Chapter 16 linear regression on father-son data and tips data.  Used ggplot for creating some nice graphs
Linear regression of NY housing data.  Created several models and made some interesting plots, including plots of coefficients
Chapter 17 using logistic regression with glm, using NY housing data.  Distribution chosen was Poisson in determing the number of children based on predictor variables.  Sever plots of interest
Chapter 17 section on survival analysis using bladder data.  Used functions surv, coxph and plot function survfit.
Chapter 18 on residuals.  Used housing data to prepare a linear model and plotted the coefficients.  Also used fortify in ggplot to plot the predicted linear responses.  The plot of the residuals showed clearly the effect of the boro.  The histogram of the residuals is not normally distributed which means the model need s some wrk.
Lander cross validtion in chapter 18.3.  Created five linear and five logistic models and compared the fit of all five using Anova, AIC and BIC.  Cross validation was performed using cv.glm function.  The cv stands for cross validation and the glm is the acronym for generalized linear models.  These models include logistic. poisson (count), gamma, inverse gaussian, quaisibinomial.
Lander Chapter 19 using elastic net to create predictors, determining which alpha and which lambda fit best.  Extensive use of plots, glmnet, cv.glmnet for cross validation.  Some really heavy code involved.