R for Data Science -- Table of Contents -- R for Data Science -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why subscribe? -- Free access for Packt account holders -- 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.