Sometimes I need to get only the first row of a data set grouped by an identifier, as when retrieving age and gender when there are multiple observations per individual. What’s a fast (or the fastest) way to do this in R? I used aggregate() below and suspect there are better ways. Before posting this question I searched a bit on google, found and tried ddply, and was surprised that it was extremely slow and gave me memory errors on my dataset (400,000 rows x 16 cols, 7,000 unique IDs), whereas the aggregate() version was reasonably fast.
(dx <- data.frame(ID = factor(c(1,1,2,2,3,3)), AGE = c(30,30,40,40,35,35), FEM = factor(c(1,1,0,0,1,1)))) # ID AGE FEM # 1 30 1 # 1 30 1 # 2 40 0 # 2 40 0 # 3 35 1 # 3 35 1 ag <- data.frame(ID=levels(dx$ID)) ag <- merge(ag, aggregate(AGE ~ ID, data=dx, function(x) x), "ID") ag <- merge(ag, aggregate(FEM ~ ID, data=dx, function(x) x), "ID") ag # ID AGE FEM # 1 30 1 # 2 40 0 # 3 35 1 #same result: library(plyr) ddply(.data = dx, .var = c("ID"), .fun = function(x) x[1,])
UPDATE: See Chase’s answer and Matt Parker’s comment for what I consider to be the most elegant approach. See @Matthew Dowle’s answer for the fastest solution which uses the
Is your ID column really a factor? If it is in fact numeric, I think you can use the
diff function to your advantage. You could also coerce it to numeric with
dx <- data.frame( ID = sort(sample(1:7000, 400000, TRUE)) , AGE = sample(18:65, 400000, TRUE) , FEM = sample(0:1, 400000, TRUE) ) dx[ diff(c(0,dx$ID)) != 0, ]