The Case for Switching

Last updated on 2026-04-22 | Edit this page

Overview

Questions

  • Why should I switch from SPSS to R?
  • What can R do that SPSS cannot?
  • How much does SPSS actually cost compared to R?

Objectives

  • Describe the practical advantages of R over SPSS for research
  • See live examples of R capabilities that go beyond SPSS
  • Understand the cost and reproducibility arguments for switching
Cartoon of a researcher at a Caribbean crossroads choosing between a cracked SPSS path and a paved R path leading to the coast
One road costs you a license fee. The other one costs you a learning curve.

Introduction

This episode is a motivational opening. You will not write any code yourself yet — sit back and watch the instructor demonstrate what R can do. By the end, you should have a clear picture of why learning R is worth the investment of your time.

What you will be able to produce

By Friday afternoon you will be able to take a single R Markdown file and produce a polished, multi-page PDF report from it with one command. The worked example we will return to is a baseball statistics report on Xander Bogaerts, Aruba’s MLB star — the kind of document that would ordinarily take an afternoon of copy-paste between Excel, SPSS output, and Word, but that here is one file and one click. You will see the finished report in this opening so you know what you are aiming at. In Episode 6 you will build a smaller version of the same thing yourself.

What you will see

The instructor will demonstrate four things that are impossible or impractical in SPSS:

  1. Pulling a live research dataset — the DCDC Network’s small-island reference list, maintained on GitHub at the University of Aruba — straight into R, no browser required.
  2. Creating a publication-quality chart in under 10 lines of code.
  3. A reproducible report that updates automatically when new data arrives.
  4. An interactive dashboard built entirely in R.

If any of those sound appealing, you are in the right place.

The cost argument

Let us start with the most concrete reason. SPSS is expensive — especially for small island institutions that pay per seat.

SPSS Standard R + RStudio
License type Annual subscription Free, open-source
Cost per user per year USD 1,170 – 5,730 (varies by tier) USD 0
5-year cost for 5 users USD 29,250 – 143,250 USD 0
Runs on Windows, Mac Windows, Mac, Linux, cloud
Updates Paid upgrades Continuous, free

For a university department in the Dutch Caribbean with three SPSS licenses, that is easily AWG 10,000+ per year that could be redirected to research funding, student assistants, or conference travel.

Callout

“But my institution already pays for SPSS”

That is true today. But institutional budgets change, and when you graduate or change jobs, your personal SPSS license disappears. R stays with you forever — on your laptop, on a cloud server, on a Raspberry Pi if you want. Your scripts will still run in 10 years.

What R gives you that SPSS does not

Reproducibility

In SPSS, a typical workflow looks like this: open a dataset, click through menus, copy output into Word, repeat. If your supervisor asks “can you re-run this with the updated data?”, you have to remember every click.

In R, your entire analysis lives in a script. You change one line (the file path) and re-run. Every step is documented.

Packages

SPSS has a fixed set of procedures. R has over 20,000 add-on packages on CRAN alone, covering everything from Bayesian statistics to text mining to geographic mapping. If a method exists, there is probably an R package for it.

Automation

Need to run the same analysis on 50 files? In SPSS, that means 50 times through the menus (or learning SPSS syntax, which few people do). In R, it is a three-line loop.

Communication

R Markdown and Quarto let you combine narrative text, code, and output into a single document — a PDF, a Word file, a website, or a slideshow. This lesson itself was built with R.

Career value

Data science job postings almost never list SPSS. R and Python dominate. Even within academia, journals increasingly expect reproducible code alongside submissions.

Live demonstration

The instructor will now run a live demonstration. Watch the screen.

Callout

What is happening on screen

Do not worry about understanding the code right now. The goal is to see what is possible. You will learn the building blocks starting in the next episode.

Summary

You have now seen R:

  • Pull live data from the internet with a single function call
  • Create a publication-ready chart in 10 lines of code
  • Do both of these things in a way that is fully reproducible

Starting in the next episode, you will learn to do these things yourself — one step at a time.

Key Points
  • R is free, open-source, and runs on any operating system
  • R scripts make your analysis fully reproducible
  • R can pull data from APIs, create interactive visualizations, and automate reports — things SPSS cannot do
  • Switching builds on your existing statistical knowledge, not replaces it