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