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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Merging the aggregate data back into the original data


Often, you will want to augment your original data with some of the calculated data as derived previously. In these cases, you can merge the data back into the original data using a common key. Again, we will use the dplyr package to take the results just obtained (by.cat) and join them back to the original data (x), using the common key cat.

We will be using a left_join just for an example; however, we could have used a right join to obtain the same results, since by.cat was completely derived from x. After joining the two dataframes, we will end up with a new dataframe named x2:

 # Merge the summary measures back into the original data. Merge by cat.


 x2 <- by.cat %>% left_join(x, by = "cat")
 head(x2) 
 > Source: local data frame [6 x 9]
 > 
 >        cat Avg.Total.Insured Avg.People      Year Year.1 Total.People
 >     (fctr)             (dbl)      (dbl)    (fctr)  (int)        (dbl)
 > 1 ALL AGES           ...
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