This is one of the most searched questions in applied statistics, and the answer is shorter than people expect: they are not alternatives.
R is the programming language. It does the actual computing. RStudio — now published by Posit — is an editor built for writing R. It cannot do anything without R installed underneath it.
The comparison is a bit like asking whether you should use English or a word processor.
Install R first, RStudio second
The order matters. RStudio looks for an R installation when it starts; install it first and it will simply report that it cannot find one.
- Download R from CRAN (cran.r-project.org) and install it.
- Download RStudio Desktop from posit.co and install that.
- Open RStudio. It finds R automatically, and you never open plain R again.
You can run R without RStudio. Almost nobody does, because the plain R console gives you no script editor, no plot pane, no package manager and no project structure.
What RStudio actually adds
- Script editor with autocomplete — you write scripts rather than typing into a console.
- Environment pane — every object currently in memory, visible and inspectable.
- Plot pane and history — plots stay visible and you can step back through them.
- Projects — each analysis gets its own working directory and state, which removes an entire class of "it worked yesterday" problems.
- R Markdown and Quarto — documents that contain code and regenerate their own results, so a table cannot drift out of date with the data behind it.
When R is worth the learning curve
R has a steeper start than SPSS. You type commands rather than clicking menus, and the first week is slower. The question is whether what you get back is worth it for your project.
| Your situation | Reasonable choice |
|---|---|
| Department expects SPSS output, analysis is standard | SPSS |
| Same analysis will be re-run on updated data | R |
| Heavy data cleaning, merging or reshaping | R |
| Method not available in SPSS (advanced SEM, meta-analysis, Bayesian) | R |
| Journal or funder requires reproducible code | R |
| Two weeks until submission, no R experience | SPSS |
| You intend to continue in research | R, learned now |
The packages worth knowing early
tidyverse— dplyr, ggplot2, tidyr and friends. The standard modern toolkit.janitor— cleans messy column names and finds duplicates in one line.psych— descriptives, reliability and factor analysis for social science.lavaan— CFA and structural equation modelling.metafor— meta-analysis, well documented and widely cited.gtsummary— publication-ready summary and regression tables.
A first script that does something useful
This reads a CSV, summarises it by group and plots the result. It is the shape of most analysis scripts you will write.
library(tidyverse)
data <- read_csv("responses.csv")
summary_table <- data |>
group_by(condition) |>
summarise(
n = n(),
mean_score = mean(score, na.rm = TRUE),
sd_score = sd(score, na.rm = TRUE)
)
print(summary_table)
ggplot(data, aes(x = condition, y = score)) +
geom_boxplot() +
labs(x = "Condition", y = "Score") +
theme_minimal()Questions this raises
No. RStudio Desktop is free and open source, and R itself is free. The paid products are server and enterprise editions that individual researchers do not need.
No. Posit is the company name; RStudio remains the product name for the desktop editor and is actively developed.
Yes. The haven package reads .sav files directly and preserves variable and value labels.
Still stuck after reading this? That is usually the point at which it is worth asking someone. Describe your project or ask on WhatsApp.
