**Tableau and R for Analytics Projects**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 27m | 281 MB

eLearning | Skill level: Intermediate

On its own, Tableau is a powerful tool that helps professionals analyze, display, and generally make sense of the data at their fingertips. With the addition of R—a free, open-source language for data science—you can glean even more insights from your data. In this course, learn how to combine the analytical strengths of R with the visualization power of Tableau to analyze and present data more effectively. Instructor Curt Frye demonstrates how to install R and RServe; create a connection between Tableau and R; perform several types of analyses in R, from linear regression to cluster identification; and incorporate those analyses into Tableau visualizations.

Topics include:

- Importing data
- Creating calculations in R
- Creating and visualizing linear regression models
- Detecting and visualizing outliers
- Defining and visualizing clustering models
- Creating a logistic regression model in R
- Creating a support vector machine model
- Visualizing random forest analysis data in Tableau

**+ Table of Contents**

**Introduction**

1 Include R analyses in your Tableau visualizations

2 What you should know

**Introducing Tableau and R**

3 Compare the strengths of Tableau and R

4 See how R and Tableau can work together

5 Install R on a computer

6 Download and install CRAN packages in R

7 Run Rserve and establish a connection to Tableau

**Prepare for Analysis with Tableau and R**

8 Import data into R

9 Create calculations in R

10 Import data into Tableau

11 Create a visualization in Tableau

12 Create a calculated field in Tableau

**Create and Visualize Linear Regression Models**

13 Linear regression and multiple regression models

14 Create a single- and multiple-variable linear regression model in R

15 Analyze regression variables for significance in R

16 Visualize data for linear regression in Tableau

17 Add an R regression model to a Tableau viz

**Detect and Visualize Outliers**

18 Explore outliers and outlier detection

19 Create an outlier detection model in R

20 Visualize data for outlier detection in Tableau

21 Add an R outlier detection model to a Tableau viz

**Define and Visualize Clustering Models**

22 Explore clustering algorithms

23 Create a centroid-based clustering model in R

24 Visualize clustered data in Tableau

25 Add an R clustering model to a Tableau viz

**Classify Data Using Logistic Regression**

26 Explore logistic regression algorithms

27 Create a logistic regression model in R

28 Visualize data for logistic regression in Tableau

29 Add an R logistic regression model to a Tableau viz

**Classify Data Using Support Vector Machines**

30 Explore support vector machine algorithms

31 Create a support vector machine model in R

32 Visualize support vector machine data in Tableau

33 Add an R support vector machine model to a Tableau viz

**Visualize Random Forest Analysis Data in Tableau**

34 Explore random forest analysis

35 Create a random forest analysis model in R

36 Visualize data for random forest analysis in Tableau

37 Add a random forest analysis model to a Tableau viz

**Conclusion**

38 Next steps

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