How can I get the average (mean) of selected columns

Question

I would like to get the average for certain columns for each row.

I have this data:

w=c(5,6,7,8) x=c(1,2,3,4) y=c(1,2,3) length(y)=4 z=data.frame(w,x,y) 

Which returns:

  w x  y 1 5 1  1 2 6 2  2 3 7 3  3 4 8 4 NA 

I would like to get the mean for certain columns, not all of them. My problem is that there are a lot of NAs in my data. So if I wanted the mean of x and y, this is what I would like to get back:

  w x  y mean 1 5 1  1    1 2 6 2  2    2 3 7 3  3    3 4 8 4 NA    4 

I guess I could do something like z$mean=(z$x+z$y)/2 but the last row for y is NA so obviously I do not want the NA to be calculated and I should not be dividing by two. I tried cumsum but that returns NAs when there is a single NA in that row. I guess I am looking for something that will add the selected columns, ignore the NAs, get the number of selected columns that do not have NAs and divide by that number. I tried ??mean and ??average and am completely stumped.

ETA: Is there also a way I can add a weight to a specific column?

Answer 1

Here are some examples:

> z$mean <- rowMeans(subset(z, select = c(x, y)), na.rm = TRUE) > z   w x  y mean 1 5 1  1    1 2 6 2  2    2 3 7 3  3    3 4 8 4 NA    4 

weighted mean

> z$y <- rev(z$y) > z   w x  y mean 1 5 1 NA    1 2 6 2  3    2 3 7 3  2    3 4 8 4  1    4 >  > weight <- c(1, 2) # x * 1/3 + y * 2/3 > z$wmean <- apply(subset(z, select = c(x, y)), 1, function(d) weighted.mean(d, weight, na.rm = TRUE)) > z   w x  y mean    wmean 1 5 1 NA    1 1.000000 2 6 2  3    2 2.666667 3 7 3  2    3 2.333333 4 8 4  1    4 2.000000 

Answer 2

Try using rowMeans:

z$mean=rowMeans(z[,c("x", "y")], na.rm=TRUE)    w x  y mean 1 5 1  1    1 2 6 2  2    2 3 7 3  3    3 4 8 4 NA    4