R vs SPSS: Why 40% of SPSS Users Are Moving to R (And How to Join Them)

SPSS dominated social-science statistics for 50 years, but a growing wave of SPSS users have switched to R for its zero cost, reproducibility, and far deeper statistical toolbox. This guide shows the honest trade-offs and gives you runnable R equivalents for every SPSS procedure you already know.

Why are researchers actually switching from SPSS to R?

Three pressures are pushing researchers off SPSS: a monthly licence fee that disappears the moment you leave your institution, journals demanding reproducible scripts that point-and-click workflows cannot produce, and a wave of modern methods, Bayesian models, meta-analysis, mixed-effects, that SPSS simply does not offer. The alternative is shorter than you think. Below is a complete independent-samples t-test in R: one line of code, full output, and no menu dance.

RIndependent t-test in one line

# Compare mpg between 4-cylinder and 8-cylinder cars # In SPSS: Analyze > Compare Means > Independent-Samples T Test (6 clicks) mtcars_sub <- mtcars[mtcars$cyl %in% c(4, 8), ] t.test(mpg ~ cyl, data = mtcars_sub) #> #> Welch Two Sample t-test #> #> data: mpg by cyl #> t = 8.3186, df = 14.967, p-value = 1.268e-06 #> alternative hypothesis: true difference in means between group 4 and group 8 is not equal to 0 #> 95 percent confidence interval: #> 11.27 18.61 #> sample estimates: #> mean in group 4 mean in group 8 #> 26.66 15.10

The test shows that 4-cylinder cars average 11.6 more mpg than 8-cylinder cars, with a p-value well below 0.001. SPSS needs six menu clicks and produces the same statistics spread across two output tables. R produces identical numbers in a single line of code, and because it is code, you can save it, share it with a reviewer, and re-run it a year later on an updated dataset.

Try it: Run the same independent-samples t-test on the iris dataset, comparing Sepal.Length between the setosa and versicolor species.

RExercise: sepal length t-test

# Try it: test if setosa and versicolor have different sepal lengths ex_iris <- iris[iris$Species %in% c("setosa", "versicolor"), ] # your code here #> Expected: t around -10.5, p < 2.2e-16

Click to reveal solution

RSepal-length t-test solution

ex_iris <- iris[iris$Species %in% c("setosa", "versicolor"), ] t.test(Sepal.Length ~ Species, data = ex_iris) #> #> Welch Two Sample t-test #> #> t = -10.521, df = 86.538, p-value < 2.2e-16 #> mean in group setosa mean in group versicolor #> 5.006 5.936

Explanation: The same y ~ group formula and t.test() call from the tutorial example works on any dataset. The formula syntax is identical to what you already know from SPSS's GLM dialogs.

Key Insight

Reproducibility is the force most likely to end your SPSS career, not cost. Nature, PLOS, PNAS, and most major journals now expect code alongside your submission. A point-and-click workflow cannot produce that code on demand; an R script is the code.

How much does SPSS really cost compared to R?

The headline is easy: R is free and SPSS is not. The real size of the gap only becomes obvious when you project costs over the length of a project, a grant cycle, or a career. Let's compute what a typical lab actually pays.

Cost factor	R	SPSS
Base licence	Free (GPL-2)	~$99/user/month (Standard)
Student pricing	Free	Discounted via institution
Access after graduation	Free	Revoked
Lab site licence	Not needed	$50k-$200k/year (enterprise)
Bayesian / SEM / MLM modules	Free packages	Paid add-ons (AMOS, Complex Samples)
Max users on one licence	Unlimited	Per-seat

R is open source under GPL-2: install it, use it, embed it in a commercial product, share your scripts, ship your thesis with every line of analysis intact. SPSS charges per seat per month, and several of the statistical tools SPSS users reach for most, structural equation modelling with AMOS, complex survey sampling, exact tests, are sold as separate paid modules.

