Over the last couple of days, I’ve been fettling the build scripts for the TM351 VM, which typically uses vagrant to build a VirtualBox VM from a set of shell scripts, so they can be used to build a single Docker container that runs all the TM351 services, specifically Jupyter notebooks, OpenRefine, PostgreSQL and MongoDB. Docker
For ref, when running IRkernel Jupyter R notebooks, media objects can be embedded by making use of the shiny::tags function, that can return HTML elements with appropriate MIME types, and are renderable using _repr_html machinery (h/t @flying-sheep): For example: PS By the by, I notice the existence of another R kernel for Jupyter notebooks, JuniperKernel.
I notice from the binder-examples/r repo that Binderhub now appears to offer all sorts of R goodness out of the can, if you specify a particular R build. From the same repo root, you can get: Jupyter notebook+R: http://beta.mybinder.org/v2/gh/binder-examples/r/master?filepath=index.ipynb RStudio: http://beta.mybinder.org/v2/gh/binder-examples/r/master?urlpath=rstudio A Shiny app: http://beta.mybinder.org/v2/gh/binder-examples/r/master?urlpath=shiny/bus-dashboard/ And from previously, here’s a workaround for displaying R/HTMLwidgets in
It’s coming round to that time of year where we have to create the assessment material for courses with an October start date. In many cases, we reuse question forms from previous presentations but change the specific details. If a question is suitably defined, then large parts of this process could be automated. In the OU, automated
A quick note for displaying R htmlwidgets in Jupyter notebooks without requiring pandoc – there may be a more native way but this acts as a workaround in the meantime if not: PS and from the other side, using reticulate for Python powered Shiny apps.
In a couple of recent posts (Textualisation With Tracery and Database Reporting 2.0 and More Tinkering With PyTracery) I’ve started exploring various ways of using the pytracery port of the tracery story generation tool to generate variety of texts from Python pandas data frames. For my F1DataJunkie tinkerings I’ve been using R + SQL as
A bit more tinkering with F1 data from the ergast db, this time trying to generating trivia / facts around races. The facts are identified using SQL queries: Some of the queries also embed query fragments, which I intend to develop further… I'm using knitr to generate Github flavoured markdown (gfm) from my Rmd docs
I’m back on the R thang with F1 data from ergast.com, and started having a look at how drivers and teams compare at a circuit. One metric I came across for comparing teams over a season is the supertime, typically calculated for each manufacturer as the average of their fastest single lap recorded by the team
Yesterday saw the public reboot of Binder / MyBinder (which I first wrote about a couple of years ago here), as reported in The Jupyter project blog post Binder 2.0, a Tech Guide and this practical guide: Introducing Binder 2.0 — share your interactive research environment. For anyone not familiar with Binder / MyBinder, it’s
Got some data, relating to how students move from one module to another. Rows are student ID, module code, presentation date. The flow is not well-behaved. Students may take multiple modules in one presentation, and modules may be taken in any order (which means there are loops…). My first take on the data was just
