I’ve given a version of this talk at all three EARL conferences this year starting in San Francisco. I think this is a measure of the extent to which I believe bridging the gap between Data Science and Ops is becoming increasingly important.
There is a perception in many Ops teams that Data Science lives outside the organisational mainstream, and to some extent or other “gets away” with doing what it likes.
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Pre-requisites Before we begin, it’s a good idea to install some general purpose tools that will help us out once R is installed.
The first of these dependencies are a bunch of development tools. These are needed because on Linux, packages are generally installed from source, and if they contain any compiled code, will need to ensure that we have the compilers and build tools available.
I take a shortcut here and just install the pre-configured group called ‘Development Tools’.
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I wrote a really quick script to install R packages from the command line that I thought I’d share.
It doesn’t really do a great deal, but you can use it to install one package at a time.
Save the below as rpkginstall and make sure it’s executable with chmod + x rpkginstall.
Then you can install a package like this example, which would install dplyr, ./rpkginstall dplyr.
#!/usr/bin/env bash pkg=${1} function help { echo "Usage: $(basename $0) <package name>" exit 1 } [ -z ${pkg} ] && help REXEC=$(which R) if [ -z ${REXEC} ]; then echo "R not found, please ensure R is available and try again.
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Awesome Blogdown Collage by [Mara Averick](https://twitter.com/dataandme) Earlier this year, at RStudio::conf 2017, I heard about Yihui Xie’s Blogdown package for the first time. Blogdown is a fantastic tool kit for static website generation that works with R and RMarkdown, and built on top of the hugo static website generator.
I had used hugo a little for a previous version of my blog, but I update so infrequently that I’d forget how to use it from one update to the next.
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Anyone that I’ve ever talked to about RPMs knows that I can’t stand the ‘official’ rpmbuild tools.
For whatever reason, I’ve just never got on with them. I know loads of people who use them all the time without complaint, but the whole process has always seemed dysfunctional to me. That’s why, for the vast majority of my packaging needs, I turn to fpm. Using fpm allows me to quickly create RPMs and debs with a really simple, single command line call.
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This post was originally published on the Mango Solutions blog.
At Mango we work with a great many clients using the Shiny framework for R. Many of those use Shiny Server or Shiny Server Pro to publish their shiny apps within their organisations. Shiny Server Pro in particular is a great product, but some of the stock html pages are a little plain, so I asked the good folks at RStudio if it was possible to customise them to match corporate themes and so on.
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Turn a shiny application into a tablet or desktop app Since we first demoed it at our really successful trip to Strata London last year, a few people have asked us how we made the awesome looking Data Science Radar app that we were running on the tablets we had with us. In this post we’ll take a look at how we did it, and hopefully show you how easy it is to do yourself.
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This is a really quick one, as we use this trick a lot when working remotely, but we always have to scrabble around to find the info!
We use the open source OpenVPN for our office VPN. In general it’s great, however when working away from the office, it’s configured such that any request for a resource first checks outside of the firewall to see if it can access it. If it can, it does, no VPN involved.
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This post originally appeared on the Mango blog, here - http://www.mango-solutions.com/wp/2017/02/production-r-at-ons/
I’ve recently been working at the Office for National Statistics, under the very broad umbrella of a SAS to R transition project.
As you might imagine, ONS, as the UK’s largest producer of official statistics, has a huge number of internal statistical applications, so it’s not possible to “simply” switch from SAS to R. Working with various product owners and maintainers, a small team from Mango were able to perform a deep dive into a few of their existing applications which rely on SAS for statistical processing, and identify some likely candidates for proof of concept transitions.
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If you were in an environment where you have R running on server on a secure LAN with no internet access, you’ll be familiar with the situation outlined in the image above. R is running in an environmnet where there is no internet access, and you’re therefore unable to install anything from CRAN. This is common in many large organisations where the security of the systems is more important than end user convenience.
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