Can you elaborate on data.table being 'a game changer'. I am inclined to agree, but I'm am just starting to get a handle on it. I am still hesitant and switching between sqldf, reshape2, base::merge and data.table more than I would like. Do you think it could become a dominant method for data preparation?

Python has PyTables which complements Pandas nicely and seems to offer the same sort of features as data.table (note, I've not actually used data.table)

I am using R to analyse and document (knitr and latex) epidemiologic data which does not involve parsing a lot of text to extract my analysis data set. Data preparation for this type of research involves more combining data from different source tables, restructuring repeated measures, etc. I only know how to do that using R. Can Python be incorporated into the knitr literate programming framework and is it worth learning another language?

Python will be better supported in knitr in future; for now it only has preliminary support: http://yihui.name/knitr/demo/engines/