R for Data Science by Toomey, Dan -- Read -- Imperial Library of Trantor

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R for Data Science

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

1. Data Mining Patterns

Cluster analysis

K-means clustering

Usage Example

K-medoids clustering

Usage Example

Hierarchical clustering

Usage Example

Expectation-maximization

Usage List of model names Example

Density estimation

Usage Example

Anomaly detection

Show outliers

Example Example Another anomaly detection example

Calculating anomalies

Usage Example 1 Example 2

Association rules

Mine for associations

Usage Example

Questions Summary

2. Data Mining Sequences

Patterns

Eclat

Usage Using eclat to find similarities in adult behavior Finding frequent items in a dataset An example focusing on highest frequency

arulesNBMiner

Usage Mining the Agrawal data for frequent sets

Apriori

Usage Evaluating associations in a shopping basket

Determining sequences using TraMineR

Usage Determining sequences in training and careers

Similarities in the sequence

Sequence metrics Usage Example

Questions Summary

3. Text Mining

Packages

Text processing

Example Creating a corpus

Converting text to lowercase Removing punctuation Removing numbers Removing words Removing whitespaces Word stems Document term matrix Using VectorSource

Text clusters

Word graphics Analyzing the XML text

Questions Summary

4. Data Analysis – Regression Analysis

Packages

Simple regression Multiple regression Multivariate regression analysis Robust regression

Questions Summary

5. Data Analysis – Correlation

Packages

Correlation

Example

Visualizing correlations Covariance Pearson correlation Polychoric correlation Tetrachoric correlation A heterogeneous correlation matrix Partial correlation

Questions Summary

6. Data Analysis – Clustering

Packages K-means clustering

Example

Optimal number of clusters

Medoids clusters The cascadeKM function Selecting clusters based on Bayesian information Affinity propagation clustering Gap statistic to estimate the number of clusters Hierarchical clustering

Questions Summary

7. Data Visualization – R Graphics

Packages

Interactive graphics The latticist package

Bivariate binning display Mapping Plotting points on a map Plotting points on a world map Google Maps

The ggplot2 package

Questions Summary

8. Data Visualization – Plotting

Packages Scatter plots

Regression line A lowess line scatterplot Scatterplot matrices

splom – display matrix data cpairs – plot matrix data

Density scatter plots

Bar charts and plots

Bar plot

Usage

Bar chart ggplot2 Word cloud

Questions Summary

9. Data Visualization – 3D

Packages Generating 3D graphics

Lattice Cloud – 3D scatterplot scatterplot3d scatter3d cloud3d RgoogleMaps vrmlgenbar3D Big Data

pbdR

Common global values Distribute data across nodes Distribute a matrix across nodes

bigmemory

pdbMPI snow More Big Data

Research areas

Rcpp parallel microbenchmark pqR SAP integration roxygen2 bioconductor swirl pipes

Questions Summary

10. Machine Learning in Action

Packages Dataset

Data partitioning Model

Linear model Prediction Logistic regression Residuals Least squares regression Relative importance Stepwise regression The k-nearest neighbor classification Naïve Bayes

The train Method

predict Support vector machines K-means clustering Decision trees AdaBoost Neural network Random forests

Questions Summary

11. Predicting Events with Machine Learning

Automatic forecasting packages

Time series The SMA function The decompose function Exponential smoothing Forecast

Correlogram Box test

Holt exponential smoothing

Automated forecasting ARIMA Automated ARIMA forecasting

Questions Summary

12. Supervised and Unsupervised Learning

Packages

Supervised learning

Decision tree Regression Neural network Instance-based learning Ensemble learning Support vector machines Bayesian learning Random forests

Unsupervised learning

Cluster analysis Density estimation Expectation-maximization Hidden Markov models Blind signal separation

Questions Summary

Index

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