7 Reasons for Policy Professionals to Get Pumped About R Programming in 2019

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With the rise of ‘Big Data’, ‘Machine Learning’ and the ‘Data Scientist’ has come an explosion in the popularity of using open-source programming tools for data analysis.

This article provides a short summary of some of the evidence of these tools overtaking commercial alternatives and why, if you work with data, adding an open programming language, like R or Python, to your professional repertoire is likely to be a worthwhile career investment for 2019 and beyond.

Like most faithful public policy wonks, I’ve spent more hours than I can count dragging numbers across a screen to understand, analyse or predict whatever segment of the world I have data on.

Exploring where the money was flowing in the world’s youngest democracy; analysing which government program was delivering the biggest impact; or predicting which roads were likely to disappear first as a result of climate change.

New policy questions, new approaches to answer them and a fresh set of data.

Yet, every silver-lining has a cloud. And in my experience with data it’s often the need to scale a new learning curve to adhere to legacy systems and fulfil an organizational fetish for using their statistical software of choice.

Excel, SAS, SPSS, Eviews, Minitab, Stata and the list goes on.

Which is why I’ve decided this article needed to be written:

Because not only am I tired of talking to fellow analytical wonks about why they’re limiting themselves by only being able to work on data with spreadsheets, but also that there are distinct professional advantages to unshackling yourself from the professional tyranny of proprietary tools:

  1. Open-Source Statistics is Becoming the Global Standard

Firstly, if you haven’t been watching, the world is increasingly full of data. So much data, that the world is chasing after nerds to analyse it. As a result, the demand for a ‘jack of all trades’ data person, or “data scientist” has been outstripping that of a more vanilla-flavoured ‘statistician’:

% Job Advertisements with term “data scientist” vs. “statistician”

(Credit: Bob Muenchen – r4stats.com)

And although you might not have aspirations to work in what the Harvard Business Review called the ‘Sexiest Job of the 21st Century’ the data gold rush has had implications far beyond the sex appeal of nerds.

For one, online communities like Stackoverflow, Kaggle and Data for Democracy have flourished. Providing practical avenues for learning how to do some science with data and driving demand for tools that make applying this science accessible to everyone, like R and Python.

So much, that some of the best evidence, suggests that not only is demand for quants with R and Python skills booming, but the practical use of open-source statistical tools like R and Python are starting to eclipse their proprietary relatives:

Statistical software by Google Scholar Hits:

(More credit to Bob Muenchen – r4stats.com)

Of course, I’m not here to conclusively make the point that a particular piece of software is a ‘silver bullet’. Only that something has happened in the world of data that the quantitatively inclined shouldn’t ignore: Not only are R and Python becoming programming languages for the masses, but they’re increasingly proving themselves as powerful complements to more traditional business analysis tools like Excel and SAS.

2. R is for Renaissance Wo(Man)

For those watching the news, you’ll no doubt have heard of the great battle being waged between the R and Python languages that has tragically left the internet strewn with the blood of programmers and their pocket protectors.

But I’m going to goosestep right over the issue as in my opinion much of what I say for R, is increasingly applicable to Python.

For those of you unfamiliar with R, in essence it’s a programming language made to use computers to do stuff with numbers.

Enter: “10*10” and it will tell you ‘100’ 

Enter: “print(‘Sup?’)” and the computer will speak to you like that kid loitering on your lawn.

Developed around 25 years ago, the idea behind R was in essence to develop a simpler, more open and extendible programming language for statisticians. Something which allowed you greater power and flexibility than a ‘point and click’ interface, but that was quicker than punch cards or manually keying in 1s and 0s to tell the computer what to do.

The result: R – A free statistical tool whose sustained growth use has led to one of the most flexible statistical tools in existence.

So much growth in fact, that in 2014 enough new functionality was added to R by the community that “R added more functions/procs than the SAS Institute has written in its entire history.” And while it’s not the quantity of your software packages that counts, the speed of development is impressive and a good indication of the likely future trajectory of R’s functionality. Particularly as many heavy hitters including the likes of Microsoft, IBM and Google are already using R and making their own contributions to the ecosystem:  

Using R for Analytics – Get in Before George Clooney Does:

Image source. Also, see here

Not only that, but with much of this growth being driven by user contributions, it is also a great reminder of the active and supportive community you have access to as an R and Python user. Making it easier to get help, access free resources and find example code to steal base your analysis on.

3. R is Data and Discipline Agnostic

(Source: xkcd)

One of the first things that motivated me to learn R, was the observation that many of the most interesting questions I encountered went unanswered because they crossed disciplines, involved obscure analytical techniques, or were locked away in a long-forgotten format. It therefore seemed logical to me that if I could become a data analytics “MacGyver”, I’d have greater opportunities to work on interesting problems.

Which is how I discovered R. You see, as somebody that is interested in almost everything, R’s adoption by such a diverse range of fields made it nearly impossible to overlook. With extensions being freely available to work with a wide variety of data formats (proprietary or otherwise) and apply a range of nerdy methods, R made a lot of sense.

