R-Shiny vs Tableau – Why To Use Either Package?

Are you having trouble deciding on using R-Shiny vs Tableau for your data science or analytics project? Both of these packages have their benefits and limitations. However, you are able to combine the two of them together so they can be used in conjunction with one another. Here is a quick overview of the benefits and drawbacks of using R-Shiny vs Tableau as your analytics software of choice.

R Shiny Overview

R is a open-source statistical computing package. R is a statistical programming language, and Shiny is the server package that allows it to be used to create web applications. Shiny is used for visualizations and data analysis. As R-Shiny is a powerful tool, it allows you to create apps with a frontend and backend. R-Shiny is often used as a backend, and users typically combine that with HTML and Javascript libraries on the frontend for additional functionality.

These can be full-fledged database powered web applications, only they would also include the statistical analysis and visualization capabilities of R. R Shiny is different from Tableau because much of its focus is for scientific and technical users. R Shiny is known for offering many robust packages for statistical analysis or loading data in from various sources.

Tableau Overview

Tableau, like R is open source. However, Tableau is a product which is owned by Salesforce. Like R, Tableau is capable of creating very impressive visualizations. Tableau’s primary users are people looking to create Business Intelligence dashboards.
Tableau is also capable of integrating with other software packages that are more business-focused and aimed at a wide range of industries. While R Shiny has plenty of impressive visualization capabilities, Tableau is capable of building the more beautiful and interactive interfaces.

R Packages

R-Shiny is known for it’s packages, while Tableau is known for its dashboards. Currently, there are over 10,000 R packages available. These packages are able to be used across a wide range of applications. Some R packages are used for working with biological data while there are plenty of other examples of R packages that are used to work with financial data.

Tableau Dashboards

Tableau (on the other hand) is aimed to be more user-accessible. It offers a lot of customize-able dashboards, but no additional functionality that R-Shiny offers with its packages. Tableau users are typically using the software to create an analytics dashboard from internal databases. Users are typically looking to gather business intelligence from multiple sources and present the data in a meaningful way.

Tableau and R can be combined together if you would like to take advantage of the benefits that both combined have to offer. If there is an R package that you like that Tableau does not have capabilities for, simply combine the two together. You’ll have the best of both worlds – the beautiful visualizations and the powerful R packages.

Examples of R Functionality

R’s strength is in being able to do statistical analysis. For quantitative analysis for finance, R gives you several different options. QuantLib is able to provide a framework for modeling using a very practical implementation of a modeling tool that is free to use in R. R is also a powerful tool for machine learning applications. R has clustering capabilities as well as several other machine learning tools for those looking to understand large data sets. Some people even create web scrapers using the R-Shiny toolset.

Tableau’s Functionality

Tableau is great for being able to combine data from multiple sources in order to create some very beautiful charts. Unlike R, a lot of the tasks you’ll do in this platform can be done without code. It allows you to combine data in real time in order to analyze trends and patterns in data sets. Tableau can also sit on top of nearly any type of database from an Excel sheet to a regular cloud-based SQL database. With Tableau, you’re able to create a wide range of data sets using spreadsheet applications. Tableau is great for business users, although for greater customization ability R is the preferred application.

Integrating R with Tableau

R is a powerful tool for working with data with it’s many packages. R and Tableau are both used by data scientists. Using this tool, the integration of these powerful computing packages is possible. In case you would like to integrate the two, you can use the Rserve package. Once you’ve added the Rserve package, you’re able to connect the two with an Analytics Extension Connection. It’s possible to have the powerful computational tools of R-Shiny in combination with the beautiful analytics dashboards of Tableau.


Shiny and Tableau are both powerful tools. While both have very high-quality data visualization tools, both are aimed at different users. Tableau has a lower learning curve, while R Shiny is for a person with more technical skills. However, that doesn’t mean that either can be powerful for creating visualizations from data.

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