If you’re in the Big Data world, you know that the R statistics language is totally rad! It has more than 6,100 add-on packages, 41,000+ members in LinkedIn’s R group, and 170+ R Meetup groups currently in existence. The bonus? It’s free, open source, powerful and highly extensible. To say it is popular is an understatement.
R has become the single most important tool for computational statistics, visualization and data science. Millions of statisticians and data scientists worldwide use R to solve complex problems in areas ranging from computational biology to quantitative marketing. R is an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn.
Applications of R, according to Revolution Analytics (recently acquired by Microsoft) include:
- Facebook: Used by some within the company for tasks such as analyzing user behavior.
- Google: There are more than 500 R users at Google, according to David Smith at Revolution Analytics, doing tasks such as making online advertising more effective.
- National Weather Service: Flood forecasts.
- Orbitz: Statistical analysis to suggest best hotels to promote to its users.
- Trulia: Statistical modeling.
While the R language is relatively simple, it’s syntax is different from many other languages and is unconventional. This can make it appear difficult to learn. But guides such as the one from Sharon Machlis (published in Computerworld) can help you harness its power to your advantage. R includes just about every data manipulation, statistical model, and chart that the modern data scientist could ever need. It goes beyond the ancient bar chart and line plot by including multi-panel charts, 3D surfaces, and much more. The custom charting capabilities of R language are on full display via infographics in major publications such as the New York Times and The Economist.
If you’re a data scientist, then R is for you. With support by a community of more than 2 million users and thousands of developers worldwide, you can use R to optimize portfolios, analyze genomic sequences, predict component failure times, and more. Or, you can tap Chateaux Principal Hugo Toledo to use R and create analytics for you as part of our Data Science Service!
