https://github.com/sol-eng/tableau-examples

Example content for using Tableau with RStudio Connect
https://github.com/sol-eng/tableau-examples

Last synced: 5 months ago
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Example content for using Tableau with RStudio Connect

• Host: GitHub
• URL: https://github.com/sol-eng/tableau-examples
• Owner: sol-eng
• Created: 2021-09-24T04:44:06.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2026-01-14T04:02:30.000Z (6 months ago)
• Last Synced: 2026-01-14T07:32:09.757Z (6 months ago)
• Language: Jupyter Notebook
• Size: 15.3 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 46
• Metadata Files:
  • Readme: README.md

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README

          
# Tableau Examples

> A collection of examples highlighting how Tableau and RStudio Connect can be used together.

## Setup

In order to fully take advantage of these examples, you'll need access to a

Tableau environment. One option is to register for the [Tableau Developer

Program](https://www.tableau.com/developer), which will give you access to your

own Tableau Online sandbox.

Once you have access to Tableau, you can follow the instrucitons to [configure

Tableau with RStudio

Connect](https://docs.rstudio.com/rsc/integration/tableau/). Each example in

this repository has a Tableau workbook in the `Tableau/` that can used to

showcase the extensions from the example.

### [Capitalize](capitalize/)

![Capitalize screenshot](img/capitalize-workbook.png)

A very basic example showing how both Python and R extensions can be used to

capitalize text passed from Tableau.

---

### [Superstore](superstore/)

![Superstore screenshot](img/superstore-workbook.png)

An example built on top of the Tableau Superstore data showing how R and Python

extensions can be used side by side in the same workbook and even in the same

visualization.

---

### [Music Explorer](music/)

![Music Explorer screenshot](img/music-dashboard.png)

An example using [music genre data from

Kaggle](https://www.kaggle.com/datasets/vicsuperman/prediction-of-music-genre)

that enables users to interactively explore the data and evaluate the

performance of separate Random Forest models trained in both R and Python.