Updating R

As you might know by now, the latest R version was recently released (R 3.4.0). That means that you are highly encouraged to update your R installation. There are several ways to do this some of which are documented in these other blog posts: Tal Galili, 2013, Kris Eberwein, 2015. You would think that it’s just a matter of downloading the latest R installer for your OS, installing it, and continuing your analysis.

How to ask for help for Bioconductor packages

tl;dr Please post your question at the Bioconductor support website and check the posting guide It’s important that you provide reproducible code and information about your R session. Recently I have been getting more questions about several packages I maintain. It’s great to see more interest from users, but at the same time most questions lack the information I need to help the users. I have also gotten most of the questions via email, which is why I am writing this post.

Use hidden advanced arguments for user-friendly functions

As a user Imagine that you are starting to learn how to use a specific R package, lets call it foo. You will look at the vignette (if there is one), use help(package = foo), or look at the reference manual (for example, devtools’ ref man). Eventually, you will open the help page for the function(s) you are interested in using. ?function_I_want_to_use In many packages, there is a main use case that is addressed by the package.

Trying to reduce the memory overhead when using mclapply

I am currently trying to understand how to reduce the memory used by mclapply. This function is rather complicated and others have explained the differences versus parLapply (A_Skelton73, 2013; lockedoff, 2012 ) and also made it clear that in mclapply each job does not know if the others are running out of memory and thus cannot trigger gc (Urbanek, 2012). While I still struggle to understand all the details of mclapply, I can successfully use it to reduce computation time at the expense of a very high memory load.