The following is an example of a question that has been asked several times in the past, but I have not seen a fully satisfactory answer. I would like to know if newer strategies have been developed, or if there is no convenient way to get to the desired result.
The typical question asks if a function can be applied to several data frames. An example is:

df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])

Above are the two (of perhaps a long list of) data frames, and what is desired is to add another column to each of the original dataframes which displays the average of that row. A typical answer is:

lapply(list(df1, df2), function(w) { w$Avg <- rowMeans(w[1:2]); w })

However, this does not change or add a column to the original data frames, but produces the following:

[[1]]
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0

[[2]]
x y ID Avg
1 5 2 f 3.5
2 5 3 g 4.0
3 5 4 h 4.5
4 5 5 i 5.0
5 5 6 j 5.5

Question: Is there a procedure to apply a function to many data frames in a way that changes those dataframes directly?

I looked at many questions and answers, but they were basically the above strategy.