For example, if we calculate Pearson correlation and P-value of first two variables of data set mtcars, results are something like this:
Correlation value:
mpg disp
mpg 1.00 -0.85
disp -0.85 1.00
P-value:
mpg disp
mpg 0.0000 0.0000
disp 0.0000 0.0000
Instead of this, Is there any way to have results like this:
Corr. p-value
mp mp 1.00 0.0000
mp dip -0.85 0.0000
I have more than 200 variable and want to generate results like this and then write those results on CSV using write.csv command. Thank you!
If we wanted pairwise, cor.test, use combn
out <- combn(mtcars, 2, FUN = function(x)
cor.test(x[[1]], x[[2]], conf.level = 0.95), simplify = FALSE)
names(out) <- combn(names(mtcars), 2, FUN = paste, collapse='_')
The output of corr.test is a list
str(out[[1]])
#List of 9
# $ statistic : Named num -8.92
# ..- attr(*, "names")= chr "t"
# $ parameter : Named int 30
# ..- attr(*, "names")= chr "df"
# $ p.value : num 6.11e-10
# $ estimate : Named num -0.852
# ..- attr(*, "names")= chr "cor"
# $ null.value : Named num 0
# ..- attr(*, "names")= chr "correlation"
# $ alternative: chr "two.sided"
# $ method : chr "Pearson's product-moment correlation"
# $ data.name : chr "x[[1]] and x[[2]]"
# $ conf.int : num [1:2] -0.926 -0.716
# ..- attr(*, "conf.level")= num 0.95
It can be directly extracted with list extraction methods i.e. $ or [[
mydata <– do.call(rbind, Map(cbind, corgroups = names(out),
unname(lapply(out, function(x)
data.frame(cor.value = x$estimate, cor.pvalue = x$p.value)))))