plyr

I have a data frame that looks like this: #df ID DRUG FED AUC0t Tmax Cmax 1 1 0 100 5 20 2 1 1 200 6 25 3 0 1 NA 2 30 4 0 0 150 6 65 Ans so on. I want to summarize some statistics on AUC, Tmax and Cmax by drug DRUG and FED STATUS FED . I use dplyr. For example: for the AUC: CI90lo <- function(x) quantile(x, probs=0.05, na.rm=TRUE) CI90hi <- function(x) quantile(x, probs=0.95, na.rm=TRUE) summary <- df %>% group_by(DRUG,FED) %>% summarize(mean=mean(AUC0t, na.rm=TRUE), low = CI90lo(AUC0t), high= CI90hi(AUC0t), min=min(AUC0t, na.rm=TRUE), max=max(AUC0t,na.rm=TRUE), sd= sd(AUC0t, na.rm=TRUE))

I have a dataframe in R of the following form: > head(data) Group Score Info 1 1 1 a 2 1 2 b 3 1 3 c 4 2 4 d 5 2 3 e 6 2 1 f I would like to aggregate it following the Score column using the max function > aggregate(data$Score, list(data$Group), max) Group.1 x 1 1 3 2 2 4 But I also would like to display the Info column associated to the maximum value of the Score column for each group. I have no idea how to do this. My desired output would be: Group.1 x y 1 1 3 c 2 2 4 d Any hint? First, you split the data using split : split(z,z$Group) Than, for each chunk, select the row with max Score:

When using summarise with plyr 's ddply function, empty categories are dropped by default. You can change this behavior by adding .drop = FALSE . However, this doesn't work when using summarise with dplyr . Is there another way to keep empty categories in the result? Here's an example with fake data. library(dplyr) df = data.frame(a=rep(1:3,4), b=rep(1:2,6)) # Now add an extra level to df$b that has no corresponding value in df$a df$b = factor(df$b, levels=1:3) # Summarise with plyr, keeping categories with a count of zero plyr::ddply(df, "b", summarise, count_a=length(a), .drop=FALSE) b count