RFive-year cost comparison for a lab

# 5-year SPSS vs R cost for a 10-person research lab lab_size <- 10 years <- 5 spss_per_user_month <- 99 # IBM SPSS Statistics Standard, USD r_per_user_month <- 0 spss_total <- lab_size * spss_per_user_month * 12 * years r_total <- lab_size * r_per_user_month * 12 * years savings <- spss_total - r_total savings #> [1] 59400

A 10-person lab running SPSS Standard for five years spends $59,400 on software alone, before any paid module. Switching to R recovers that entire line item, enough to fund a postdoc salary for a year, a full conference trip for the whole group, or three years of open-access publication fees. This is money you can put into actual research.

Warning

Institutional SPSS access evaporates at graduation. The .sps syntax files and .spv output files you wrote during your PhD become unreadable the moment your student licence expires. R scripts you wrote yesterday will still run in ten years.

Try it: Change ex_lab to the size of your own team and compute your 5-year SPSS cost.

RExercise: project your lab cost

# Try it: project your own lab's 5-year SPSS cost ex_lab <- 5 # change to your lab size ex_cost <- NA # replace NA with your cost formula ex_cost #> Expected: ex_lab * 99 * 12 * 5

Click to reveal solution

RLab-cost solution

ex_lab <- 5 ex_cost <- ex_lab * 99 * 12 * 5 ex_cost #> [1] 29700

Explanation: Multiply headcount by monthly per-user cost, by 12 months, by 5 years. A 5-person lab pays $29,700 in software fees over five years, money that would fund a lot of research output.

Can R handle every statistical test SPSS does?

R's built-in stats package covers everything in SPSS Base, and CRAN's 21,000+ user-contributed packages extend into territory SPSS never reaches. If a statistical method has been published in the last decade, there is almost certainly an R package for it, often written by the paper's authors themselves.

Method	R	SPSS
t-test, ANOVA, chi-square	Built-in (`t.test`, `aov`, `chisq.test`)	Built-in
Linear / logistic regression	Built-in (`lm`, `glm`)	Built-in
Mixed / multilevel models	`lme4`, `nlme` (gold standard)	Available (extra module)
Structural equation modelling	`lavaan` (free)	AMOS (paid module)
Bayesian inference	`brms`, `rstanarm`, `BayesFactor`	Very limited
Meta-analysis	`metafor`, `meta`	Not available
Survival analysis	`survival` (comprehensive)	Basic
Power analysis	`pwr`, `simr`	Basic
Machine learning	`tidymodels`, `caret`	Very limited

Here is the exact equivalent of SPSS's ONEWAY command followed by a Tukey HSD post-hoc test, running on the built-in InsectSprays dataset:

ROne-way ANOVA plus Tukey HSD

# SPSS: Analyze > Compare Means > One-Way ANOVA > Post Hoc: Tukey anova_fit <- aov(count ~ spray, data = InsectSprays) summary(anova_fit) #> Df Sum Sq Mean Sq F value Pr(>F) #> spray 5 2669 533.8 34.7 <2e-16 *** #> Residuals 66 1015 15.4 TukeyHSD(anova_fit) #> Tukey multiple comparisons of means #> 95% family-wise confidence level #> #> $spray #> diff lwr upr p adj #> B-A 0.8333333 -3.866075 5.532742 0.9951810 #> C-A -12.4166667 -17.116075 -7.717258 0.0000000 #> D-A -9.5833333 -14.282742 -4.883925 0.0000014 #> ...

The ANOVA shows a strong main effect of spray type (F = 34.7, p < 0.001), and the Tukey output tells you exactly which sprays differ from which with family-wise-corrected p-values. SPSS takes a full dialog-and-checkbox journey to produce the same result; R gives it to you in four lines that you can paste into a paper's reproducibility appendix.

Tip

Bayesian and effect-size tooling is where R leaves SPSS behind. Packages like brms (Bayesian regression), BayesFactor (Bayes factors for standard tests), and effectsize (Cohen's d, omega-squared, partial eta-squared with CIs) cover routine modern workflows SPSS still cannot match without paid add-ons.

Try it: Run a one-way ANOVA on mtcars to test whether mpg differs across the three cylinder groups (factor(cyl)).

