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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Toc

Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining FREE CHAPTER 2. Fuzzy Logic Induced Content-Based Recommendation 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Demonstrating the use of RecordLinkage package


We will leverage the RecordLinkage package in R. The data shown in the previous section is available with the package:

Note

RecordLinkage: Record linkage in R provides functions to link and deduplicate datasets. Methods based on a stochastic approach are implemented, as well as classification algorithms from the machine learning domain. Authors: Andreas Borg and Murat Sariyar.

> library(RecordLinkage, quietly = TRUE)
> data(RLdata500)
> str(RLdata500)
'data.frame':    500 obs. of  7 variables:
 $ fname_c1: Factor w/ 146 levels "ALEXANDER","ANDRE",..: 19 42 114 128 112 77 42 139 26 99 ...
 $ fname_c2: Factor w/ 23 levels "ALEXANDER","ANDREAS",..: NA NA NA NA NA NA NA NA NA NA ...
 $ lname_c1: Factor w/ 108 levels "ALBRECHT","BAUER",..: 61 2 31 106 50 23 76 61 77 30 ...
 $ lname_c2: Factor w/ 8 levels "ENGEL","FISCHER",..: NA NA NA NA NA NA NA NA NA NA ...
 $ by      : int  1949 1968 1930 1957 1966 1929 1967 1942 1978 1971 ...
 $ bm    ...
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