Apply a function to each element of a list and each row of two data frames

Question

is there an "apply" application for this instead of double-looping across each row of two data frames in base R (without using packages)?

listD <- matrix(1:6, ncol=2)
listD <- split(listD,seq(NROW(listD)))
df1 <- data.frame(x=c(1,2), y=c(3,4))
df2 <- data.frame(x=c(3,2), y=c(1,1))
testFunc <- function(a, b, c) a * (b + c)

for (j in 1:nrow(df1)) {
  for (s in 1:nrow(df2)) {
     print(lapply(listD, FUN= testFunc, b=df1[j,], c=df2[s,]))
  }
}

Thank you!

user3603486 · Accepted Answer · 2017-03-24 18:54:25Z

1

Use outer for its side effect:

invisible(outer(as.matrix(df1), as.matrix(df2), FUN = Vectorize(function (b, c) {
  print(lapply(listD, testFunc, b, c))
  NULL
})))

I don't know if this is an improvement on your for loop. There's a myth going round that for loops in R must be avoided at all costs (perhaps they look too easy?)

answered Mar 24, 2017 at 18:54

user3603486

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3 Comments

user971102 Over a year ago

Thank you dash2! I didn't know about outer! could you please explain why I would need "invisible" and "NULL" (if I don't use NULL I get an error)... also... how do I save this? thanks!!

user3603486 Over a year ago

You need it because otherwise it tries to print the result as a matrix, which it won't be. Saving the result is hard with outer because you can't return the result as a matrix. You might want to go inside out: put the lapply outside the outer call and return a list of matrices.

user971102 Over a year ago

Ok if there is no difference between looping and apply then I will stick to looping as it's clearer (at least to me right now...)

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