File under “I keep forgetting how to do this basic, frequently-required task, so I’m writing it down here.”

Let’s create a data frame which contains five variables, vars, named A – E, each of which appears twice, along with some measurements:

df.orig <- data.frame(vars = rep(LETTERS[1:5], 2), obs1 = c(1:10), obs2 = c(11:20))
df.orig
#    vars obs1 obs2
# 1     A    1   11
# 2     B    2   12
# 3     C    3   13
# 4     D    4   14
# 5     E    5   15
# 6     A    6   16
# 7     B    7   17
# 8     C    8   18
# 9     D    9   19
# 10    E   10   20

Now, let’s say we want only the rows that contain the maximum values of obs1 for A – E. In bioinformatics, for example, we might be interested in selecting the microarray probeset with the highest sample variance from multiple probesets per gene. The answer is obvious in this trivial example (6 – 10), but one procedure looks like this:

# use aggregate to create new data frame with the maxima
df.agg <- aggregate(obs1 ~ vars, df.orig, max)
# then simply merge with the original
df.max <- merge(df.agg, df.orig)
df.max
#   vars obs1 obs2
# 1    A    6   16
# 2    B    7   17
# 3    C    8   18
# 4    D    9   19
# 5    E   10   20

This also works using min() and, I guess, using any function that returns a single value per variable mapping to a value in the original data frame.

With thanks to this mailing list thread.