## Statistics with R

A 2-Day Seminar

Taught by Richard Gonzalez, Ph.D.

This two-day seminar is designed to introduce the R package to those who already have some experience doing statistical analysis with another software package. At the conclusion of the seminar, you should be well equipped to do almost any standard analysis using R.

R has rapidly become a major tool for statistical analysis in both academia and industry. Journal articles on new statistical procedures often include R code so that readers can use the new technique immediately rather than wait years for it to be incorporated into traditional statistics packages. R produces amazing publication-quality graphics and plots, which are now being used by major news outlets such as the New York Times.

R can be easily modified so that you can organize output the way you like it. Repetitive tasks can be delegated to a function that can be stored and used in the future. It is even possible to write the results section of an article within RStudio, where the commands to insert all the statistics, tables and figures are in the document. RStudio will then generate a MS Word file ready to include in your manuscript. No more cutting and pasting figures or retyping tables to conform to journal guidelines. Of course, standard tools such as correlations, ANOVAs and regressions can be easily computed in R as well.

In this seminar, we assume that participants already know basic statistics through the general linear model (ANOVA and linear regression) and are familiar with at least one other statistics package such as SPSS, SAS or Stata.

The seminar will introduce the basics of how to work with R using examples and hands-on exercises. Many different types of statistical analyses will be illustrated and explained. Participants will also learn basic programming so they can harness the power of R to solve their own analytic problems.

Participants will learn how to use over a dozen different packages in R that provide advanced functionality and advanced statistical techniques.

### Who should attend?

This seminar will be useful for researchers from any field who want to learn how to use R in data analysis and also learn about the many unique features R offers. You should be familiar with basic statistical methods including ANOVA and multiple regression. While more advanced statistical methods will be illustrated, such as structural equation modeling, advanced statistical knowledge is not assumed nor required.

The seminar is designed for participants who want to learn how to use R in a hands-on setting to work through a set of examples and leave with a set of templates that can be used in subsequent applications.

### COMPUTING

This is a hands-on seminar with lots of exercises. Participants should bring their own laptops with the freeware packages, R and RStudio, already installed. Click here for further instructions. Power outlets will be available at each seat. WiFi will also be provided.

### Schedule and materials

The class will meet from 9 to 5 each day with a 1-hour lunch break.

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.

### Registration and Lodging

The fee of $895 includes all course materials.

**Lodging Reservation Instructions**

A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut St., Philadelphia, PA at a nightly rate of $142 for a Standard room. This hotel is about a 5-minute walk from the seminar location. To make a reservation, you must call 203-905-2100 during business hours and identify yourself by giving the **group code ST1023**. For guaranteed rate and availability, you must make your reservation by September 23, 2014. **There is a shortage of hotel rooms in Philadelphia for these dates so please make your reservation as early as possible.**

### SEMINAR OUTLINE

1. Introduction to R and RStudio

2. Importing Data, Part I

3. Basic Analysis

a. Exploratory analysis

b. T-tests

c. ANOVA

d. Multiple regression

4. Data Management

a. Restructuring data sets

b. Setting variable attributes and levels

c. Transformations

d. Dealing with different types of data: numbers, dates, characters, strings

e. Missing data

5. Plots and Graphics

a. Scatterplots

b. Basic plotting commands to build your own publication quality points

6. Writing Your Own Functions

a. Introduction

b. Saving your functions to re-use in subsequent sessions

7. Basics of Packages

8. Importing Data, Part II

9. Advanced Statistical Methods

a. Generalized linear models

b. Random effects methods

c. Principal components and factor analysis

d. Structural equations modeling, including mediation

e. Classification and regression trees

f. Bootstrapping

g. Basic psychometrics

10. Basic Simulations

11. Advanced Graphics

a. ggplot2 package

b. Three dimensional and interactive graphics

12. Getting Help Beyond the Workshop

13. Discussion of Workflow Issues with R

a. Preparing reports

b. Lab protocols for syntax files