These presentations introduce and discuss how to use R for basic statistical analysis and modeling of epidemiological data. The slides can be found here.

**Statistical Functions in R**

An overview of a few of the many statistical functions that come with base R, including summary(), barplot(), t.test() and wilcox.test(), and how to extract information from the results of statistical function.

**Linear Regression in R**

A linear regression example using data from John Fox's "car" package and the lm() function including simple residual analysis and how to update a model.

**Logistic Regression in R**

A brief review of odds, log odds and the logistic model followed by a logistic regression example using the glm() function.

**Poisson Regression in R Part 1**

A review of the Poisson model, offset variables and the predictive interpretation of Poisson regression coefficients.

**Poisson Regression in R Part 2**

A Poisson regression example using traffic fatality data from Achim Zeileis's "AER" package.

**Overdispersion and the Negative Binomial Model in R**

Approaches to assessing and adjusting for overdispersion in Poisson models.

**About R packages**

An overview of R packages including how to find, install find help and update packages from the command line.

**Categorical Data Analysis in R Part 1**

An introduction to 2x2 table analysis in R using epitab() from Tomas Aragon's "epitools" package.

**Categorical Data Analysis in R Part 2**

The Mantel-Haenszel adjusted odds ratio using the cc() and mhor() from Virasakdi Chongsuvivatwong's "epicalc" package.

**Introduction to Survival Analysis in R: 1. Risks vs. Rates**

An overview of the fundamental difference between disease risk and disease rates, and the implication of this difference for modeling time to event data.

**Introduction to Survival Analysis in R: 2. Binomal Model vs. Exponential Model of Disease**

A description of the assumptions underlying binomial models and exponential models of disease risk, and the role of Kaplan-Meier and the product limit estimator when those assumptions can not be reasonably met.

**Introduction to Survival Analysis in R: 3. The Kaplan-Meier Method**

A description of the Kaplan-Meier method and illustration of how it can be implemented with basic R code.

**Introduction to Survival Analysis in R: 4. The survival package**

An overview of how survival methods can be implemented in R using functions in the "survival" package.