jmurray1022/learnR

R walkthroughs

R Walkthroughs

Introduction to R

• Installing R: installing R and RStudio.
• Heights of students versus their parents: a "hello world"-style introduction to the R environment.
• Installing a library: installing the mosaic library from within RStudio.

Exploratory data analysis

• Survival on the Titanic: basics of contingency tables.
• Temperatures in San Diego and Rapid City: measuring and visualizing dispersion; changing default plots in R.
• SAT scores and GPA at UT-Austin: boxplots, between-group and within-group variation, sample correlation, scatter plots, pairs plots, and lattice plots.

Fitting equations to data

• Asking prices of pickup trucks on Craigslist: simple linear regression via ordinary least squares; residual summaries
• Utility bills versus temperature: adding polynomial terms to fit nonlinear curves
• Infant mortality and GDP: using log transformations to fit power laws via linear least squares

Predictable and unpredictable variation

• Kidney function and aging: naive prediction intervals; R^2 and the decomposition of variance

Grouping variables in regression

• Reaction time in video games: modeling numerical outcomes with more than one categorical predictor; dummy variables and interaction terms; analysis of variance.
• House prices: regression with one numerical and multiple categorical predictors; dummy variables and interactions in simple regression models.

Quantifying uncertainty via the bootstrap

• Gone fishing: using the Monte Carlo method to simulate the sampling distributions of the sample mean and of the least-squares estimator
• Kidney function and aging, revisited: bootstrapping the sample mean and the OLS estimator; computing confidence intervals from bootstrapped samples.
• Newspapers: using the normal linear regression model to quantify uncertainty about parameters and predictions.

Multiple regression: basics

• The wage gap: an introduction to multiple regression
• Current population survey: the affect of collinearity on the estimated coefficients and ANOVA table in a multiple regression model.

Hypothesis testing

• The Patriots and the coin toss: testing a simple hypothesis by Monte Carlo simulation.
• Titanic, revisited: relative risk, odds ratios, and permutation tests in 2x2 contingency tables.

Building a predictive model

• Google flu trends: Building and checking a predictive model using stepwise selection

Generalized linear models

• Basketball point spreads: Logistic regression.

Time series

• Atmospheric CO2: Trends and seasonal variation in time-series models.

Monte Carlo simulation

• Introduction to Monte Carlo in the context of a simplified investment problem.

Miscellaneous

• Optimization: defining and optimizing your own functions in R.