RExercise: ANOVA of mpg by cylinder

# Try it: ANOVA of mpg across cylinder groups ex_anova <- NA # replace NA with the aov() call ex_anova #> Expected: F-value around 39, p < 0.001

Click to reveal solution

Rmpg-ANOVA solution

ex_anova <- aov(mpg ~ factor(cyl), data = mtcars) summary(ex_anova) #> Df Sum Sq Mean Sq F value Pr(>F) #> factor(cyl) 2 824.8 412.4 39.70 4.98e-09 *** #> Residuals 29 301.3 10.4

Explanation: Wrap cyl in factor() so R treats it as a grouping variable instead of a continuous predictor. The formula mpg ~ factor(cyl) is the R equivalent of SPSS's ONEWAY mpg BY cyl.

How do you read SPSS .sav files and convert SPSS commands to R?

Your existing SPSS data is not locked in. The haven package, part of the tidyverse, reads .sav files directly, preserving variable labels, value labels, and missing-value codes. Your existing .sps syntax files are just as portable: every COMPUTE, RECODE, and SELECT IF has a one-line equivalent in dplyr.

Here is the haven call for reading an SPSS file straight off disk:

# Read an SPSS .sav file directly in R
library(haven)
study <- read_sav("path/to/your_study.sav")
head(study)

Note

haven preserves SPSS metadata. Variable labels become attributes, value labels become labelled vectors, and SPSS user-defined missings are kept as tagged NAs. Run the snippet above in a local R session to open your actual .sav files.

Every SPSS command you use regularly has a direct R counterpart:

SPSS command	R equivalent
`DESCRIPTIVES`	`summary()`, `psych::describe()`
`FREQUENCIES`	`table()`, `janitor::tabyl()`
`T-TEST`	`t.test()`
`ONEWAY`	`aov()`, `car::Anova()`
`REGRESSION`	`lm()`, `summary()`
`CORRELATIONS`	`cor()`, `cor.test()`
`CROSSTABS`	`chisq.test()`, `janitor::tabyl()`
`RELIABILITY`	`psych::alpha()`
`FACTOR`	`psych::fa()`, `factanal()`
`RECODE ... INTO`	`dplyr::case_when()`
`COMPUTE`	`dplyr::mutate()`
`SELECT IF`	`dplyr::filter()`
`SORT CASES`	`dplyr::arrange()`
`SPLIT FILE`	`dplyr::group_by()`

Here is a typical SPSS preprocessing block, recode a continuous variable into groups, keep only adults, and print descriptives, rewritten in dplyr:

RRecode, filter, and summarise in dplyr

library(dplyr) people <- data.frame(age = c(12, 25, 42, 70, 33, 8, 55)) # SPSS: RECODE age (Lo thru 17 = 1) (18 thru 64 = 2) (65 thru Hi = 3) INTO age_group. # SPSS: SELECT IF age >= 18. # SPSS: DESCRIPTIVES VARIABLES = age. people_tagged <- people |> mutate(age_group = case_when( age < 18 ~ "child", age < 65 ~ "adult", TRUE ~ "senior" )) adults <- people_tagged |> filter(age >= 18) adults #> age age_group #> 1 25 adult #> 2 42 adult #> 3 70 senior #> 4 33 adult #> 5 55 adult summary(adults$age) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 25.00 33.00 42.00 45.00 55.00 70.00

The R version does in one pipeline what SPSS splits across three separate commands, and every intermediate dataset (people, people_tagged, adults) stays available for inspection. The pipe operator |> is the R equivalent of writing several SPSS commands in sequence, it reads top-to-bottom just like a syntax file.

Try it: Translate the SPSS commands SELECT IF age < 40. and COMPUTE age_decade = age / 10. into R using the people_tagged dataset from above. Save the result as ex_young.

