Getting regression coefficients for all list elements without using a loop

Question

Given two lists with identical keys, can you get regression coefficients into a third list without using loops and hopefully also without creating temporary data frames for each list item?

I'm familiar with lapply, but don't know how to apply it for this case, if that's even possible!

s = list()
s$x = list(a=c(1, 2, 3), b=c(4, 5, 6))
s$y = list(a=c(1, 2, 4), b=c(4, 5, 8))

for(i in names(s$x)) {
  df = data.frame(x = s$x[[i]], y = s$y[[i]])
  model = lm(y ~ x, df)
  s$co[[i]] = model$coefficients
}

Answer 1

Map the indicated anonymous function over x and y extracting the coefficients and concatenate that to the input s. No packages are used.

s_out <- c(s, co = list(Map(function(x, y) coef(lm(y ~ x)), s$x, s$y)))

giving:

> str(s_out)
List of 3
 $ x :List of 2
  ..$ a: num [1:3] 1 2 3
  ..$ b: num [1:3] 4 5 6
 $ y :List of 2
  ..$ a: num [1:3] 1 2 4
  ..$ b: num [1:3] 4 5 8
 $ co:List of 2
  ..$ a: Named num [1:2] -0.667 1.5
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
  ..$ b: Named num [1:2] -4.33 2
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"