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  • Book Overview & Buying R for Data Science
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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
Buy this Book
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey
Buy this Book

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Mining Patterns
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1. Data Mining Patterns
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Cluster analysis
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Anomaly detection
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Association rules
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Questions
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Summary
2
2. Data Mining Sequences
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2. Data Mining Sequences
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Patterns
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Questions
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Summary
3
3. Text Mining
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3. Text Mining
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Packages
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Questions
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Summary
4
4. Data Analysis – Regression Analysis
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4. Data Analysis – Regression Analysis
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Packages
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Questions
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Summary
5
5. Data Analysis – Correlation
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5. Data Analysis – Correlation
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Packages
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Questions
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Summary
6
6. Data Analysis – Clustering
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6. Data Analysis – Clustering
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Packages
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K-means clustering
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Questions
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Summary
7
7. Data Visualization – R Graphics
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7. Data Visualization – R Graphics
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Packages
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Questions
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Summary
8
8. Data Visualization – Plotting
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8. Data Visualization – Plotting
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Packages
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Scatter plots
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Bar charts and plots
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Questions
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Summary
9
9. Data Visualization – 3D
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9. Data Visualization – 3D
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Packages
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Generating 3D graphics
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Questions
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Summary
10
10. Machine Learning in Action
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10. Machine Learning in Action
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Packages
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Dataset
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Questions
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Summary
11
11. Predicting Events with Machine Learning
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11. Predicting Events with Machine Learning
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Automatic forecasting packages
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Questions
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Summary
12
12. Supervised and Unsupervised Learning
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12. Supervised and Unsupervised Learning
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Packages
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Questions
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Summary
13
Index
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Index

K-means clustering

K-means is the process of assigning objects to groups so that the sum of the squares of the groups is minimized. R has the kmeans function available for cluster analysis. K-means is a method of determining clusters based on partitioning the data and assigning items in the dataset to the nearest cluster.

K-means clustering is done in R using the kmeans function. The kmeans function is defined as follows:

kmeans(x, centers, iter.max = 10, nstart = 1,
   algorithm = c("Hartigan-Wong", "Lloyd", "Forgy","MacQueen"), trace=FALSE)

The various parameters of this function are described in the following table:

Parameter

Description

x

This is the dataset.

centers

This contains the number of centers/clusters to find.

iter.max

This stores the maximum number of iterations allowed.

nstart

This contains the number of random clusters to find.

algorithm

This contains the algorithm to be used to determine clusters. Hartigan-Wong is the default. Lloyd and Forgy are the same...

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Tech Concepts
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Programming languages
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R for Data Science
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