Why use R when you have Tableau? Tableau vs. R?

I have seen many discussions around Tableau Vs. R. Here are my thoughts on this topic. Tableau is a fantastic tool for pattern discovery using data visualization. It is usually my tool of choice when I want throw some data and keep playing with the data to see whether any patterns emerge. Learning and using Tableau is a very low time consuming activity, but you could keep playing with the data and nothing might emerge.

Whereas, R has a very steep learning curve; any investment you make in R, however, will be returned with significant rewards. R is easily more than a programming language; it is almost a whole framework. You have access to countless libraries (from data manipulation to data visualization to machine learning to financial analysis to interactive Google charts to text mining to web apps).

Why learn R?

If very few people are using R around you, then it is more the reason to learn R, because soon you’ll be wowing them (and future employers) with your skills. Remember: if something is easy to do, everybody is doing that thing; you will become indistinct by becoming a commodity. Better, become indispensable.

As as example of R’s power, consider this report that I created in R. It is reporting an inclination score (generated using R), showing sparklines (again R), getting data from SQL Server data warehouse, and then repeating the analysis for every region and every capacity range. More than 100s of nice looking pages within seconds.

Auto Reports using R Auto Reports using R

Auto Reports using R Auto Reports using R

If there were only two reasons to use R, I would say these: reproducibility and repeatability. You can do everything in R in one script and then come back to it after a few years still able to track your steps down.

As I see it, it is really not Tableau vs. R issue. They can be used together. For all Tableau’s promises, I believe that only few people actually use it for exploration and it becomes another reporting a.k.a BI tool. I use Tableau for exploring, finding quick patterns and then coding that in R to reproduce the patterns for all various combinations. You can quickly build dashboards together without actually thinking about the process or about your objective of actionable insights. In addition, since it is easy to create charts, there is a real danger of quickly creating many useless charts.

Here’s another example of R’s magic: a convex hull to cluster customers (so getting data, clustering, convex hull and mapping in one single script) to find regions with opportunity.

Convex Hull and Clusters Using R

Convex Hull and Clusters Using R

RStudio provides a nice interface and makes R very easy to use. RStudio supports Markdown which produces nice looking documents or HTML pages with the benefit of retaining all your code to reproduce all your numbers and graphs with newer data. One more advantage of Markdown: Markdown is very easy to learn.

If you want to create more controlled, beautiful looking documents, however, you can take the complicated, frustrating, yet rewarding route, and rely on knitr, LaTex/Sweave and RStudio.

In sum, R provides you with everything: data extraction, manipulation, analysis, visualization, and reporting. Don’t let the learning curve scare you; you are missing out if you are not using it.

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About the Author

The author of Tableau Data Visualization Cookbook and an award winning keynote speaker, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life cycle (from recruitment to giving). He enjoys speaking about the power of data, as well as ranting about data professionals who chase after “interesting” things. He earned his PhD/MS from West Virginia University and his BEng from Nagpur University, all in industrial engineering. Currently, he is leading the data science, reporting, and prospect development efforts at the University of Southern California.

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