TL;DR If you include others code in your own R package, list them as contributors with comments about what they contributed, and add a license statement in the file that includes their code.
Motivation I recently created the knitrProgressBar package. It is a really simple package, that takes the dplyr progress bars and makes it possible for them to write progress to a supplied file connection. The dplyr package itself is licensed under MIT, so I felt fine taking the code directly from dplyr itself.
TL;DR If you use the docopt package to create command line R executables that take options, there is something to know about numeric command line options: they should have as.double before using them in your script.
Setup Lets set up a new docopt string, that includes both string and numeric arguments.
" Usage: test_numeric.R [–string=<string_value>] [–numeric=<numeric_value>] test_numeric.R (-h | –help) test_numeric.R Description: Testing how values are passed using docopt.
TL;DR Use a short bash script to do deployment from your own computer directly to your *.github.io domain.
Why? So Yihui recommends using Netlify, or even Travis-CI in the Blogdown book. I wasn’t willing to setup a custom domain yet, and some of my posts involve a lot of personally created packages, etc, that I don’t want to debug installation on Travis. So, I wanted a simple script I could call on my laptop that would copy the /public directory to the repo for my github.
Manual Linking? Using blogdown for generating websites and blog-posts from Rmarkdown files with lots of inserted code and figures seems pretty awesome, but sometimes you want to include a figure manually, either because you want to generate something manually and convert it (say for going from SVG of lots of points to hi-res PNG), or because it is a figure from something else (like this figure from wikipedia).
Where to?
I don’t remember how I got on this, but I believe I had a recent twitter exchange with some persons (or saw it fly by) about pushing R package vignettes to the web after building and checking on travis-ci. Hadley Wickham pointed to using such a scheme to push the web version of his book after each update and the S3 deploy hooks on travis-ci. Deploying your html content to S3 is great, but given the availability of the gh-pages branch on GitHub, I thought it would be neat to work out how to deploy the html output from an R package vignette to the gh-pages branch on GitHub.
TL;DR Instead of writing an analysis as a single or set of R scripts, use a package and include the analysis as a vignette of the package. Read below for the why, the how is in the next post.
Analyses and Reports As data science or statistical researchers, we tend to do a lot of analyses, whether for our own research or as part of a collaboration, or even for supervisors depending on where we work.
Following from my last post, I am going to go step by step through the process I use to generate an analysis as a package vignette. This will be an analysis of the tweets from the 2012 and 2014 ISMB conference (thanks to Neil and Stephen for compiling the data).
I will link to individual commits so that you can see how things change as we go along.
Setup Initialization To start, we will initialize the package.
tl;dr Imposing a different structure than R packages for distributing R code is a bad idea, especially now that R package tools have gotten to the point where managing a package has become much easier.
ProjectTemplate ?? My last two posts (1, 2) provided an argument and an example of why one should use R packages to contain analyses. They were partly motivated by trends I had seen in other areas, including the appearance of the package ProjectTemplate.
There has been some interesting activity about getting R to send a notification somehow when a long running job is completed. The most notable entries I have seen in this category are RPushBullet for web notifications and pingr for audio notifications.
Although RPushBullet looks really cool (and Dirk does great work), I wondered if there was a way to do this using a free service that I already had access to, namely twitter.
I can finally say that the publication on my Bioconductor package categoryCompare is finally published in the Bioinformatics and Computational Biology section of Frontiers in Genetics. This has been a long time coming, and I wanted to give some background on the inspiration and development of the method and software.
TL;DR The software package has been in development in one form or another since 2010, released to Bioconductor in summer 2012, and the publication has bounced around and been revised since spring of 2013, and it is finally available to you.