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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

Packages

In R, there are several packages that provide the correlation functionality to the programmer. We will be using the following packages in this chapter:

  • corrgram: This is the tool to graphically display correlations

  • Hmisc: This contains a variety of miscellaneous R functions

  • polycor: This contains functions to compute polychoric correlations

  • ggm: This contains functions for analyzing and fitting graphical Markov models

Correlation

Basic correlation is performed in R using the cor function. The cor function is defined as follows:

cor(x, y = NULL, 
  use = "everything", 
  method = c("pearson", "kendall", "spearman"))

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

Parameter

Description

x

This is the dataset.

y

This is the dataset that is compatible with x.

use

This is the optional method for computing the covariance of missing values assigned. The choices are:

  • everything

  • all.obs

  • complete.obs

  • na.or.complete

  • pairwise.complete.obs

method...

CONTINUE READING
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Tech Concepts
36
Programming languages
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R for Data Science
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