using t.test within data.table on multiple columns - r

Question

I am trying to run a t.test() on multiple columns of data within 'j' in a data.table. I've found a way that works, but isn't very elegant, and I feel like there's probably a more concise way using .SDcols, but haven't had any luck looking through here, or the data.table vignette. If this has been asked previously I apologize and please point me in the right direction.
My data.table has essentially the following format
DT <- data.table(name = c('a', 'b', 'c', 'a', 'b', 'c'),
y = c(rnorm(6, mean = 100, sd = 30)),
z = c(rnorm(6, mean = 10, sd = 3)),
group = rep(c('One', 'Two'), 3))
When I want to run a t.test comparing the values of y in group One and group Two, it's very straightforward:
DT[,t.test(y~group)]
If I want to get an output for both y and z the following works, but is clunky and inelegant. And with my actual data, I'm trying to do this over many columns so it would be more time consuming to type out each iteration I would like to run.
DT[,.(t.test(y~group), t.test(z~group))]
In the data.table vignette, using a function over a specific subset of columns is achieved by
DT[,lapply(.SD, mean), .SDcols = c('y', 'z')]
However replacing mean with t.test yields a one sample t.test, while I'm trying to get a two sample t.test. I've tried:
DT[,lapply(.SD, t.test, formula = .SDcols ~ group, data = DT), .SDcols = c('y', 'z')]
But this gives me a comparison between y and z, not both the comparisons of y~group and z~group.
I've tried several versions of lapply with a custom function to get the output I want, but I won't make anyone read through my walls of unsuccessful code. Needless to say I have been unable to get that to work.
Question:
Is there a way via lapply() or function() or a way currently unknown to me, to get t.test to run over multiple columns of data within 'j' in a data.table?
Thanks in advance for your help,
Chris

Answer 1

To pull together the parts of the answer and to rearrange to put the name in the first column (if desired for nicer printing):
library(data.table)
DT <- data.table(name = c('a', 'b', 'c', 'a', 'b', 'c'),
y = c(rnorm(6, mean = 100, sd = 30)),
z = c(rnorm(6, mean = 10, sd = 3)),
group = rep(c('One', 'Two'), 3))
result <-
DT[,lapply(.SD, function(x) t.test(x ~ group)), .SDcols = y:z][
,ttname:=names(t.test(1:5))][ # add names
,.(ttname,y,z)] # put names in first column
result
# ttname y z
# 1: statistic 0.1391646 0.1295093
# 2: parameter 3.468876 3.559917
# 3: p.value 0.8970165 0.9039359
# 4: conf.int -99.61786,109.47358 -8.209637, 8.972439
# 5: estimate 110.7286,105.8008 11.15414,10.77274
# 6: null.value 0 0
# 7: stderr 35.41031 2.94497
# 8: alternative two.sided two.sided
# 9: method Welch Two Sample t-test Welch Two Sample t-test
# 10: data.name x by group x by group

Answer 2

Here is my solution, wrapped as a function. In the accepted answer, , I didn't like that the test output was rows and the variables columns, i like it the other way around, makes it easier to read I think.
I also a added an argument for rounding, and one that default only prints the most important info, the pvalue and test statistic. the function requires purrr. the input for the group variable and the variables to test are character, so dt_test(dtx, 'varname', c('z','y'))
dt_ttest <- function(dtx, grp, thecols, decimals=3, small=TRUE, ...) {
x1 <- a2[, map(.SD, ~ t.test(.x ~ get(grp))), .SDcols = thecols]
x2 <- t(x1) %>% data.table()
setnames(x2, names(t.test(1:2)))
x2 <- x2[, var := thecols] [, !'data.name']
tcols <- c('p.value', 'statistic', 'stderr', 'null.value', 'parameter', 'method', 'alternative')
x2[, (tcols) := map(.SD, unlist), .SDcols=tcols ]
x2
thecols2 <- keep(x2, is.numeric) %>% names()
x2[, (thecols2) := map(.SD, ~ round(.x, decimals)), .SDcols=thecols2 ]
# go one level deeper to round the two list cols
thecols3 <- c('conf.int', 'estimate')
x2[, (thecols3) := modify_depth(.SD, 2, ~ round(.x, decimals)), .SDcols=thecols3 ]
# set order
setcolorder(x2, c('var', 'p.value', 'statistic', 'stderr', 'conf.int', 'estimate', 'parameter', 'method', 'alternative') )
if( small) x2[, .(var, p.value, statistic)] else x2[]
}