Role of print() function in a for loop - r

Question

I have a question here about the print() inside a for loop.
I have a dataset (gpa) with 2 columns. I am trying to get mean, variance, and standard deviation of values inside the two columns. When I code,
for(x in c(1:2)) {
mean(gpa[[x]])
var(gpa[[x]])
sd(gpa[[x]])
}
I don't get any output:
for(x in c(1:2)) {
print(mean(gpa[[x]]))
print(var(gpa[[x]]))
print(sd(gpa[[x]]))
}
But If i insert print before each of the lines, I do get the desired values.
What is the difference here? Is print really necessary?

Answer 1

The reason for this is, that all the stuff inside the loop gets evaluated, but never returned to somewhere. Though print deliver something I wouldn't advise it, because you can't use these values later, because they get returned to the console rather then the global environment. Instead you might want to assign them to something.
For example:
#example data
df <- data.frame(x = 1:10, y = rnorm(10))
#it is good to create the output in the desired length first
#it is much more efficient in terms of speed an memory beeing used but here it don't really matter
ret <- vector("list", NCOL(df))
for(x in seq_len(NCOL(df))){
ret[[x]] <- c(mean(df[[x]]),
var(df[[x]]),
sd(df[[x]]))
}
ret
Though I don't advise that neither. For the most (if not all things) you can do with a for loop you can use the apply family of functions from base r or the map function from purrr. This would look that way:
library(purrr)
map(df, ~c(mean(.), var(.), sd(.)))
#or even save it with names
ret <- map(df, ~c(mean = mean(.), var = var(.), sd = sd(.)))
ret
The apply/map variants are faster & shorter, but more importantly for me easier to understand and have less room for errors. Though there are a hole bunch of other arguments why you might want to use apply/map.