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...well! Statistics doesn't always have a positive image. I can recommend running an R course as one way of making a good impression. Antony Unwin Professor of Computer-Oriented Statistics and Data Analysis, Mathematics Institute, University of Augsburg, 86135 Augsburg, Germany Tel: + 49 821 5982218 http://stats.math.uni-augsburg.de/ [[alternative HTML version deleted]]

...interesting to know just how widely R is used, where it is used most (and where least or not at all). Does anyone have a good overview? Antony Unwin Professor of Computer-Oriented Statistics and Data Analysis, Mathematics Institute, University of Augsburg, 86135 Augsburg, Germany Tel: + 49 821 5982218 antony.unwin@math.uni-augsburg.de http://stats.math.uni-augsburg.de/ [[alternative HTML version deleted]]

I have been experimenting with interactive packages such iplots and playwith. Consider the following sample dataset: A B C D 1 5 5 9 3 2 8 4 1 7 3 0 7 2 2 6 Let's say I make a plot of variable A. I would like to be able to click on a data point (e.g. 3) and have a pop-up window tell me the corresponding value for variable D (e.g. 4). I am also trying to produce multiple

Hi, is there a way to take a data frame with 100+ columns and large data set to do efficient exploratory analysis in R with pairs? I find using pairs on the whole matrix is slow and the resulting matrix is tiny. Also the variable of interest for me is a binary var Y or N . Is there an efficient way to graphically view many variable relationships that does not look teeny ? I could do

I have code like this: - #----------------------------------------------------------------------- ------------------------------------------------------ x=scan() 0 0 0 0 0 1 2 3 4 y=scan() 1 1 1 2 2 1 3 4 5 plot(x,y) identify(0,1,3) #Allows me to select manually to identify co-ordinate (0,1) as being duplicated 3 times identify(0,2,2) #Allows me to select manually to identify co-ordinate

Hi, I find that a typical workflow for me looks something like this: 1) import some data from files 2) mess around with the data for a while 3) mess around with plotting for a while 4) get a plot or analysis that looks good 5) go back through my history to make a list of the shortest command sequence to recreate the plot or analysis 6) send out that sequence to colleagues, along with the