We caught up with Emily Riederer ahead of her presentation at the upcoming Enterprise Applications of the R Language Conference taking place online in September and asked her a few questions…
Hi Emily! So your talk at EARL is called ‘How to make R packages part of your team’, why is it so important to have a coherent internal ecosystem?
There are two reasons in my mind why I get excited about this. First is the most basic level, and the reason I got to thinking about it was as a pure efficiency play – it makes it so you can get the easy part of your job done and you can then look at the harder and more interesting complex parts. It makes work faster and more fun.
More holistically, I’ve realised that having a really solid internal ecosystem can actually be an amazing community building device. We are so lucky to have the external R Community, so having that structure to build off from, really helps to build a similar culture internally.
What problems could you run into if you didn’t have an internal ecosystem set up?
There can be a lot of inefficiencies in people solving similar problems in different ways and using different terms to describe their problems. I think that often large companies have silos – whether that’s a data silo or the structure of data teams. Using different tools can put us in boxes that we perhaps don’t need to be in.
Why do you think the R community is so special?
That’s a tough question! I think there are a couple of things that make it special – especially because R is a unique language – and I think it attracts a certain type of person. If you’re really passionate about R, you’re probably very curious about answering questions with data and maybe came to programming as your second choice – not your first. It’s an interesting group of people that are bound by similar challenges. Also thinking about the R community versus other tech communities, I think we have great informal leadership at the top, that really emphasises the importance of being inclusive and making space for newcomers. Other communities are defined by their ‘stars’ whereas we are lucky to have the whole community on board.
What can people expect to leave your talk knowing?
I hope that I can inspire the audience to think a little more critically and holistically about bringing internal packages into their own organisation in a couple of different ways. If they haven’t thought about it before, I’d like to spark some curiosity – could this work for their teams? I have also learned some hard lessons through trial and error, and for those who have started their journey, I hope I can lay out a more formal structure for them to follow. I want to help them to be able to succeed – we have many great external R packages too, and you don’t want to steal the limelight from those. It’s about finding the right balancing act of using internal packages and using them to solve the unique challenges where nothing can work.
Are there any developments in the Rstats world or are there any things that are on your list to try or learn?
Where do I start! That’s a good question. A couple of things that come to mind for me is the phenomenal interoperability work that is going on right now – better integration with R and Python and reticulate (even in the tidymodels space) making it equally easy to have one unified interface to hook up the many different back ends of different R packages to a common framework. I think that’s another interesting challenge with internal R packages, you definitely have to find good ways to recruit teams that are mostly Python people or mostly Microsoft Office people. So I think in the internal enterprise setting, all of the interoperability work is super exciting to me because it means we can go places and talk to people that we couldn’t have talked to before.
You can hear from Emily and a host of other Rstats users at this year’s EARL online Conference – tickets for the Friday session are just £9.99.
There are also four workshops in the week leading up to the Friday session – each workshop is taught by one of Mango’s expert Data Scientists and it is £90 for a half-day workshop.