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, which SPSS cannot do
Switching builds on your existing statistical knowledge rather than replacing it

RStudio is your workspace, combining a script editor, console, and data viewer
haven::read_sav() imports SPSS files directly, preserving labels
readxl::read_excel() reads Excel files and can target a named sheet
summary(), table(), and str() replace the Descriptives and Frequencies menus in SPSS
A character column full of number-looking values is R refusing to lie to you about the data

dplyr verbs (filter, select, mutate, arrange, summarise) replace SPSS menu operations
The pipe operator |> chains operations together, making code readable
group_by() combined with summarise() replaces SPSS Split File + Aggregate
The mean of a TRUE/FALSE column is a proportion, which saves you building dummy variables
Keep an explicit Unknown category rather than dropping incomplete rows

ggplot2 builds plots in layers: data, aesthetics, geometry, labels, theme
Every SPSS Chart Builder chart has a ggplot2 equivalent that offers more control
position = "fill" turns counts into proportions, which is usually the honest comparison
Highlight specific cases by layering a second geom_point() over a grey base
Faceting (facet_wrap, facet_grid) creates small multiples, which SPSS Chart Builder handles poorly

Every SPSS statistical test has a direct R equivalent, usually in a single function call
R output is more compact than SPSS, and broom::tidy() converts it to a clean table
Use chisq.test() on a crosstab when both variables are categorical
A non-significant predictor is a result; do not drop terms to make the table look better
Every row you filter out is a methods sentence you owe the reader
Rank variables invert the sign of a coefficient, so say what direction means

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
Inline R code puts computed numbers inside your sentences, so text and results cannot drift apart
Write your caveats into the code that produces them, so they travel with the number
Parameterise a report and one file serves every subgroup in your data
Make the data file a parameter too, and a new release of the data is one changed line
Check what your .default branch caught every time the data changes

R has a large, active community, so you are never stuck alone
cbsodataR and WDI pull Dutch Caribbean data directly into R
Country coding schemes differ between sources, and that is where most joins break
A stopword list is an analytical choice; publish it with your results
Save your analyses as scripts and build a personal reference library
The DCDC Network is your regional peer community for continued learning