Dear list, thanks to your help I managed to find means of analysing my data.
However, the whole data set contains 264 variables. Of which some are factors, others are not. The factors tend to be grouped, e.g. data$f1304 to data$f1484 and data$f3204 to data$5408. But there are other types of variables in the data set as well, e.g. data$f1504. Not every spot is taken, i.e data$f1345 to data$1399 might not exist in the data set. The solution "summaryBy" works for cross analysis, of which there is a handful. So I am not worried here. The solution from Jorge is fine. However, I am trying to get my head around how to efficiently reduce my data set to the dependet variable and the factors such that the solution is applicable. Having to type each variable into my.reduced.data <- cbind(my.data$f1001, my.data$1002, my.data$1003... is an obvious option, but does not seem to be the most efficient one. Are there better ways to go about? Thanks, Gerit -- Sensationsangebot nur bis 30.11: GMX FreeDSL - Telefonanschluss + DSL für nur 16,37 Euro/mtl.!* http://dsl.gmx.de/?ac=OM.AD.PD003K11308T4569a ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.