This Is How Rprogramming Will Look Like In 10 Years

Every business is trying to figure out the best way to understand information about their customers and themselves. But simply using Excel pivot tables to analyze such quantities of information is absurd, so many companies use the commercially available tool SAS to cull business intelligence.

But SAS is no match for the open-source language that pioneering data scientists use in academia, which is simply known as R. The R programming language leans more frequently to the cutting edge of data science, giving businesses the latest data analysis tools. 

THE R EVANGELISTS

At least one company thinks R is ready for commercial prime time. Like RedHat is to Linux and Cloudera is to Hadoop, Revolution Analytics is to the R language in the commercial world. Several years ago, David Smith, chief community officer at Revolution Analytics, noticed that a lot of academics and students used R but saw less usage in industry. “At the time, there was no company there to support R, provide expertise around R, or provide any kind of commercial backing for R. So that’s how Revolution Analytics was founded,” says Smith.

The majority of R's modifying libraries are free, yet Revolution Analytics makes its business from its administration bundles, which give clients access to the libraries the organization creates in-house. Not solely constrained to R, Revolution Analytics likewise makes UIs and calculations, habitually utilizing C++ to compose its calculations.

DEALING WITH TONS OF BUSINESS DATA

Here’s what their packages actually do. One, ScaleR, helps businesses go through all of their data by scaling it to work on parallel processors. Using standard R packages, machines will run out of memory when dealing with such large amounts of data, but ScaleR repurposes the data to process chunks of it on different servers simultaneously. Smith calls this parallel processing algorithm its “secret sauce.”

COOL GRAPHICS

At Facebook, the data science team’s data visualizations in R give it the best overview of what kind of data it is dealing with. The data can range from something like News Feed numbers to correlations with the amount of Facebook friends a user has. Although these packages are not commercial, Revolution Analytics has kept tabs on Facebook’s R usage for some time.

“Generally, we use R to move fast when we get a new data set,” says Solomon Messing, data scientist at Facebook. “With R, we don’t need to develop custom tools or write a bunch of code. Instead, we can just go about cleaning and exploring the data.”

TALENT IS EVERYWHERE

In school, data scientist Casey Herron studied statistics and came to Revolution Analytics with an already intimate understanding of R. Having used R as an undergraduate, she continued using it in her master’s program and when she moved into her first job after graduate school, as a statistician. She has now been at Revolution Analytics for 10 months.

“I think the number one value to businesses [in using R] is access to talent,” says Smith. “So many businesses now are doing much more with data, especially with the big data revolution and doing much more with analytics. And because they’re hiring people coming out of school. They know R already.”

Data scientists like Herron have commonly spent years in college, coding in R. “That’s a typical story that kind of led the company to be founded. We saw, way back in 2007, just how R had taken over academia. Everyone that was studying statistics, or machine learning, or what we, today, call data science was doing it in R,” Smith says.