Efficient method to subset drop rows with NA values in R

Question

Background Before running a stepwise model selection, I need to remove missing values for any of my model terms. With quite a few terms in my model, there are t

Answer 1

Let dat be a data frame and cols a vector of column names or column numbers of interest. Then you can use

dat[!rowSums(is.na(dat[cols])), ]

to exclude all rows with at least one NA.

Answer 2

Edit: I completely glossed over subset, the built in function that is made for sub-setting things:

my.df <- subset(my.df, 
  !(is.na(termA) |
    is.na(termB) |
    is.na(termC) )
  )

---

I tend to use with() for things like this. Don't use attach, you're bound to cut yourself.

my.df <- my.df[with(my.df, {
  !(is.na(termA) |
    is.na(termB) |
    is.na(termC) )
}), ]

But if you often do this, you might also want a helper function, is_any()

is_any <- function(x){
  !is.na(x)
}

If you end up doing a lot of this sort of thing, using SQL is often going to be a nicer interaction with subsets of data. dplyr may also prove useful.

Answer 3

This is one way:

#  create some random data
df <- data.frame(y=rnorm(100),x1=rnorm(100), x2=rnorm(100),x3=rnorm(100))
# introduce random NA's
df[round(runif(10,1,100)),]$x1 <- NA
df[round(runif(10,1,100)),]$x2 <- NA
df[round(runif(10,1,100)),]$x3 <- NA

# this does the actual work...
# assumes data is in columns 2:4, but can be anywhere
for (i in 2:4) {df <- df[!is.na(df[,i]),]}

And here's another, using sapply(...) and Reduce(...):

xx <- data.frame(!sapply(df[2:4],is.na))
yy <- Reduce("&",xx)
zz <- df[yy,]

The first statement "applies" the function is.na(...) to columns 2:4 of df, and inverts the result (we want !NA). The second statement applies the logical & operator to the columns of xx in succession. The third statement extracts only rows with yy=T. Clearly this can be combined into one horrifically complicated statement.

zz <-df[Reduce("&",data.frame(!sapply(df[2:4],is.na))),]

Using sapply(...) and Reduce(...) can be faster if you have very many columns.

Finally, most modeling functions have parameters that can be set to deal with NA's directly (without resorting to all this). See, for example the na.action parameter in lm(...).