I think it was Richard Branson that once said “If somebody offers you a problem but you are not sure you can do it, say yes. R probably has a package for it”:

Then R (and increasingly Python) has you covered.

Yet there is perhaps a subtler reason adopting R made sense and that’s the simple fact that by being ‘discipline agnostic’ it’s well-suited for multidisciplinary teams, applied multi-potentialites and anyone uncertain about exactly where their career might take them.

4. R Helps Avoid Fitting the Problem to the Tool

As an economist, I love a good echo chamber. Not only does everybody speak my language and get my jokes, but my diagnosis of the problem is always spot-on. Unfortunately, thanks to errors of others, I’m aware that such cosy teams of specialists, isn’t always a good idea – with homogeneous specialist teams risking developing solutions which aren’t fit for purpose by too narrowly defining a problem and misunderstanding the scope of the system it’s embedded in.

(Source: chainsawsuit.com)

While good organizations are doing their best to address this, creating teams that are multidisciplinary and have more diverse networks can be a useful means to protect against these risks while also driving better performance. Which of course stands to be another useful advantage of using more general statistical tools with a diverse user base like R: as you can more fluidly collaborate across disciplines while being better able to pick the right technique for your problem, reducing the risk that everything look like a nail, merely because you have a hammer.  5. Programming Encourages Reproducibility

Yet programming languages also hold an additional advantage to more typical ‘point and click’ interfaces for conducting analysis – transparency and reproducibility.  

For instance, because software like R encourages you to write down each step in your analysis, your work is more likely to be ‘reproducible’ than had it been done using more traditional ‘point and click solutions. This is because you’re encouraged to record each step needed to achieve the final result making it easier for your colleagues to understand what the hell you’re doing and increasing the likelihood you’ll be able to reproduce the results when you need to (or somebody else will).

In addition to this being practically useful for tracing your journey down the data-analysis-maze, for analytical teams it can also serve as a means for encouraging collaboration by allowing to more easily understand your work and replicate your results. Assisting with organizational knowledge retention and providing an incentive for ensuring analysis is accurate by often making it easier to spot errors before they impact your analysis or soil your reputation.

Finally, while the use of scripting isn’t unique to open-source programming languages, by being free, R and Python comes with an additional advantage that in the instance you decide to release your analysis, the potential audience is likely to be greater and more diverse than had it been written using propriety software. Which is why in a world of the “Open Government Partnership” open-source programming languages makes a lot of sense, providing a means of easing the transition towards government publicly releasing government policy models.

6. R Helps Make Bytes Beautiful  

As data-driven-everything becomes all the rage, making data pretty is becoming an increasingly important skill. R is great at this, with virtually unlimited options for unleashing your creativity on the world and communicating your results to the masses. Bar graphs, scatter diagrams, histograms and heat maps. Easy.

Just not pie graphs. They’re terrible.  

But R’s visualization tools don’t finish at your desk, with the ‘Shiny’ package allowing you to take your pie graphs to the bigtime by publishing interactive dashboards for the web. Boss asking you to redo a graph 20 times each day? Outsource your work to the web by automating it through a dashboard and send them a link while you sip cocktails at the beach.

7. R and Python are free, but the Cost of Ignoring the Trend Towards Open-Source Statistics Won’t Be

Finally, R and Python are free, meaning not only can you install it wherever you want, but that you can take it with you throughout your career:

  • Statistics lecturers prescribing you textbooks that are trying to get you hooked on expensive software that likely won’t exist when you graduate?  Tell them it’s not 1999 and send them a link to this.
  • Working for a not-for-profit organization that needs statistical software but can’t afford the costs of proprietary software? Install R and show them how to install Swirl’s free interactive lessons.
  • Want to install it at home? No problem. You can even give a copy to your cat.
  • Got a promotion and been gifted a new team of statisticians?  Swap the Christmas bonuses for the give the gift that keeps giving: R!

But I’m not here to tell you R (or Python) are perfect. Afterall, there are good reasons some companies are reluctant to switch their analysis to R or Python. Nor am I interested in convincing you that it can, or should, replace every proprietary tool you’re using. As I’m an avid spreadsheeter and programs like Excel have distinct advantages.

Rather, I’d like to suggest that for all the immediate costs involved in learning an open-source programming language, whether it be R or Python, the long-term benefits are more than likely to surpass them.


Not only that, but as a new generation of data scientists continue to push for the use of open-source tools, it’s reasonable to expect R and Python will become as pervasive a business tool as the spreadsheet and as important to your career as laughing at your boss’ terrible jokes.  

Interested in learning R? Check out this link here for a range of free resources.

You can also read my review of the online specialization I took to scale the R learning curve here.

To leave a comment for the author, please follow the link and comment on their blog: R-Programming – Giles Dickenson-Jones.

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