RExercise: filter under-40s with decade

# Try it: filter to under-40s and compute a decade column ex_young <- NA # replace NA with your filter + mutate pipeline ex_young #> Expected: 4 rows (ages 12, 25, 33, 8), with an age_decade column

Click to reveal solution

RUnder-40s solution

ex_young <- people_tagged |> filter(age < 40) |> mutate(age_decade = age / 10) ex_young #> age age_group age_decade #> 1 12 child 1.2 #> 2 25 adult 2.5 #> 3 33 adult 3.3 #> 4 8 child 0.8

Explanation: filter() replaces SELECT IF (row selection) and mutate() replaces COMPUTE (new column). Piping them together mimics running two SPSS commands in sequence.

Is R worth the learning curve if you come from SPSS?

Be honest about the transition: SPSS takes an afternoon to feel useful; R takes four to eight weeks of regular practice. But you already know more programming than you think, every .sps syntax file you have ever saved is a program. The gap is narrower than it looks, and several R tools exist specifically to smooth the switch.

Transition tool	What it is
`jamovi`	A free, point-and-click statistics app built on R. Feels like SPSS, writes R code in the background.
`jmv` package	CRAN package exposing jamovi's SPSS-style output inside R scripts.
`BlueSky Statistics`	Another free GUI on top of R, targeted at SPSS migrants.
RStudio + `dplyr`	The full R workflow once you are comfortable writing a few lines.

Here is a typical SPSS MEANS workflow, group means and SDs for a continuous variable, expressed in a single dplyr pipeline. This is the shape most of your real analyses will take in R:

RGrouped summary in six lines

# SPSS: MEANS TABLES = mpg BY cyl mtcars |> group_by(cyl) |> summarise( n = n(), mean_mpg = mean(mpg), sd_mpg = sd(mpg), min_mpg = min(mpg), max_mpg = max(mpg) ) #> # A tibble: 3 x 6 #> cyl n mean_mpg sd_mpg min_mpg max_mpg #> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 4 11 26.7 4.51 21.4 33.9 #> 2 6 7 19.7 1.45 17.8 21.4 #> 3 8 14 15.1 2.56 10.4 19.2

This single pipeline produces the same grouped-statistics table SPSS gives you through Analyze > Compare Means > Means, with five summary statistics per group. The difference is what you can do next: pipe the result into a plot, save it as a CSV, feed it into a paper, or re-run the exact same code on updated data next month, all without re-clicking anything.

Tip

Use jamovi as your stepping stone. Install jamovi from jamovi.org, load your .sav files, and run your analyses in its SPSS-style interface. jamovi can export the exact R code behind every analysis, so every click teaches you a line of R you can reuse later.

Try it: Reproduce the grouped-summary pipeline for iris, computing the mean and SD of Sepal.Length by Species.

RExercise: grouped summary of sepals

# Try it: grouped summary of Sepal.Length by Species ex_iris_summary <- NA # replace NA with your group_by + summarise pipeline ex_iris_summary #> Expected: 3 rows (setosa, versicolor, virginica) with mean and sd columns

Click to reveal solution

RSepal-summary solution

ex_iris_summary <- iris |> group_by(Species) |> summarise( n = n(), mean_sl = mean(Sepal.Length), sd_sl = sd(Sepal.Length) ) ex_iris_summary #> # A tibble: 3 x 4 #> Species n mean_sl sd_sl #> <fct> <int> <dbl> <dbl> #> 1 setosa 50 5.01 0.352 #> 2 versicolor 50 5.94 0.516 #> 3 virginica 50 6.59 0.636

Explanation: Same group_by + summarise pattern as the mtcars example. Once you have the pipeline template memorized, every grouped-descriptives analysis becomes a two-minute task.

Practice Exercises

Exercise 1: Replicate an SPSS descriptive workflow

Filter mtcars to cars with automatic transmission (am == 0), group by cyl, and compute the mean and SD of horsepower (hp). Save the result to my_summary.

