It’s the industry standard for statistics and data mining, but R can be less than user-friendly. Tableau makes it faster and easier to identify patterns and build practical models using R. Tableau helps you make sense of your data with the ability to slice, filter, and aggregate it with a few clicks – so you can optimise your models before writing a single line of code. Communicating your findings is easy: build an interactive dashboard with drop-downs, sliders and other visual cues in minutes. With Tableau, your audience can get the full value of your analysis without handholding, so you can focus on building more impactful models. Keep reading to see how Tableau changes R analytics.
R for statistical computing & analysis
A powerhouse for business intelligence and big data analytics
Few would dispute R’s ability to make easy work of big data queries. The R programming language is a key player in enterprises’ pursuits to leverage big data for business intelligence analysis. One challenge that arises in this type of deployment is that R is a tool which is intended to be used by trained personnel who are familiar with R or the Python programming language. Tableau reduces the need for an entire department comprised of these specialists by making the data which R leverages accessible without the user needing to be able to write code. It allows users to access and ask their own questions of the data, surfacing new data discoveries within the data uncovered by R.
Additionally, Tableau's visual analytics interface makes analysis simpler and communication of findings virtually effortless. Ad-hoc analysis and per-user contexts are suddenly available when R is no longer limited by the users' knowledge or lack thereof. All you need is a question to ask the data. After that, exploration through complex data becomes virtually effortless.