Question

I have been scratching my head over this. I have two data frames: df

df <- data.frame(group = 1:3,
                 age = seq(30, 50, length.out = 3),
                 income = seq(100, 500, length.out = 3),
                 assets = seq(500, 800, length.out = 3))

and weights

weights <- data.frame(age = 5, income = 10)

I would like to multiply these two data frames only for the same column names. I tried something like this:

colwise(function(x) {x * weights[names(x)]})(df)

but that obviously didn’t work as colwise does not keep the column name inside the function. I looked at various mapply solutions (example), but I am unable to come up with an answer.

The resulting data.frame should look like this:

structure(list(group = 1:3, age = c(150, 200, 250), income = c(1000, 
3000, 5000), assets = c(500, 650, 800)), .Names = c("group", 
"age", "income", "assets"), row.names = c(NA, -3L), class = "data.frame")

  group age income assets
1     1 150   1000    500
2     2 200   3000    650
3     3 250   5000    800

Answer 1

sweep() is your friend here, for this particular example. It relies upon the names in df and weights being in the right order, but that can be arranged.

> nams <- names(weights)
> df[, nams] <- sweep(df[, nams], 2, unlist(weights), "*")
> df
  group age income assets
1     1 150   1000    500
2     2 200   3000    650
3     3 250   5000    800

If the variable names in weights and df are not in the same order, you can make them so:

> df2 <- data.frame(group = 1:3,
+                   age = seq(30, 50, length.out = 3),
+                   income = seq(100, 500, length.out = 3),
+                   assets = seq(500, 800, length.out = 3))
> nams <- c("age", "income") ## order in df2
> weights2 <- weights[, rev(nams)]
> weights2  ## wrong order compared to df2
  income age
1     10   5
> df2[, nams] <- sweep(df2[, nams], 2, unlist(weights2[, nams]), "*")
> df2
  group age income assets
1     1 150   1000    500
2     2 200   3000    650
3     3 250   5000    800

In other words we reorder all objects so that age and income are in the right order.