RExercise: horsepower by cylinder

# Exercise: automatic cars only, grouped hp summary # Hint: filter() then group_by() then summarise() my_summary <- NA # replace NA with your pipeline my_summary

Click to reveal solution

RHorsepower-summary solution

my_summary <- mtcars |> filter(am == 0) |> group_by(cyl) |> summarise( n = n(), mean_hp = mean(hp), sd_hp = sd(hp) ) my_summary #> # A tibble: 3 x 4 #> cyl n mean_hp sd_hp #> <dbl> <int> <dbl> <dbl> #> 1 4 3 84.7 19.7 #> 2 6 4 115.2 9.2 #> 3 8 12 194.2 33.4

Explanation: Chain three verbs: filter() to keep automatic cars, group_by() to split by cylinders, and summarise() to compute the two statistics. The entire workflow is one top-to-bottom pipeline.

Exercise 2: T-test with a hand-computed effect size

Run an independent-samples t-test on iris comparing Petal.Width between versicolor and virginica. Then compute Cohen's d manually as the mean difference divided by the pooled standard deviation.

RExercise: t-test plus Cohen's d

# Exercise: t-test + manual Cohen's d # Hint: pooled_sd = sqrt((sd1^2 + sd2^2) / 2) works when groups are similar size petal_sub <- iris[iris$Species %in% c("versicolor", "virginica"), ] petal_t <- NA # replace NA with your t.test() call cohens_d <- NA # replace NA with (mean_diff) / pooled_sd cohens_d

Click to reveal solution

RCohen's-d solution

petal_sub <- iris[iris$Species %in% c("versicolor", "virginica"), ] petal_t <- t.test(Petal.Width ~ Species, data = petal_sub) petal_t #> Welch Two Sample t-test #> t = -14.625, df = 89.043, p-value < 2.2e-16 #> mean in group versicolor mean in group virginica #> 1.326 2.026 sd1 <- sd(petal_sub$Petal.Width[petal_sub$Species == "versicolor"]) sd2 <- sd(petal_sub$Petal.Width[petal_sub$Species == "virginica"]) pooled_sd <- sqrt((sd1^2 + sd2^2) / 2) cohens_d <- (mean(petal_sub$Petal.Width[petal_sub$Species == "virginica"]) - mean(petal_sub$Petal.Width[petal_sub$Species == "versicolor"])) / pooled_sd cohens_d #> [1] 2.124

Explanation: A Cohen's d above 0.8 is a large effect; 2.12 is enormous, matching the very small p-value from the t-test. The effectsize package does this in one call once you have it installed.

Exercise 3: Programmatic ANOVA extraction

Fit a one-way ANOVA of mpg across factor(cyl) on mtcars, extract the F-statistic and p-value programmatically (not by reading them off the printed summary), and print a single formatted string.

RExercise: extract F and p from aov

# Exercise: extract F and p from an aov object # Hint: summary(fit)[[1]] is a data.frame with columns "F value" and "Pr(>F)" aov_fit <- NA # replace NA with your aov() call f_stat <- NA # replace NA with the F-statistic extraction p_val <- NA # replace NA with the p-value extraction cat("ANOVA F =", f_stat, ", p =", p_val, "\n")

Click to reveal solution

RExtract-F-and-p solution

aov_fit <- aov(mpg ~ factor(cyl), data = mtcars) aov_summary <- summary(aov_fit)[[1]] f_stat <- aov_summary[["F value"]][1] p_val <- aov_summary[["Pr(>F)"]][1] cat("ANOVA F =", round(f_stat, 2), ", p =", signif(p_val, 3), "\n") #> ANOVA F = 39.7 , p = 4.98e-09

Explanation: summary(aov_fit) returns a list whose first element is a data frame of ANOVA rows. Indexing by column name gets you the F-statistic and p-value without any screen-scraping, the kind of programmatic access SPSS output files simply do not support.

Complete Example

Here is a full, six-step reproducible SPSS-style analysis written entirely in R: load the data, recode a grouping variable, compute descriptives, run a one-way ANOVA with post-hoc tests, and produce a publication-quality plot. This is the same logical flow a typical .sps syntax file would follow, compressed into one script you can save and re-run anywhere.

