I have a table like this:
treatment individual phase dist_mean track
1 control 1 pre 13.33 569.99
2 control 1 post 10.95 624.65
3 control 2 pre 9.93 363.35
4 control 2 post 10.11 339.88
5 control 3 pre 12.00 676.42
6 control 3 post 12.80 939.15
In principle, two rows are always paired. I need to subtract dist_mean of the post-phase from the pre-phase of a sample. The easy way would be to subtract row 2 from 1 and so on. But given the possibility that this order is disturbed at any point, the whole calculation would go wrong. That's why I would like to have the calculation under the conditions, that the treatment and the individual of both phases match. Info: the treatment changes. It's not always control.
Use aggregate:
aggregate(dist_mean ~ treatment + individual, df1, function(x) diff(rev(x)))
# treatment individual dist_mean
#1 control 1 2.38
#2 control 2 -0.18
#3 control 3 -0.80
Data
df1 <- read.table(text = "
treatment individual phase dist_mean track
1 control 1 pre 13.33 569.99
2 control 1 post 10.95 624.65
3 control 2 pre 9.93 363.35
4 control 2 post 10.11 339.88
5 control 3 pre 12.00 676.42
6 control 3 post 12.80 939.15
", header = TRUE)