Organising useR!2017 was a challenge but a very rewarding experience. With about 1200 attendees of over 55 nationalities exploring an interesting program, we believe it is appropriate to call it a success - something the aftermovie only seems to confirm. Behind the Scenes To give you a glimpse behind the scenes of the conference organization, Maxim Nazarov held a lightning talk on ‘redmineR and the story of automating useR!
In our last blog post we mentioned the need people felt to integrate Shiny apps in content management systems like Drupal. Release 0.7.8 added one extra feature in this respect so people could also include ShinyProxy hosted Shiny apps in external iframes. It also kicked-off our effort to support service scalability and ever more complex apps. In the scalability area, we introduced facilities to manage and set memory limits on individual Shiny applications.
Since the last release (see this blog post), we received two interesting use cases for which we did not imagine ShinyProxy would be used. First of all, we received a request to support other authentication methods than LDAP authentication. Whereas ShinyProxy is currently used primarily by larger organizations and companies that typically work with LDAP-based authentication systems, it seems people appreciate the elegance of the framework also for more small-scale use.
Our previous post on the how and why of ShinyProxy triggered a lot of encouraging reactions. Here’s our favorite: Indeed choosing for Docker opens a world of possibilities for ShinyProxy and making you no longer dependent on a particular version of R or shiny is only one of the advantages. We also received a number of useful suggestions and decided to quickly release the new features and fixes as version 0.
ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Why is this needed? There is currently no valid open source alternative that offers this functionality. What does it offer? authentication authorization securing traffic with TLS/SSL usage statistics scalability This is free and open source, is there also a paying and proprietary version?
The knarrs of Open Analytics have left the port of Antwerp on their way to Denmark. What will our delegation bring to Aalborg besides our loyal sponsorship? Tuesday For starters we’ve just released a Dockerfile Editor which may be particularly useful for Dirk Eddelbuettel’s tutorial on Docker on June 30. An overview of all tutorials can be found here. Wednesday On the first day of the conference Willem Ligtenberg will present at 16:00 on how to use databases in R without a line of SQL with Rango.
In the last two years, software development had taken a step forward with the advent of the Docker. For the uninitiated, a Docker is a tool that automates the deployment of applications by packaging them with their dependencies in a virtual container, eliminating the need for virtual machines. Docker has streamlined the process of application development on Linux servers, and Open Analytics has streamlined the creation of Docker files with Architect’s new Dockerfile Editor.
As R has continued its growth in populary, it’s made some exotic friends. Friends who speak other (programming) languages. Friends who live on servers and virtual machines. Friends who sometimes need to set aside their differences and work towards a common goal. In the absence of a protocol droid or Babel fish, we have the enterprise service bus. For the uninitiated, an enterprise service bus is a software architecture model designed to interface between various software applications.
The first time I discovered data.table it felt like magic. I was waiting on a process that was projected to take the better part of an afternoon. In the meantime, I followed the data.table tutorial, rewrote my code using the data.table structure, and fully executed said code, all while the data.frame equivalent was wheezing along. In the last year, data.table has gotten even faster. data.table’s Automatic Indexing For the uninitiated, data.
Introduction “Can you check if this is significant?” It was a seemingly innocuous question from a dangerous source: a semi data-literate scientist. The kind who believed, deep in his heart, that small p-values were “good” and large p-values were “erroneous”. On this day, the man in question had come forth with a large, complex multivariate dataset. He’d manually combed the data, visually inspected it, and hand-picked a hypothesis. “Can you check if this is significant?