After the success of rstudio::conf 2017 this year the conference was back and bigger and better than ever with 1000+ attendees in sunny San Diego. Since the conference, my colleagues and I have been putting the techniques we learned into practice (which is totally why you’re only seeing this blog post now!).
Day 1 – shiny stole the show
The first stream was all things Shiny. With all the hype surrounding Shiny in the past few years, it didn’t disappoint. Joe Cheng spoke at the EARL London Conference in September last year about the exciting new feature allowing users to take advantage of asynchronous programming within Shiny applications through the use of the
promises are not just Shiny specific and Joe is hoping to release the package on CRAN soon. In the meantime you can check out the package here.
At mango we’re already excited to start streamlining existing and future customer applications using
promises. From a business point of view, it’s going to allow us to build more efficient and complex applications.
Straight after Joe was RStudio’s Winston Chang. Winston gave another great demo – this time showing the new features of the
shinytest package. As well as improved user interaction, compared to previous
shinytestversions, Winston demonstrated the latest snapshot comparison feature. This allows users to compare snapshots side by side when re-running tests and interactively dragging images to compare between them.
This is another potentially exciting breakthrough in the world of Shiny. Testing user interface components of a Shiny app has historically been a manual process, so formalising this process with
shinytest will hopefully provide the framework to take proof of concept applications into a validated production ready state. You can check out the latest version here.
We were also excited to hear RStudio have built their own load testing tools which they’ll make available for us as well. Traditional tools for load testing often are incompatible with Shiny apps. RStudio’s main goals were to create something that’s easy to use, can simulate large number of users, and can work well with Shiny apps. It has multiple features in its workflow, such as recording, playback, and result analysis, and we envisage it enabling our customers to get really in-depth metrics on their Shiny apps.
Day 2 – machine learning
Aside from Shiny, a main theme of the conference was undoubtedly machine learning.
Day 2 kicked off with a key note from J.J Allaire, RStudio’s CEO. J.J’s presentation “Machine Learning with R and TensorFlow” was a fantastic insight into how RStudio have been busy in the past year making TensorFlow’s numerical computing library available to the R community. The
keras package opens up the whole TensorFlow functionality for easy use in R, without the need to learn Python. It was great to hear TensorFlow explained in such a clear way and has already sparked interest and demand at Mango for our new “Deep Learning with keras in R” course (which, you can attend if you sign up for the EARL London Conference _hint hint)).
The interop stream gave us an insight into the leading technologies integrating with and exciting the world of R. With TensorFlow and Keras being machine learning buzz words at the moment, Javier Luraschi explained how to deploy TensorFlow models for fast evaluation and export using the
tfdeploy package. He also highlighted integration with other technologies, such as
Next year the conference has already been announced to run in Austin, Texas. Workshop materials and slides from this year’s conference can be found here.