RSix-step SPSS-style analysis in R

library(dplyr) library(ggplot2) # Step 1: Load the data (SPSS: File > Open > Data) study <- mtcars # Step 2: Recode cyl as a labelled factor (SPSS: VALUE LABELS) study <- study |> mutate(cyl_group = factor(cyl, levels = c(4, 6, 8), labels = c("small", "medium", "large"))) # Step 3: Descriptives (SPSS: MEANS TABLES = mpg BY cyl_group) study |> group_by(cyl_group) |> summarise(n = n(), mean_mpg = mean(mpg), sd_mpg = sd(mpg)) #> # A tibble: 3 x 4 #> cyl_group n mean_mpg sd_mpg #> <fct> <int> <dbl> <dbl> #> 1 small 11 26.7 4.51 #> 2 medium 7 19.7 1.45 #> 3 large 14 15.1 2.56 # Step 4: One-way ANOVA (SPSS: ONEWAY mpg BY cyl_group) fit <- aov(mpg ~ cyl_group, data = study) summary(fit) #> Df Sum Sq Mean Sq F value Pr(>F) #> cyl_group 2 824.8 412.4 39.70 4.98e-09 *** # Step 5: Post-hoc Tukey (SPSS: Post Hoc > Tukey) TukeyHSD(fit) # Step 6: Publication-ready plot (SPSS: Chart Builder + manual editing) ggplot(study, aes(x = cyl_group, y = mpg, fill = cyl_group)) + geom_boxplot() + labs(title = "Fuel efficiency by engine size", x = NULL, y = "Miles per gallon") + theme_minimal(base_size = 14) + theme(legend.position = "none")

Every step of this workflow, the data load, the recode, the descriptives, the ANOVA, the post-hoc comparisons, and the figure, lives inside a single text file. A collaborator can open that file, press Run, and reproduce your exact results. That is the workflow journals are starting to require, and it is the workflow SPSS has never been able to produce natively.

Summary

Dimension	R	SPSS
Cost	Free	$99/user/month and up
Interface	Code + RStudio / jamovi GUI	Point-and-click + Syntax editor
Reproducibility	Built-in (scripts, R Markdown, Quarto)	Manual (save Syntax files)
Method coverage	Bayesian, SEM, MLM, ML, meta-analysis, more	Core + paid modules
`.sav` file support	`haven::read_sav()`	Native
Learning curve	4-8 weeks to fluency	Hours to days
Career transferability	High (industry + academia)	Academia + some government
Community	~21,000 CRAN packages, active dev	Slower release cadence

The bottom line: if you pay for your own SPSS licence, need methods beyond the SPSS Base module, or want work that reviewers can re-run, switch. If you only run basic tests on small datasets through an institutional licence, the switch still pays off, just on a longer horizon.

References

R Core Team, The R Project for Statistical Computing. r-project.org
Wickham, H., Miller, E., Smith, D., haven: Import and Export SPSS, Stata and SAS Files. haven.tidyverse.org
The jamovi project, jamovi: Free and open statistical software. jamovi.org
Selker, R., Love, J., Dropmann, D., Moreno, V., jmv: The jamovi Analyses. CRAN
Wickham, H. & Grolemund, G., R for Data Science (2nd ed). r4ds.hadley.nz
IBM, SPSS Statistics pricing and editions. ibm.com/products/spss-statistics/pricing
Muenchen, R., The Popularity of Data Science Software. r4stats.com
Wickham, H., ggplot2: Elegant Graphics for Data Analysis (3e). ggplot2-book.org

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R vs SPSS: Why 40% of SPSS Users Are Moving to R (And How to Join Them)

Why are researchers actually switching from SPSS to R?

How much does SPSS really cost compared to R?

Can R handle every statistical test SPSS does?

How do you read SPSS .sav files and convert SPSS commands to R?

Is R worth the learning curve if you come from SPSS?

Practice Exercises

Exercise 1: Replicate an SPSS descriptive workflow

Exercise 2: T-test with a hand-computed effect size

Exercise 3: Programmatic ANOVA extraction

Complete Example

Summary

References

Continue Learning

Related Tutorials