You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code.
There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment.
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Packages should be loaded at the top of the script, so it's easy to see which ones the example needs.
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The easiest way to include data in an email is to use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I'd perform the following steps:
- Run
dput(mtcars)in R - Copy the output
- In my reproducible script, type
mtcars <-then paste.
- Run
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Spend a little bit of time ensuring that your code is easy for others to read:
- make sure you've used spaces and your variable names are concise, but informative
- use comments to indicate where your problem lies
- do your best to remove everything that is not related to the problem.
The shorter your code is, the easier it is to understand.
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Include the output of sessionInfo() as a comment. This summarises your R environment and makes it easy to check if you're using an out-of-date package.
You can check you have actually made a reproducible example by starting up a fresh R session and pasting your script in.
Before putting all of your code in an email, consider putting it on http://gist.github.com/. It will give your code nice syntax highlighting, and you don't have to worry about anything getting mangled by the email system.