R vs SPSS: Which to Learn, and How to Move Between Them

Learn SPSS if your course, department or employer works in it and you need results soon; learn R if you want free, unlimited and fully reproducible analysis that will carry into research or data-science work. Many people need both: SPSS (or an SPSS-style tool) for the coursework in front of them, and R for what comes after.

This guide compares the two honestly and shows practical ways to move between them, including reading .sav files in R and Python and turning SPSS dialogs into R code. It's published by the team behind TRTT, a free SPSS-style tool whose Paste button writes R and Python code; we explain exactly what that code does and doesn't do.

Short answer

Learning curve

SPSS is quick to start. You open a .sav file, choose Analyze ▸ Compare Means ▸ Independent-Samples T Test (in SPSS 32 the submenu is called Compare Means and Proportions), move two variables into boxes and click OK. For a student who needs a t test by Friday, that's hard to beat. The difficulty arrives later, when you need something the menus don't offer, or when you have to redo twenty analyses after the data change.

R asks more of you up front: installing R and an editor such as RStudio or Positron, learning to load data, understanding data frames and factors, and reading error messages. The first week is slower. After that, each new analysis is usually a few lines, and a mistake is fixed by editing the script and running it again.

If you're unsure, a useful test is how often you'll repeat the work. A one-off assignment favours SPSS; a thesis with revisions, or a job with recurring reports, favours R.

Reproducibility

Reproducibility means that you (or a reviewer) can rerun the analysis and get the same numbers.

• In R, the script is the analysis. Combined with a tool like R Markdown or Quarto, the code, results and text live in one document that rebuilds itself.
• In SPSS, every dialog has a Paste button that writes the command into a syntax file instead of running it. A saved .sps file is just as reproducible as an R script. The catch is habit: it's easy to click OK and never save the syntax, and then nobody can tell exactly which options produced a table.

If you work in SPSS, make Paste your default. If you work in R, keep your scripts with your data. Either way, the .sav or data file should stay untouched, with every change made by code.

Analyses and packages

R's breadth comes from its packages. CRAN, the main R package repository, listed over 25,000 packages when we checked in October 2026, covering everything from mixed models and structural equation modelling to text analysis and machine learning. New methods often appear in R first because researchers publish them as packages.

SPSS's catalogue is broad and polished, but split by licence. IBM's subscription has a Base tier plus add-on bundles (for example regression, advanced statistics and custom tables in one; forecasting and decision trees in another). What you can run depends on what your institution bought. SPSS can also run Python and R code inside BEGIN PROGRAM blocks, which lets advanced users extend it.

Python is the other code-first option. With pandas for data, SciPy and statsmodels for statistics and pyreadstat for .sav files, it covers most of what a social scientist needs, and it's the stronger choice if you're heading toward general programming or machine learning.

Reading .sav in R (haven) and Python (pyreadstat)

You don't have to convert your .sav to CSV first; both languages read SPSS files directly with labels.

R with haven:

library(haven)
d <- read_sav("survey.sav", user_na = TRUE)  # keep user-defined missing values
attr(d$q1, "label")        # variable label
d_labels <- as_factor(d)   # replace codes with value labels
write_sav(d, "survey_clean.sav")

Without user_na = TRUE, user-defined missing values such as 99 = "Refused" become NA. Labelled variables arrive as labelled_spss columns.

Python with pyreadstat:

import pyreadstat
df, meta = pyreadstat.read_sav("survey.sav", user_missing=True)
meta.column_names_to_labels    # variable labels
meta.variable_value_labels     # value labels
pyreadstat.write_sav(df, "survey_clean.sav")

The labels live in meta, separate from the DataFrame. For more on what survives a conversion to CSV or Excel, see convert .sav to CSV or Excel.

From a dialog to R code

The hardest part of switching is knowing what code corresponds to the dialog you already understand. Two tools help:

• jamovi shows the R code behind each analysis, and its jmv package runs the same analyses in R.
• TRTT, a free browser tool with SPSS 32-style menus, has a Paste button that writes three versions of what you clicked: SPSS syntax, R code and Python code.

For example, an independent-samples t test of nps by gender (groups 1 and 2) pastes this R code in TRTT:

# TRTT · T-TEST (unweighted; SPSS would apply the active weight)
library(haven)
if (!exists("d")) d <- read_sav("data.sav")
g <- as.numeric(d[["gender"]])
for (v in c("nps")) {
  x <- as.numeric(d[[v]])
  a <- x[g == 1 & !is.na(x)]
  b <- x[g == 2 & !is.na(x)]
  cat("\n", v, "by gender\n")
  print(t.test(a, b, var.equal = TRUE))   # equal variances assumed
  print(t.test(a, b, var.equal = FALSE))  # equal variances not assumed
}

It mirrors the two rows of the SPSS Independent Samples Test table: equal variances assumed and not assumed. The Python version uses pyreadstat, pandas and SciPy.

Be clear about what this is. The code aims to compute the same statistics, not to print the same tables, and some details differ: the comment in the first line flags that this version ignores an active weight, which SPSS would apply. Treat pasted code as a starting point for learning, and compare the key numbers with the output before relying on it.

TRTT itself runs procedures, transformations and labels from .sps files in a desktop browser, free and without an account, with the calculations on your own computer. It doesn't support every SPSS command (DO IF, IF, LOOP, TO ranges, AGGREGATE and MATCH FILES aren't there yet), its web version has no PDF export, and it isn't open source. Details: run SPSS syntax online. For the wider field of SPSS alternatives, see SPSS alternatives compared.

Open TRTT in your browser – free

FAQ

Is R better than SPSS?

R is more powerful, more flexible and free; SPSS is faster to learn for standard analyses and is what many courses use. If you'll analyse data regularly or need methods beyond the basics, R pays off. For a single course that teaches SPSS, SPSS (or a tool that follows it) is the practical choice.

Should I learn R or SPSS first?

Learn what your course uses first, so your grades don't depend on learning two things at once. If you have a free choice and expect to keep doing data analysis, start with R; the point-and-click tools are easy to pick up later.

What is the difference between RStudio and SPSS?

RStudio is a free editor for writing and running R code; the statistics come from R and its packages. SPSS is a complete statistics program with menus, dialogs and its own syntax. Comparing RStudio with SPSS is really comparing R with SPSS.

Can R open SPSS files?

Yes. The haven package's read_sav() reads .sav and .zsav files with variable and value labels, and write_sav() writes them back.

Is Python a good alternative to SPSS?

Yes, if you're comfortable writing code. pandas, SciPy, statsmodels and pyreadstat cover most standard analyses and read .sav files. Python suits people who also want general programming or machine-learning skills.

TRTT is an independent product. It is not affiliated with or endorsed by IBM. IBM and SPSS are trademarks of International Business Machines Corporation.