Tableau and R for Analytics Projects

Introduction

• Include R analyses in your Tableau visualizations
• What you should know

1. Introducing Tableau and R

• 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

2. Prepare for Analysis with Tableau and R

• Import data into R
• Create calculations in R
• Import data into Tableau
• Create a visualization in Tableau
• Create a calculated field in Tableau

3. Create and Visualize Linear Regression Models

• 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

4. Detect and Visualize Outliers

• 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

5. Define and Visualize Clustering Models

• 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

6. Classify Data Using Logistic Regression

• 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

7. Classify Data Using Support Vector Machines

• 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

8. Visualize Random Forest Analysis Data in Tableau

• 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

Conclusion

• Next steps