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Advanced Analytics with R and Tableau

You're reading from   Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Length 178 pages
Edition 1st Edition
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Authors (3):
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Roberto Rösler Roberto Rösler
Author Profile Icon Roberto Rösler
Roberto Rösler
Ruben Oliva Ramos Ruben Oliva Ramos
Author Profile Icon Ruben Oliva Ramos
Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
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Toc

Table of Contents (10) Chapters Close

Preface 1. Advanced Analytics with R and Tableau FREE CHAPTER 2. The Power of R 3. A Methodology for Advanced Analytics Using Tableau and R 4. Prediction with R and Tableau Using Regression 5. Classifying Data with Tableau 6. Advanced Analytics Using Clustering 7. Advanced Analytics with Unsupervised Learning 8. Interpreting Your Results for Your Audience Index

Comparing actual values with predicted results


Now, we will look at real values of weight of 15 women first and then will look at predicted values. Actual values of weight of 15 women are as follows, using the following command:

women$weight

When we execute the women$weight command, this is the result that we obtain:

When we look at the predicted values, these are also read out in R:

How can we put these pieces of data together?

women$pred <- linearregressionmodel$fitted.values

This is a very simple merge. When we look inside the women variable again, this is the result:

Investigating relationships in the data

We can see the column names in the model by using the names command. In our example, it will appear as follows:

names(linearregressionmodel)

When we use this command, we get the following columns:

[1] "coefficients"  "residuals"     "effects"      
[4] "rank"          "fitted.values" "assign"       
[7] "qr"            "df.residual"   "xlevels"      
[10] "call"          "terms"   ...
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