The difference of na.rm and na.omit in R

Question

I\'ve just started with R and I\'ve executed these statements:

library(datasets)
head(airquality)
s <- split(airquality,airquality$Month)
sapply(s, function(x         


        

Answer 1

sapply(s, function(x) {colMeans(x[,c("Ozone", "Solar.R", "Wind")], na.rm = TRUE)}) treats each column individually, and calculates the average of the non-NA values in each column.

lapply(s, function(x) {colMeans(na.omit(x[,c("Ozone", "Solar.R", "Wind")])) }) subsets sto those cases where none of the three columns are NA, and then takes the column means for the resulting data.

The difference comes from those rows which have one or two of the values as NA.

Answer 2

They are not supposed to give the same result. Consider this example:

exdf<-data.frame(a=c(1,NA,5),b=c(3,2,2))
#   a b
#1  1 3
#2 NA 2
#3  5 2
colMeans(exdf,na.rm=TRUE)
#       a        b 
#3.000000 2.333333
colMeans(na.omit(exdf))
#  a   b 
#3.0 2.5

Why is this? In the first case, the mean of column b is calculated through (3+2+2)/3. In the second case, the second row is removed in its entirety (also the value of b which is not-NA and therefore considered in the first case) by na.omit and so the b mean is just (3+2)/2.