On Jan 15, 2008 6:04 PM, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote: > Matthew Keller wrote: > > Hi all, > > > > I'm giving a talk in a few days to a group of psychology faculty and > > grad students re the R statistical language. Most people in my dept. > > use SAS or SPSS. It occurred to me that it would be nice to have a few > > concrete examples of things that are fairly straightforward to do in R > > but that are difficult or impossible to do in SAS or SPSS. However, it > > has been so long since I have used either of those commercial products > > that I am drawing a blank. I've searched the forums and web for a list > > and came up with just Bob Muenchen's comparison of general procedures > > and Patrick Burns' overview of the three. Neither of these give > > concrete examples of statistical problems that are easily solved in R > > but not the commercial packages. > > > > Can anyone more familiar with SAS or SPSS think of some examples of > > problems that they couldn't do in one of those packages but that could > > be done easily in R? Similarly, if there are any examples of the > > converse I would also be interested to know. > > > > Best, > > > > Matt > > > > Here is a simple thing that is easy to do in R or S-Plus but difficult > in SAS or SPSS: > > Compute the number of subjects having age below the mean age > > sum(age < mean(age)) > > > Here is something not quite so simple that is very difficult to do in > SPSS or SAS. Show descriptive statistics for every variable in a data > frame that is numeric and has at least 10 unique values. > > v <- sapply(mydata, function(x) is.numeric(x) && length(unique(x)) >= 10) > summary(mydata[v]) >
This can be simplified very slightly (creating mydata[v] directly rather than creating a vector v and then subscripting with it) using Filter: is.ok <- function(x) is.numeric(x) & length(unique(x)) > 10 summary(Filter(is.ok, mydata)) ______________________________________________ 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.
