Beginning Data Science with R

4.4 Interactive Visualizations using Shiny 4.5 Chapter Summary & Further Reading References 5 Exploratory Data Analysis 5.1 Summary Statistics 5.1.1 Dataset Size 5.1.2 Summarizing the Data 5.1.3 Ordering Data by a Variable 5.1.4 Group and Split Data by a Variable 5.1.5 Variable Correlation 5.2 Getting a sense of data distribution 5.2.1 Box plots 5.2.2 Histograms 5.2.3 Measuring Data Symmetry using Skewness and Kurtosis 5.3 Putting it all together: Outlier Detection 5.4 Chapter Summary References 6 Regression 6.1 Introduction 6.1.1 Regression Models 6.2 Parametric Regression Models 6.2.1 Simple Linear Regression 6.2.2 Multivariate Linear Regression 6.2.3 Log-Linear Regression Models 6.3 Non-Parametric Regression Models 6.3.1 Locally Weighted Regression 6.3.2 Kernel Regression 6.3.3 Regression Trees 6.4 Chapter Summary References 7 Classification 7.1 Introduction 7.1.1 Training and Test Datasets 7.2 Parametric Classification Models 7.2.1 Naive Bayes 7.2.2 Logistic Regression 7.2.3 Support Vector Machines 7.3 Non-Parametric Classification Models 7.3.1 Nearest Neighbors 7.3.2 Decision Trees 7.4 Chapter Summary References 8 Text Mining 8.1 Introduction 8.2 Reading Text Input Data 8.3 Common Text Preprocessing Tasks 8.3.1 Stop Word Removal 8.3.2 Stemming 8.4 Term Document Matrix 8.4.1 TF-IDF Weighting Function 8.5 Text Mining Applications 8.5.1 Frequency Analysis 8.5.2 Text Classification 8.6 Chapter Summary