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R for Data Science

By : Dan Toomey
Book Image

R for Data Science

By: Dan Toomey

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R for Data Science
Dan Toomey
Dec, 2014 | 364 pages
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Table of Contents (19 chapters)
R for Data Science
R for Data Science
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Data Mining Patterns
Data Mining Patterns
Cluster analysis
Anomaly detection
Association rules
Questions
Summary
2
Data Mining Sequences
Data Mining Sequences
Patterns
Questions
Summary
3
Text Mining
Text Mining
Packages
Questions
Summary
4
Data Analysis – Regression Analysis
Data Analysis – Regression Analysis
Packages
Questions
Summary
5
Data Analysis – Correlation
Data Analysis – Correlation
Packages
Questions
Summary
6
Data Analysis – Clustering
Data Analysis – Clustering
Packages
K-means clustering
Questions
Summary
7
Data Visualization – R Graphics
Data Visualization – R Graphics
Packages
Questions
Summary
8
Data Visualization – Plotting
Data Visualization – Plotting
Packages
Scatter plots
Bar charts and plots
Questions
Summary
9
Data Visualization – 3D
Data Visualization – 3D
Packages
Generating 3D graphics
Questions
Summary
10
Machine Learning in Action
Machine Learning in Action
Packages
Dataset
Questions
Summary
11
Predicting Events with Machine Learning
Predicting Events with Machine Learning
Automatic forecasting packages
Questions
Summary
12
Supervised and Unsupervised Learning
Supervised and Unsupervised Learning
Packages
Questions
Summary
Index
Index

Packages

We will use the following packages available in R for supervised and unsupervised learning:

  • rattle: This is a data mining GUI in R

  • rpart.plot: This is used to plot the r.part models

  • caret: This is used for classification and regression training

  • kknn: These are the weighted k nearest neighbors

  • kernlab: This is used for kernel-based machine learning

  • e1071: This package contains miscellaneous functions of the Department of Statistics

  • MCMCpack: This package contains the functions for Markov chain Monte Carlo algorithm

  • randomForest: This is used for classification and regression based on a forest of trees using random inputs

  • FactoMineR: This is used for multivariate exploratory data analysis and data mining with R

Supervised learning

In supervised learning we have a target variable and a number of possible predictor variables. The objective is to associate the predictor variables in such a way so as to accurately predict the target variable. We are using some portion of observed data to...

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