The easiest way of dealing with missing values, especially with MCAR data, is simply removing all the observations with any missing values. If we want to exclude every row of a matrix or data.frame object which has at least one missing value, we can use the complete.cases function from the stats package to identify those.

For a quick start, let's see how many rows have at least one missing value:

> library(hflights)
> table(complete.cases(hflights))
 FALSE   TRUE 
  3622 223874

This is around 1.5 percent of the quarter million rows:

> prop.table(table(complete.cases(hflights))) * 100
    FALSE      TRUE 
 1.592116 98.407884

Let's see what the distribution of NA looks like within different columns:

> sort(sapply(hflights, function(x) sum(is.na(x))))
             Year             Month        DayofMonth 
                0                 0                 0 
        DayOfWeek     UniqueCarrier         FlightNum 
                0                 0         ...