I have the following dataframe
ID <- c(1,1,1,1,1,1,1,2,2,2,2,2,2,2,2, 3,3,3,3,3,3, 3,3,3,3,3,3)
reversal = c('R0', 'R0', 'R0', 'R0', 'R1', 'R1', 'R1',
'R0', 'R0', 'R0', 'R0', 'R1', 'R1', 'R1', 'R1',
'R0', 'R0', 'R0', 'R0', 'R1', 'R1', 'R1', 'R1', 'R2', 'R2', 'R2', 'R2')
event <- c(0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1)
RT = runif(27)
df <- data.frame(ID, reversal, event, RT)
and I would like to identify/label for each ID and for each reversal the first 4 instances of 0 and the first 4 instances of 1 in event, so that I could, for example, calculate the mean RT for those first 4 instances of 0 and 1. Thank you!
You can achieve the same result by putting the logic directly into the filter statement, no need to mutate or create intermediate objects:
df %>%
filter(row_number() <= 4, .by = c(ID, reversal, event)) %>%
summarise(mean_RT = mean(RT, na.rm = TRUE), .by = c(ID, reversal, event))
Or in older versions of dplyr
df %>%
group_by(ID, reversal, event) %>%
filter(row_number() <= 4) %>%
summarise(mean_RT = mean(RT, na.rm = TRUE)) %>%
ungroup()
Both will give you the same output as your mean_values:
ID reversal event mean_RT
1 1 R0 0 0.53447121
2 1 R1 1 0.64449169
3 1 R1 0 0.91095299
4 2 R0 0 0.78950270
5 2 R1 1 0.33560007
6 2 R1 0 0.89449568
7 3 R0 0 0.70057488
8 3 R1 1 0.31019288
9 3 R1 0 0.49802194
10 3 R2 1 0.05643762
11 3 R2 0 0.07677300