Who is this for?

This course is designed for anyone who currently uses SPSS and wants to transition to a free, more powerful alternative. You do not need any programming experience. If you understand basic statistical concepts like means, standard deviations, and hypothesis testing, you have everything you need to start.

Students Using SPSS for coursework or thesis research and looking for a cost-free alternative
Lecturers Teaching research methods and interested in integrating open-source tools into courses
Researchers Seeking reproducible analysis workflows and better visualization capabilities
Institutional analysts Working with data in government, healthcare, or policy and paying for software licenses

What you will learn

  • Perform common statistical analyses in R that you currently do in SPSS
  • Create publication-quality visualizations using ggplot2
  • Import, clean, and manipulate data efficiently with tidyverse tools
  • Understand the benefits of script-based analysis for reproducibility
  • Use R Markdown to combine analysis and reporting in a single document
  • Know where to find help and continue learning independently

Course structure

The course runs across two sessions in a single week, giving you time between sessions to practice and absorb. Each session combines short demonstrations with hands-on exercises using real data. Every SPSS operation is paired with its R equivalent, so you always have a clear reference point. The material is organised as seven episodes on the lesson website, linked from the schedule below so you can jump straight to any topic.

Session 1: Why R, and getting started

Wednesday 22 April · 9:00–16:00
09:00 – 09:10

Welcome and introductions

Overview of the two days, a short round of introductions, and what we aim to achieve by Thursday afternoon.

09:10 – 09:55

Episode 1 · The case for switching

Live demonstration of what R can do that SPSS cannot. Cost comparison, reproducibility advantages, and career relevance. The goal is motivation before any coding begins.

09:55 – 10:10
Coffee break
10:10 – 11:40

Episode 2 · Your first R session

Navigating RStudio, understanding the interface, running your first commands. Importing a dataset and performing basic operations you already know from SPSS: descriptive statistics, frequency tables, variable inspection.

11:40 – 12:00

Questions and consolidation

A short Q&A to make sure everyone is on the same page before the lunch break.

12:00 – 13:00
Lunch
13:00 – 14:30

Episode 3 · Data manipulation

Filtering, sorting, recoding, and creating new variables. Every operation is shown side-by-side with its SPSS menu equivalent. Introduction to the tidyverse workflow for chaining operations together.

14:30 – 14:45
Afternoon break
14:45 – 15:45

Episode 4 · Your first visualization

Building charts with ggplot2. Starting from a basic plot and layering elements to produce something publication-ready. Comparison with SPSS chart builder output.

15:45 – 16:00

Wrap-up and homework brief

Recap of Day 1 and a walkthrough of the between-day homework: a short exercise on your own data, so you arrive on Friday with questions and momentum.

Between days

Homework brief

The 48 hours between sessions is when the course becomes a skill. Participants open a standalone homework page and write a short R script against a dataset they already use: import, transform, summarise, chart. Thirty to sixty minutes. The script comes back into the Friday open lab.

Session 2: Analysis and reproducibility

Friday 24 April · 9:00–16:00
09:00 – 09:30

Review and troubleshooting

Addressing questions from between-session practice. Common errors and how to read R error messages, a skill SPSS users rarely need.

09:30 – 09:45
Short break
09:45 – 10:45

Episode 5 · Statistical analysis, part 1

The tests participants use most often: t-tests and ANOVA. For each test, the SPSS dialog is shown alongside the R code, and we interpret the output together.

10:45 – 11:00
Coffee break
11:00 – 12:00

Episode 5 · Statistical analysis, part 2

Correlation and regression. Extracting results cleanly and a first look at how to save outputs for reports.

12:00 – 13:00
Lunch
13:00 – 14:00

Episode 6 · Reproducible reporting

Introduction to R Markdown: combining narrative text, code, and output in a single document. Creating a report that updates automatically when data changes.

14:00 – 14:15
Afternoon break
14:15 – 15:00

Episode 7 · Where to go from here

A close-out on local reference datasets (UA's CAS election data and island-research reference data), community forums and package documentation for continued learning, and building a personal reference library of R scripts that replace your SPSS workflows.

15:00 – 15:45

Open lab

Time to apply what you have learned to your own data or a worked example, with the facilitator on hand to answer questions.

15:45 – 16:00

Wrap-up and next steps

Recap, peer connections, and how to stay in touch with the network after the course.

Before the course

Participants receive preparation materials two weeks before the course starts. This ensures we spend instruction time learning R, not troubleshooting installations.

1
Two weeks before Installation instructions for R and RStudio sent to all participants, with screenshots and troubleshooting tips for common issues.
2
One week before Optional 30-minute online installation clinic. Bring your laptop, get help if anything went wrong, confirm your setup is ready.
3
Pre-course survey A short questionnaire about which SPSS analyses you use most. This helps tailor exercises to what participants actually need.

Why switch from SPSS to R?

The practical case for transitioning goes beyond cost savings, though those are significant. R gives you a reproducible workflow where every analysis step is documented in a script. You can share your exact analysis with a colleague, rerun it on new data, or return to it years later and know precisely what was done. SPSS point-and-click workflows cannot offer this.

R also opens doors that SPSS keeps closed. Advanced visualization, text analysis, spatial data, machine learning, and automated reporting are all accessible through R's package ecosystem at no additional cost. And because R is the tool of choice in data science and increasingly in academic research, proficiency signals a modern skill set to employers and collaborators.

Tool Annual cost per user 5-year cost
SPSS (standard license) AWG 400+ AWG 2,000+
R + RStudio Free Free

For institutions: Every SPSS license replaced by R proficiency is a recurring cost saved. For institutions facing budget pressure across the Dutch Caribbean, this represents tangible financial value. The course is designed to build on existing statistical knowledge rather than starting from scratch, meaning SPSS-fluent researchers can transition without losing productivity.

Course materials

All lesson materials, exercises, datasets, and reference guides are published openly and freely accessible. You can browse them before, during, and after the course.

Lesson website All 7 episodes with worked examples, exercises, and solutions. The SPSS-to-R reference card is here too. Open lessons → Source & data GitHub repository with all source files, datasets, and R scripts. Fork it, reuse it, improve it. View on GitHub →

Open educational resource. These materials are licensed under CC-BY 4.0. Anyone in the DCDC Network — or beyond — can reuse, adapt, and deliver this course. That is the point.

Part of a regional program

This course is the first in the DCDC Network's training program. It pilots in Aruba and will be offered on other islands based on local interest and scheduling. All course materials are developed as open resources: slides, exercises, datasets, and facilitator notes will be available for any network member to reuse and adapt. The goal is not a one-time workshop but a transferable capability that stays in the region.

Interested in bringing this course to your island or institution? Get in touch.