dplyr summarise multiple columns using t.test

Question

Is it possible somehow to do a t.test over multiple variables against the same categorical variable without going through a reshaping of the dataset as follows?

data(mtcars)
library(dplyr)
library(tidyr)
j <- mtcars %>% gather(var, val, disp:qsec)
t <- j %>% group_by(var) %>% do(te = t.test(val ~ vs, data = .))

t %>% summarise(p = te$p.value)

I´ve tried using

mtcars %>% summarise_each_(funs = (t.test(. ~ vs))$p.value, vars = disp:qsec)

but it throws an error.

Bonus: How can t %>% summarise(p = te$p.value) also include the name of the grouping variable?

Answer 1

After all discussions with @aosmith and @Misha, here is one approach. As @aosmith wrote in his/her comments, You want to do the following.

mtcars %>%
    summarise_each(funs(t.test(.[vs == 0], .[vs == 1])$p.value), vars = disp:qsec)

#         vars1        vars2      vars3        vars4        vars5
#1 2.476526e-06 1.819806e-06 0.01285342 0.0007281397 3.522404e-06

vs is either 0 or 1 (group). If you want to run a t-test between the two groups in a variable (e.g., dips), it seems that you need to subset data as @aosmith suggested. I would like to say thank you for the contribution.

What I originally suggested works in another situation, in which you simply compare two columns. Here is sample data and codes.

foo <- data.frame(country = "Iceland",
                  year = 2014,
                  id = 1:30,
                  A = sample.int(1e5, 30, replace = TRUE),
                  B = sample.int(1e5, 30, replace = TRUE),
                  C = sample.int(1e5, 30, replace = TRUE),
                  stringsAsFactors = FALSE)

If you want to run t-tests for the A-C, and B-C combination, the following would be one way.

foo2 <- foo %>%
        summarise_each(funs(t.test(., C, pair = TRUE)$p.value), vars = A:B) 

names(foo2) <- colnames(foo[4:5])

#          A         B
#1 0.2937979 0.5316822

Answer 2

I like the following solution using the powerful "broom" package:

library("dplyr")
library("broom")

your_db %>%
  group_by(grouping_variable1, grouping_variable2 ...) %>%
  do(tidy(t.test(variable_u_want_2_test ~ dicothomous_grouping_var, data = .)))