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I am working on a reproducible computing platform for which I would like to be able to _exactly_ reproduce an R object. However, I am experiencing unexpected randomness in some calculations. I have a hard time finding out exactly how it occurs. The code below illustrates the issue. mylm1 <- lm(dist~speed, data=cars); mylm2 <- lm(dist~speed, data=cars); identical(mylm1, mylm2); #TRUE

I am looking for a way to save a plot (graphics contents) to a file after the plot has been calculated but before it has been rendered. More specifically, assume that I made a plot that took a very long time to produce, I would like to save this plot to a generic file that I can later, on a different machine, render to either PDF, PNG, SVG using the usual R graphics devices, without recalculating

I am looking for a way to save a plot (graphics contents) to a file after the plot has been calculated but before it has been rendered. More specifically, assume that I made a plot that took a very long time to produce, I would like to save this plot to a generic file that I can later, on a different machine, render to either PDF, PNG, SVG using the usual R graphics devices, without recalculating

Is there a way to call a function, and explicitly set an argument to 'not specified'? My situation is the following. I have a function which passes on most of its arguments to another function. The second function, myfun2, serializes all arguments and is out of my control. myfun <- function(...){ return(myfun2(...)); } now, the value for arguments of myfun are stored in variables.

According to the download.file manual the timeout of a connection can be set using options(timeout=10). This seems to work as expected on windows, but on linux the connection does not timeout. I reproduced the problem both 0on R-2.13 on Ubuntu and on R-2.12.1 on CentOS, but not in Windows. > options(timeout=5) > download.file("http://123.123.123.123/bla", dest=tempfile()) I am

I am working on a system to visualize survey responses. Survey responses typically include factors, numeric, timestamps, textfields and therefore fit perfectly nice in dataframes, making it easy to visualize using standard R functions. However I am currently working on a survey that also include questions in which the respondent can check more than one answer on a single multichoice item. I.e.