Use cut2 as I suggested and David demonstrated. Michael
On Tue, Apr 3, 2012 at 9:31 AM, Val <valkr...@gmail.com> wrote: > Thank you all (David, Michael, Giovanni) for your prompt response. > > First there was a typo error for the group mean it was 89.6 not 87. > > For a small data set and few groupings I can use prob=c(0, .333, .66 ,1) to > group in to three groups in this case. However, if I want to extend the > number of groupings say 10 or 15 then do I have to figure it out the > split(x, cut(x, quantile(x, prob=c(0, .333, .66 ,1)) > > Is there a short cut for that? > > > Thanks > > > > > > > > > > > > On Tue, Apr 3, 2012 at 9:13 AM, R. Michael Weylandt > <michael.weyla...@gmail.com> wrote: >> >> Ignoring the fact your desired answers are wrong, I'd split the >> separating part and the group means parts into three steps: >> >> i) quantile() can help you get the split points, >> ii) findInterval() can assign each y to a group >> iii) then ave() or tapply() will do group-wise means >> >> Something like: >> >> y <- c(36, 45, 46, 66, 78, 125, 193, 209, 242, 297) # You need a "c" here. >> ave(y, findInterval(y, quantile(y, c(0.33, 0.66)))) >> tapply(y, findInterval(y, quantile(y, c(0.33, 0.66))), mean) >> >> You could also use cut2 from the Hmisc package to combine findInterval >> and quantile into a single step. >> >> Depending on your desired output. >> >> Hope that helps, >> Michael >> >> On Tue, Apr 3, 2012 at 8:47 AM, Val <valkr...@gmail.com> wrote: >> > Hi all, >> > >> > Assume that I have the following 10 data points. >> > x=c( 46, 125 , 36 ,193, 209, 78, 66, 242 , 297 , 45) >> > >> > sort x and get the following >> > y= (36 , 45 , 46, 66, 78, 125,193, 209, 242, 297) >> > >> > I want to group the sorted data point (y) into equal number of >> > observation per group. In this case there will be three groups. The >> > first >> > two groups will have three observation and the third will have four >> > observations >> > >> > group 1 = 34, 45, 46 >> > group 2 = 66, 78, 125 >> > group 3 = 193, 209, 242,297 >> > >> > Finally I want to calculate the group mean >> > >> > group 1 = 42 >> > group 2 = 87 >> > group 3 = 234 >> > >> > Can anyone help me out? >> > >> > In SAS I used to do it using proc rank. >> > >> > thanks in advance >> > >> > Val >> > >> > [[alternative HTML version deleted]] >> >> > >> > ______________________________________________ >> > 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. > > ______________________________________________ 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.
