Overview
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Learn how to combine the analytical strengths of R with the visualization power of Tableau to analyze and present data more effectively.
Syllabus
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Introduction
- Include R analyses in your Tableau visualizations
- What you should know
- Compare the strengths of Tableau and R
- See how R and Tableau can work together
- Install R on a computer
- Download and install CRAN packages in R
- Run Rserve and establish a connection to Tableau
- Import data into R
- Create calculations in R
- Import data into Tableau
- Create a visualization in Tableau
- Create a calculated field in Tableau
- Linear regression and multiple regression models
- Create a single- and multiple-variable linear regression model in R
- Analyze regression variables for significance in R
- Visualize data for linear regression in Tableau
- Add an R regression model to a Tableau viz
- Explore outliers and outlier detection
- Create an outlier detection model in R
- Visualize data for outlier detection in Tableau
- Add an R outlier detection model to a Tableau viz
- Explore clustering algorithms
- Create a centroid-based clustering model in R
- Visualize clustered data in Tableau
- Add an R clustering model to a Tableau viz
- Explore logistic regression algorithms
- Create a logistic regression model in R
- Visualize data for logistic regression in Tableau
- Add an R logistic regression model to a Tableau viz
- Explore support vector machine algorithms
- Create a support vector machine model in R
- Visualize support vector machine data in Tableau
- Add an R support vector machine model to a Tableau viz
- Explore random forest analysis
- Create a random forest analysis model in R
- Visualize data for random forest analysis in Tableau
- Add a random forest analysis model to a Tableau viz
- Next steps
