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I have a dataframe df, containing three vectors:

subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9
...

There are four observations (and hence four rows) for subject 01, with two observations corresponding to condition A and two corresponding to condition B. Let's say that due to a technical error, there are three condition B observations for subject 02.

My question is this: how can I truncate df to ensure that each condition only has two observations for each individual subject (hence removing the erroneous third row where condition==B for subject 02)?

Thanks in advance for any assistance!

Here's a dplyr solution -

df %>%
 group_by(subject, condition) %>%
 filter(row_number() < 3) %>%
 ungroup()

# A tibble: 8 x 3
 subject condition value
 <chr> <chr> <dbl>
1 01 A 12
2 01 A 6
3 01 B 10
4 01 B 2
5 02 A 5
6 02 A 11
7 02 B 3
8 02 B 5

For each subject/condition pair create a sequence number seq for its rows and then only keep those rows whose sequence number is less than 3.

subset(transform(DF, seq = ave(value, subject, condition, FUN = seq_along)), seq < 3)

giving:

 subject condition value seq
1 01 A 12 1
2 01 A 6 2
3 01 B 10 1
4 01 B 2 2
5 02 A 5 1
6 02 A 11 2
7 02 B 3 1
8 02 B 5 2

Note

The input in reprodudible form is assumed to be:

Lines <- "subject condition value
01 A 12
01 A 6
01 B 10
01 B 2
02 A 5
02 A 11
02 B 3
02 B 5
02 B 9"
DF <- read.table(text = Lines, header = TRUE, strip.white = TRUE,
 colClasses = c("character", "character", "numeric"))