Origin of the idea Recently the team I work with has had a few new members and I’ve been thinking lately of ways we could try to help them. The team leader was traveling this week, which gave me the opportunity to come up with a new type of session and test it out. That’s the origin of this learning from our search history idea. We tested it today and I’m quite happy with the results so far, so I thought it would be useful to document what we did and share it with others.
Recently I’ve been thinking on the subject of asking for help. In short, it’s hard to ask for help. It involves admitting to yourself that you can’t solve the problem alone, opening yourself up, hoping that another person will understand you and guide you in the right direction. Thus it can be painful if your request for help is misunderstood, met with criticism or ignored. Regardless of these obstacles, I think that the potential rewards make it worth it.
This is a joint blog post between Stephanie Hicks and Leonardo Collado-Torres. We want to share with you our experience using Slack and why you should join us. This post is in an interview style.
What is Slack? [SH] Slack is a communication tool for teams. The main idea is you have individual chat rooms (referred to as channels that always begin with the # symbol), which are organized by topics.
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.
tl;dr Please post your question at the Bioconductor support website https://support.bioconductor.org/ and check the posting guide http://www.bioconductor.org/help/support/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.
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.
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.