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For those who don't follow Ubuntu development carefully, the first Beta for the next Ubuntu was recently released, so I took my home system and upgraded to help out with filing bugs, etc. Just to be clear, I am not looking for help with the upgrade process. I've had R, and a few miscellaneous CRAN packages installed on this computer for years. Today, when I loaded an R session I had

For those who don't follow Ubuntu development carefully, the first Beta for the next Ubuntu was recently released, so I took my home system and upgraded to help out with filing bugs, etc. Just to be clear, I am not looking for help with the upgrade process. I've had R, and a few miscellaneous CRAN packages installed on this computer for years. Today, when I loaded an R session I had

I was asked by my boss to do an analysis on a large data set, and I am trying to convince him to let me use R rather than SPSS. I think Sweave could make my life much much easier. To get me a little closer to this goal, I ran my analysis through R and SPSS and compared the resulting values. In all but one case, they were the same. Given the matrix [,1] [,2] [1,] 110 358 [2,] 71 312 [3,]

If I want to make a numerical series, I can do so easily with: series.numbers <- 1:10 But, I don't seem to be able to do the same with time. I want to create a vector with 480 points that corresponds to the 480 minutes in a 8 hour work day. Thus I want series.time to look something like this: 9:00 9:01 9:02 9:03 etc. Last night I managed to build this by concatenating a series of

I want to learn how to use the reshape package. The reshape package is not included in the Ubuntu repositories, so I attempted to install reshape with: install.packages("reshape") This is what I got for output: Warning in install.packages("reshape") : argument 'lib' is missing: using '/home/andy/R/i486-pc-linux-gnu-library/2.7'

...v Test (Uwe Ligges) > 13. Re: memory limit (seanpor) > 14. Re: Very slow: using double apply and cor.test to compute > correlation p.values for 2 matrices (jim holtman) > 15. how to check linearity in Cox regression (Terry Therneau) > 16. Chi-Square Test Disagreement (Andrew Choens) > 17. Re: eclipse and R (Tobias Verbeke) > 18. Finding Stopping time (Debanjan Bhattacharjee) > 19. Re: Very slow: using double apply and cor.test to compute > correlation p.values for 2 matrices (David Winsemius) > 20. memory limit (iwalters) > 21. Needs suggestions...

I am trying to automate a report that my company does every couple of years for the state of Maine. In the past we have used SPSS to run the data and then used complicated Excel template to make the tables/graphics which we then imported into Word. Since there are 256 tables/graphics for this report, this work flow is a little painful. I would like to automate the process and I think I can do

I need to do some fairly deep tables, and ftable() offers most of what I need, except for the weighting. With smaller samples, I've just used replicate to let me have a weighted data set, but with this data set, I'm afraid replicate is going to make my data set too big for R to handle comfortably. That being said, is there some way to weight my table (similar to wtd.table) but offer the

I have a dataframe with the following variables: idnum area gender race etc. I would like to make a table that looks like area gender race M F B W A 1 4 5 3 5 1 2 6 7 4 6 3 etc. Basically, I want to make a single broad table with a number of sub-set tables. I have tried: cbind(table(area, gender), table(area, race)) But, when I do this, I lose the labels gender / race. This makes it

I am trying to import a SPSS.sav file into R. The attached file is not technically the file I am trying to import, but does replicate my problem. The actual file is much too large to attach. No matter what I do, I can not get R (base or Hmisc) to apply the value labels in the .sav file to the dataframe created in R. Here's the code that I am using. maine <- spss.get("test.sav") #

I would like to create a random sample of the rows of a data frame that is larger than the number of rows in the data frame. With an individual vector, this is easy using select(variable, number, replace = TRUE). I looked on-line I found some guides to sample from a data frame using indexing, but I don't seem to be able to get the results that I want. # Example: name <- c("andy",

"The R Inferno" is now on the Burns Statistics website at http://www.burns-stat.com/pages/Tutor/R_inferno.pdf Abstract: If you are using R and you think you're in hell, this is a map for you. Also, I've expanded the outline concerning R on the Burns Statistics 'Links' page. Suggestions (off-list) for additional items are encouraged. Patrick Burns patrick at