Getting rid of missing data

An alternative way of using the na.rm argument in R functions is removing NA from the dataset before passing that to the analysis functions. This means that we are removing the missing values from the dataset permanently, so that they won't cause any problems at later stages in the analysis. For this, we could use either the na.omit or the na.exclude functions:

> na.omit(c(1:5, NA))
[1] 1 2 3 4 5
attr(,"na.action")
[1] 6
attr(,"class")
[1] "omit"
> na.exclude(c(1:5, NA))
[1] 1 2 3 4 5
attr(,"na.action")
[1] 6
attr(,"class")
[1] "exclude"

The only difference between these two functions is the class of the na.action attribute of the returned R object, which are omit and exclude respectively. This minor difference is only important when modelling. The na.exclude function returns NA for residuals and predictions, while na.omit suppresses those elements of the vector:

> x <- rnorm(10); y <- rnorm(10)
> x[1] <- NA; y[2] <- NA
> exclude <...