By: Chris Dunleavy, Data Analyst
Unless you’re a programmer, there’s a decent chance you’ve never heard of R. But if you want to be in the business of data—and here’s why you should—then this industry standard should be on your radar. Here’s a quick down and dirty on this suite of tools for data visualization and analysis.
R is Excel’s lesser-known cousin.
This open-source environment is one of the best tools out there for data-first marketers. For one thing, R has far fewer limitations than Excel making it much more flexible (meaning you can deal with a lot more types of problems). For starters, the maximum number of rows in an Excel worksheet is fixed at just over 1 million, but R doesn’t have that limit. So if you plan on working with any big data sets, excel just wont cut it.
Because it’s a language as opposed to a closed-off application like Excel, R allows you to connect with packages that extend and enhance your ability to manipulate data—kind of like a (near) bottomless toolbox. Want to get data from Google Analytics? There’s a package for that. Need to create a reactive dashboard for yourself or a client? There is a wonderful package for that (it’s called Shiny!).
Best of all? It’s free. Your only costs are the time and effort you put into learning it—and trust me, they’re worth it.
Don’t throw out Excel entirely just yet, though, as R isn’t perfect as a total replacement for Microsoft’s industry standard. It's often much easier to do certain things in Excel, especially if that’s how you get the data in the first place. Apply filters, perform quick calculations, or use color scales to get a quick view of the data there. Once you start needing to create plots, or do any serious calculations however, move to R.
When it comes to analytics, R is second to none.
R shines when you’re slicing and dicing data. You can perform virtually any kind of manipulation or visualization your marketing strategy dictates. Clean your data (no unintentional blank or missing data, no erroneous commas or formatting/data type issues, outliers are identified and excluded when appropriate), filter/sort, create new features (columns when thinking about it in Excel), and create plots all within R. In short, R gives you complete control over your data.
Automation is another huge benefit. You can tell R exactly what you want to do, how often you want to do it, and then automate that process—making daily work (like running routine reports) much simpler and less tedious.
From linear and nonlinear modeling to time-series analysis to classification to clustering, R empowers you to understand and leverage your data. And if the list in that last sentence made your eyes glaze over, don’t worry because …
Help with understanding and using R abounds.
TIP: Hit up RStudio, which offers a free and open integrated development environment (IDE) and other tools for R that help make the language more visual-based for users of all experience levels. They are creating great addition that make working with R much easier and more accessible. Specifically check out Hadley Wickhams Tidyverse.org and the previously mentioned Shiny package.
For nonprogrammers, languages like R can be intimidating. But if you’re pulling reports or doing any data visualization, R is worth breaking out of your comfort zone. The best way to get acquainted is to just play with it. R is equipped with in-software documentation and search functionality that are refreshingly simple and accessible, even for the layman. Or check out R courses available at Lynda and Coursera.
There’s also an extensive online R community. You can almost Google any problem with “R” and find help or someone who has done something similar. Stackoverflow.com will become your best friend.
One way or another, data affects every part of your business and every person who contributes to it. Your data analysts should definitely know R, but it’s not a bad idea for others on your team to be familiar with it, too—its benefits are that universal. Ready to see firsthand how R can help make sense of your data? Download it here.
Chris is iostudio’s principal data analyst. With a Master of Science degree in analytics and a Google Analytics certification, he brings to the table an extensive technical skill set for data science spanning more than a dozen programming languages, softwares and techniques. Chris is adept at recommending advertising strategies by analyzing business potential based on clear data visualization and statistical modeling. View more of his profile on LinkedIn.