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.

Easily explore a table with shinycsv

Have you ever had to explore a table with data? I believe the answer is yes for most people that work at a computer or even just use it for communicating with their friends and family. Tables of data pop up everywhere, for example in personal finance. Websites like allow you to download your transactions in a CSV file called transactions.csv. CSV is one of the many formats for storing tables and most likely when you try to open the transactions.

Finding possible class schedules

Over the weekend my brother wanted to figure out his class schedule for the next semester. He is a veterinary medicine and zootechnology student at UNAM. For this upcoming semester there is a set of classes he has to take and each has 8 or so instructor options. The website where he finds the class times lists about 8 pre-constructed class schedules. So he normally finds one he likes quite a bit, and then manually starts checking if he can change X instructor for Y for a given class.

Are you doing parallel computations in R? Then use BiocParallel

It’s the morning of the first day of oral conferences at #ENAR2016. I feel like I have a spidey sense since I woke up 3 min after an email from Jeff Leek; just a funny coincidence. Anyhow, I promised Valerie Obenchain at #Bioc2014 that I would write a post about one of my favorite Bioconductor packages: BiocParallel (Morgan, Obenchain, Lang, and Thompson, 2016). By now it’s on the top 5% of downloaded Bioconductor packages, so many people know about it or are unaware that their favorite package uses it behind the scenes.

Teaching a short topic to beginner R users

A couple weeks ago I was given the opportunity to teach a 1 hr 30 min slot of an introduction to R course. In the past, I’ve taught lectures for similar courses, and I ended up asking myself what would be the best short topic to teach and how to teach it. Best short topic There are two ways to answer the first question, one boring and one more interesting.

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.

An xpd-tion into R plot margins

This is a guest post by Prasad Patil that answers the question: how to put a shape in the margin of an R plot? The help page for R’s par() function is a somewhat impenetrable list of abbreviations that allow you to manipulate anything and everything in the plotting device. You may have used this function in the past to create an array of plots (using mfrow or mfcol) or to set margins (mar or mai).

Where do I start using Bioconductor?

I was recently asked where do I get started with Bioconductor? and thought this would be a good short post. What is BioC? Briefly, Bioconductor (Gentleman, Carey, Bates, and others, 2004) is an open source project that hosts a wide range of tools for analyzing biological data with R (R Core Team, 2014). These analysis tools are bundled into packages which are designed to answer specific questions or to provide key infrastructure.

Concerns that can deter potential orders for developing Shiny apps

A few weeks ago I was invited to a meeting where a group was interested in exploring options for replacing their contract with a propriety software. They invited me because they saw some resemblances between a Shiny application I made and the features they need. It is a relatively small project and it seemed feasible to implement, but well, some details could have been tricky to code. During the meeting I explained what Shiny is, showcased some of the Shiny apps I’ve made, and proposed some options including a simple site password.