R is a free software environment for scientific and statistical computing and graphics that runs on all common computing platforms. An active and highly skilled developer community works on development and improvement. It has become an environment of choice for the implementation of new methodology. It is at the same time attracting wide attention from statistical application area specialists. The powerful and innovative graphics abilities available in R include the provision of well-designed publication-quality plots.

The first day of this course will focus on the R software environment, the remaining days of this workshop will focus on learning advanced statistical methods with R.

We will spent an almost equal amount of time in PowerPoint sessions and computer exercises. During the PowerPoint sessions the focus will be on the statistical methods with minimal discussion of computer software. During the computer exercise time you will be using R to apply the statistical methods taught in the lectures. At the start of each session of computer exercises Mark will perform the first exercise in each set on his own computer demonstrating to the class the use of the software and the statistical results obtained.

Day 1

The R software environment:

• what does the R window look like,

• help screens in R, data objects and data types in R,

• importing and exporting data from R,

• R packages,

• writing your own R scripts, data visualisation in R

• Introduction to linear regression

Day 2

• Regression diagnostics

• Logistic and Poisson regression - Including odds ratios, incidence rate ratios

Day 3

• Analysis of Variance

• Factor analysis – including factor rotations, uniqueness and commonality

Day 4

• Mixed effects models for longitudinal and clustered data.

• Clustering techniques – k means clustering, cluster linkage, and dendrograms

Day 5

• Missing data and multiple imputation

Approximately half of the time in this workshop will be spent in PowerPoint seminars, and the other half will be spent in computer demonstrations and self-paced computer exercises (using datasets publicly available within R).

This course is intended for

• Participants who have a basic knowledge and experience with statistical methods in R, and would like to further develop this statistical expertise.

• Participants who have some familiarity with these statistical methods in other software (eg SPSS, SAS or Stata), and who wish to learn how to use these methods in the R system (potentially as new R users).

A basic knowledge of statistics is assumed. No prior knowledge of the statistical methods taught in this course or any prior experience with R is assumed, though students with prior knowledge will be better suited to tackle the more advanced topics in this workshop.

Participants must be comfortable with typing commands at the command line.