TL;DR If you like dplyr progress bars, and wished you could use them everywhere, including from within Rmd documents, non-interactive shells, etc, then you should check out knitrProgressBar (cran github).
Why Yet Another Progress Bar?? I didn’t set out to create another progress bar package. But I really liked dplyrs style of progress bar, and how they worked under the hood (thanks to the examples from Bob Rudis).
As I used them, I noticed that no progress was displayed if you did rmarkdown::render() or knitr::knit().
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. Options: –string=<string_value> A string value [default: Hi!
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).
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.