In all these tutorials, I will be packing R code within code blocks. For the most part, I will not be printing out the results of the R code, and expect you to follow along with me by copying and pasting the code into R, observing the results, and reading my commentary.

Programming in R

• R Environment – Explanation of the strengths of R, Download Links, and Suggested complementary programs
• Variables in R – Variable types, how to declare and manipulate them
• Loops in R – Loops, how to define them, how to create boundaries and use iterators
• Functions in R – Functions, how to declare them, pass data to them and get data out

Data Preparation in R

• Adult Dataset – Example dataset for classification. Using demographic variables, attempt to predict if income is greater than 50k

Linear Regression Methods in R

• Multiple Linear Regression: Cirrhosis – Attempt to predict cirrhosis prevalence in populations based on liquor consumption, population, and birth rates
• Simple Linear Regression: Brain Weight – Attempt to predict the brain’s weight based on the body’s weight. Uses log-transformation of variables

Data Mining and Machine Learning Methods in R

• Neural Networks in R: Adult – Using an artificial Neural Network, attempt the classification problem on the Adult dataset
• Classification Trees in R: Adult – Using Classification Trees (rpart package), attempt the classification problem on the Adult dataset
• Random Forests in R: Adult – Using a Random Forest (randomForest package), attempt the classification problem on the Adult dataset
• Logistic Regression in R: Adult – Using Logistic Regression, attempt the classification problem on the Adult dataset. Perform diagnostics on resulting model

Publishing in R

• Rstudio, Sweave and LaTeX – Demonstrating the power of the publishing tools available within Rstudio, automatically placing output from R environments into LaTeX documents
• Making Pretty Graphics with ggplot2 – Demonstrating and extending the power of the graphing capabilities available in R