var2 = sapply(strsplit(var, "_"), function(x) x[2])) %>%
mutate(var2 = paste(toupper(var2), as.character(condition), sep = "_")) %>%
select(-condition, -var) %>%
spread(var2, val)
Or you can completely rewrite it to:
data %>%
select(mpg, cyl, wt, condition) %>%
gather(vars, val, -condition) %>%
group_by(condition, vars) %>%
summarise(mean = mean(val),
se = sd(val)/sqrt(n())) %>%
ungroup() %>%
gather(var2, val, -c(condition, vars)) %>%
mutate(var2 = paste(toupper(var2), condition, sep = "_")) %>%
select(-condition) %>%
spread(var2, val)
In this case it, first, selects the variables of interest. Second, it performs a transformation from wide to long format, excluding the "condition" column. Third, it groups by conditions and variable names and calculates the metrics. In the forth step, it performs a second wide-to-long transformation, excluding the "condition" column and the column with initial variable names. Finally, it combines together the metric (upper case) and condition, removes the redundant variable and returns it to the desired format.