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R Programming Table of Contents


Table of Content
 


1. Pre-Model Building Steps

  • Performing Univariate Analysis
  • Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
  • Detecting and Treating Outlier
  • Treating Missing Values with `mice`

2. Regression Modelling-In Depth

  • Interpreting Regression Results and Interactions Terms
  • Performing Residual Analysis and Extracting Extreme Observations With Cook’s Distance
  • Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
  • Validating Model Performance on New Data with k-Fold Cross Validation
  • Building Non-Linear Regressors with Splines and GAMs

3. Classification Models and caret Package-In Depth

  • Understanding the Concept and Building Naive Bayes Classifier
  • Building k-Nearest Neighbors Classifier
  • Building Tree Based Models Using RPart, cTree, and C5.0
  • Building Predictive Models with the caret Package
  • Selecting Important Features with RFE, varImp, and Boruta

4. Core Machine Learning-In Depth

  • Understanding Bagging and Building Random Forest Classifier
  • Implementing Stochastic Gradient Boosting with GBM
  • Regularization with Ridge, Lasso, and Elasticnet
  • Building Classifiers and Regressors with XGBoost

5. Unsupervised Learning

  • Clustering with k-means and Principal Components
  • Determining Optimum Number of Clusters
  • Understanding and Implementing Hierarchical Clustering
  • Clustering with Affinity Propagation
  • Building Recommendation Engines

6. Time Series Analysis and Forecasting

  • Stationarity, De-Trend, and De-Seasonalize
  • Understanding the Significance of Lags, ACF, PACF, and CCF
  • Forecasting with Moving Average and Exponential Smoothing
  • Forecasting with Double Exponential and Holt Winters
  • Forecast with ARIMA Modelling

7. Text Analytics-In Depth

  • Corpus, TDM, TF-IDF, and Word Cloud
  • Cosine Similarity and Latent Semantic Analysis
  • Extracting topics with Latent Dirichlet Allocation
  • Sentiment Scoring with tidytext and Syuzhet
  • Classifying Texts with RTextTools

8. ggplot2

  • Manipulating Legend, AddingText, and Annotation
  • Drawing Multiple Plots with Faceting and Changing Layouts
  • Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
  • ggplot2 Extensions and ggplotly

9. Speeding Up R Code

  • Implement Parallel Computing with doParallel and foreach
  • Write Readable and Fast R Code with Pipes and DPlyR
  • Write Super Fast R Code with Minimal Keystrokes Using Data.Table
  • Interface C++ in R with RCpp

10. Build Packages and Submit to CRAN

  • Build, Document, and Host an R Package on GitHub
  • Performing Important Checks before Submitting to CRAN
  • Submitting an R Package to CRAN


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