R for SPSS Users: A Short Course

This course takes the perspective of the SPSS users. SPSS users know how to carry out their preferred analyses in SPSS. However, in moving to R, analysis approaches, workflows, and output often differ from R defaults. This course teaches you how to derive all of statistics common to SPSS (and commonly reported) in R.

This course is hands-on. There are regular exercises to ensure understanding and online help for those who get stuck. You are encouraged to bring your own data and post questions about translating your SPSS-focused analyses to R.

This seminar will familiarize you with using R through the RStudio interface and demonstrate how to smoothly transition to working in the R environment. You will leave with course materials including detailed examples of all common social science analyses and approaches.

To engage with hands-on exercises, you will need to have a current version of R and RStudio installed on your computer. Both are free and available on all major platforms. Prior to the course, attendees will receive detailed instructions for installation on Windows and Macintosh platforms.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.

This course is for SPSS users who want to move to the R environment. It is designed for attendees who want to transition to R seamlessly with a minimal learning curve.

Introduction to the R computing environment

• Basics of code structure
• Tour of RStudio set up and features

Importing data

• Viewing data in RStudio
• Hands on work focused on importing data

Introduction to packages

• Installing packages
• Useful packages
• Errors that occur when installing packages and how to fix them

Importing SPSS (and other) files

• Hands on work importing an SPSS file and installing and loading packages
• Overview of strategies for importing other data formats
• How to point functions to data

Basic data visualizations

• Histograms
• Bar charts
• Scatterplots
• Boxplots
• Hands-on work with graphs (note: this is a nice starting point for commands as most find commands like hist(var) straightforward)

Data types

• Review of SPSS data types (scale, ordinal, nominal)
• Drawing connections between SPSS data types R data types
• Common problems and solutions – e.g., factors imported as numbers, factor levels in wrong order

Introduction to data manipulation

• Review of common SPSS tasks such as select cases, transform-compute, recode, create summary scores
• Carrying out common SPSS tasks in R
• Hands-on work with data

Descriptive statistics

• CT and dispersion
• Skew, Kurtosis
• Percentiles
• Frequencies
• Hands on work running various descriptive measures

Correlations/Simple linear regression

• Pearson’s correlation approaches
• Work with scatterplots
• Linear regression
• Adding regression lines to scatterplots
• Hands on work

Chi-square tests and variations

• Goodness of Fit and Test of Independence
• Frequency/Proportion tables
• Effect sizes
• Hands on work with Chi-square including installing lsr package

ANOVA basics

• One factor and factorial
• Descriptive for ANOVA
• Graphs for ANOVA (Introduction to GGPlot2)*
• Setting up R for factorial ANOVA to match SPSS settings*
• Getting information out of the ANOVA via summary
• Hands on work with ANOVA

Multiple regression

• Basic output
• Getting additional statistics (e.g., standardized coefficients)*
• Assumption plots
• Hands on work with MR

*Time permitting. However, materials and annotated code for these topics will be provided.