In R, sample n rows from a df in which a certain column has non-NA values (sample conditionally)

Question

Background

Here's a toy df:

df <- data.frame(ID = c("a","b","c","d","e","f"), 
                gender = c("f","f","m","f","m","m"), 
                zip = c(48601,NA,29910,54220,NA,44663),stringsAsFactors=FALSE)

As you can see, I've got a couple of NA values in the zip column.

Problem

I'm trying to randomly sample 2 entire rows from df -- but I want them to be rows for which zip is not null.

What I've tried

This code gets me a basic (i.e. non-conditional) random sample:

df2 <- df[sample(nrow(df), 2), ]

But of course, that only gets me halfway to my goal -- a bunch of the time it's going to return a row with an NA value in zip. This code attempts to add the condition:

df2 <- df[sample(nrow(df$zip != NA), 2), ]

I think I'm close, but this yields an error invalid first argument.

Any ideas?

Answer 1

Here is a base R solution with complete.cases()

# define a logical vector to identify NA
x <- complete.cases(df)

# subset only not NA values
df_no_na <- df[x,]

# do the sample
df_no_na[sample(nrow(df_no_na), 2),]

Output:

  ID gender   zip
3  c      m 29910
6  f      m 44663

Answer 2

We can use is.na

tmp <- df[!is.na(df$zip),]
> tmp[sample(nrow(tmp), 2),]

Answer 3

We can use rownames + na.omit to sample the rows

> df[sample(rownames(na.omit(df["zip"])), 2),]
  ID gender   zip
3  c      m 29910
4  d      f 54220