Perform multiple two-sample t-test using dplyr in R - r

Question

I would like to perform multiple pairwise t-tests on a dataset containing about 400 different column variables and 3 subject groups, and extract p-values for every comparison. A shorter representative example of the data, using only 2 variables could be the following;
df <- tibble(var1 = rnorm(90, 1, 1), var2 = rnorm(90, 1.5, 1), group = rep(1:3, each = 30))
Ideally the end result will be a summarised data frame containing four columns; one for the variable being tested (var1, var2 etc.), two for the groups being tested every time and a final one for the p-value.
I've tried duplicating the group column in the long form, and doing a double group_by in order to do the comparisons but with no result
result <- df %>%
pivot_longer(var1:var2, "var", "value") %>%
rename(group_a = group) %>%
mutate(group_b = group_a) %>%
group_by(group_a, group_b) %>%
summarise(n = n())

Answer 1

We can reshape the data into 'long' format with pivot_longer, then grouped by 'group', apply the pairwise.t.test, extract the list elements and transform into tibble with tidy (from broom) and unnest the list column
library(dplyr)
library(tidyr)
library(broom)
df %>%
pivot_longer(cols = -group, names_to = 'grp') %>%
group_by(group) %>%
summarise(out = list(pairwise.t.test(value, grp
) %>%
tidy)) %>%
unnest(c(out))
-output
# A tibble: 3 x 4
group group1 group2 p.value
<int> <chr> <chr> <dbl>
1 1 var2 var1 0.0760
2 2 var2 var1 0.0233
3 3 var2 var1 0.000244

Answer 2

In case you end up wanting more information about the t-tests, here is an approach that will allow you to extract more information such as the degrees of freedom and value of the test statistic:
library(dplyr)
library(tidyr)
library(purrr)
library(broom)
df <- tibble(
var1 = rnorm(90, 1, 1),
var2 = rnorm(90, 1.5, 1),
group = rep(1:3, each = 30)
)
df %>%
select(-group) %>%
names() %>%
map_dfr(~ {
y <- .
combn(3, 2) %>%
t() %>%
as.data.frame() %>%
pmap_dfr(function(V1, V2) {
df %>%
select(group, all_of(y)) %>%
filter(group %in% c(V1, V2)) %>%
t.test(as.formula(sprintf("%s ~ group", y)), ., var.equal = TRUE) %>%
tidy() %>%
transmute(y = y,
group_1 = V1,
group_2 = V2,
df = parameter,
t_value = statistic,
p_value = p.value
)
})
})
#> # A tibble: 6 x 6
#> y group_1 group_2 df t_value p_value
#> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 var1 1 2 58 -0.337 0.737
#> 2 var1 1 3 58 -1.35 0.183
#> 3 var1 2 3 58 -1.06 0.295
#> 4 var2 1 2 58 -0.152 0.879
#> 5 var2 1 3 58 1.72 0.0908
#> 6 var2 2 3 58 1.67 0.100
And here is #akrun's answer tweaked to give the same p-values as the above approach. Note the p.adjust.method = "none" which gives independent t-tests which will inflate your Type I error rate.
df %>%
pivot_longer(
cols = -group,
names_to = "y"
) %>%
group_by(y) %>%
summarise(
out = list(
tidy(
pairwise.t.test(
value,
group,
p.adjust.method = "none",
pool.sd = FALSE
)
)
)
) %>%
unnest(c(out))
#> # A tibble: 6 x 4
#> y group1 group2 p.value
#> <chr> <chr> <chr> <dbl>
#> 1 var1 2 1 0.737
#> 2 var1 3 1 0.183
#> 3 var1 3 2 0.295
#> 4 var2 2 1 0.879
#> 5 var2 3 1 0.0909
#> 6 var2 3 2 0.100
Created on 2021-07-30 by the reprex package (v1.0.0)