Key Points
The Case for Switching
- 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
Your First R Session
- RStudio is your workspace — it combines a script editor, console, and data viewer
-
haven::read_sav()imports SPSS files directly, preserving labels -
summary(),table(), andstr()replace the Descriptives and Frequencies menus in SPSS
Data Manipulation
- dplyr verbs (
filter,select,mutate,arrange,summarise) replace SPSS menu operations - The pipe operator
|>chains operations together, making code readable -
group_by()combined withsummarise()replaces SPSS Split File + Aggregate
Visualization with ggplot2
- ggplot2 builds plots in layers: data, aesthetics, geometry, labels, theme
- Every SPSS Chart Builder chart has a ggplot2 equivalent that offers more control
- Faceting (
facet_wrap) lets you create small multiples — something SPSS Chart Builder handles poorly
Statistical Analysis in R
- Every SPSS statistical test has a direct R equivalent, usually in a single function call
- R output is more compact than SPSS —
broom::tidy()converts it to a clean table - The workflow in R is: load data, run test, extract results, visualize — all in a script
Reproducible Reporting
- R Markdown combines your analysis and write-up in a single document
- When data changes, re-knitting updates every table and figure automatically
- You can output to Word, PDF, or HTML from the same source file
- This eliminates the copy-paste errors that are common with SPSS output
Where to Go from Here
- R has a large, active community — you are never stuck alone
-
cbsodataRandWDIlet you pull Dutch Caribbean data directly into R - Save your analyses as scripts and build a personal reference library
- The DCDC Network is your regional peer community for continued learning