Is it worth learning R for one thesis? If your university has no SPSS license, your supervisor uses R, or you expect to keep doing research, the answer is usually yes. Learning R for thesis statistics takes a few weeks of steady practice, and in return you get free software, a script that reproduces every number in your results chapter, and packages for almost any method. This guide covers seven steps from installation to APA tables, with code for the tests most theses need.
This article is part of our guide How to Write a Master’s Thesis by the Mastermind PhD team and relates to our Statistical Analysis service. Sources last checked: November 2026.
What is R? R is a free, open-source language and environment for statistical computing and graphics, maintained by the R Core Team and distributed through CRAN, the Comprehensive R Archive Network. Most users work in RStudio, a free interface from Posit that combines a script editor, console, data viewer and plots in one window.
Why use R for thesis statistics?
| Feature | R | SPSS |
|---|---|---|
| Cost | Free | Paid license |
| Interface | Code (script) | Menus, with optional syntax |
| Reproducibility | Every step saved in the script | Only if you save syntax |
| Range of methods | Very wide, through packages | Wide for standard tests |
| Graphics | Highly customizable (ggplot2) | Standard charts |
| Learning curve | Steeper at the start | Gentler |
The decision depends on your field and supervisor; our comparison of SPSS, R and Python covers it in more depth. If you choose R for thesis statistics, plan for a learning period before your data arrive, not after.
How do you start with R for thesis statistics in 7 steps?
Step 1: Install R and RStudio
Download R from CRAN for your operating system, then install RStudio Desktop from Posit. Open RStudio, not R, from then on.
Step 2: Create a project
In RStudio, choose File > New Project > New Directory. Keep the raw data in a data folder and scripts in a scripts folder. A project sets the working directory automatically, so file paths stay short and portable.
Step 3: Install and load packages
Packages are installed once and loaded in every session:
install.packages(c("tidyverse", "haven", "readxl", "psych", "rstatix", "effectsize"))
library(tidyverse)
Step 4: Import your data
library(haven); df <- read_sav("data/survey.sav") # SPSS file
library(readxl); df <- read_excel("data/survey.xlsx") # Excel file
df <- read_csv("data/survey.csv") # CSV file
The haven package keeps SPSS value labels, which you can convert to factors with as_factor().
Step 5: Clean and prepare
Use dplyr verbs from the tidyverse: filter() to select cases, mutate() to compute scale scores, select() to keep variables. Wickham’s “tidy data” principle (one variable per column, one observation per row) makes every later step easier.
df <- df %>% mutate(burnout = rowMeans(across(b1:b9), na.rm = TRUE))
Step 6: Describe and test
psych::alpha(select(df, b1:b9)) # Cronbach's alpha
psych::describe(df$burnout) # descriptives
t.test(burnout ~ gender, data = df) # Welch t-test
effectsize::cohens_d(burnout ~ gender, data = df)
summary(aov(burnout ~ school_type, data = df)) # one-way ANOVA
cor.test(df$burnout, df$support, method = "pearson")
summary(lm(burnout ~ support + workload, data = df))
These lines cover the tests most survey-based theses need. Each function prints its own output in the console; copy nothing by hand into Word, because the export step below does that reproducibly.
Step 7: Export tables and figures
Use ggplot2 for figures and ggsave() to save them at publication resolution. For tables, packages such as apaTables or flextable export to Word. Keep all of this in the script so that a change in the data reproduces every table.
📱 Ask a statistician about your data — Send a short description of your instrument and sample size on WhatsApp and get a suitable analysis plan. Every analysis comes with the raw SPSS file and a results report.
Which packages matter most for a thesis?
You do not need hundreds of packages. For R for thesis statistics, about ten cover nearly every method in a master’s results chapter:
| Task | Package |
|---|---|
| Import SPSS, Stata, SAS files | haven |
| Data cleaning and graphics | tidyverse (dplyr, ggplot2, tidyr) |
| Reliability, descriptives, factor analysis | psych |
| Common tests with tidy output | rstatix |
| Effect sizes | effectsize |
| Assumption tests such as Levene’s | car |
| Post hoc and estimated marginal means | emmeans |
| Mixed models | lme4 |
| SEM and CFA | lavaan |
How do you check assumptions in R?
Assumption checks are short in R, and the output is easy to paste into an appendix:
| Assumption | Function |
|---|---|
| Normality of a variable or residuals | shapiro.test(), qqnorm() with qqline() |
| Equal variances across groups | car::leveneTest(burnout ~ gender, data = df) |
| Regression diagnostics | plot(model) for residual plots, car::vif(model) for multicollinearity |
| Outliers | boxplot() or rstatix::identify_outliers() |
Write a short comment above each check in the script explaining the decision you took, for example “Levene significant, Welch test reported”. Those comments become the justification sentences in your methodology chapter.
A realistic first-month plan for R for thesis statistics
- Week 1: install R and RStudio, create a project, work through the first chapters of R for Data Science on a practice dataset.
- Week 2: import a copy of your own pilot or dummy data, compute scale scores and descriptive statistics.
- Week 3: run the tests in your analysis plan on the pilot data and compare one or two results with a classmate’s SPSS output.
- Week 4: build the figures and tables in APA format, so the script is ready when the full dataset arrives.
How do R results compare with SPSS?
The numbers match when the same method is used, but defaults differ. R’s t.test() uses the Welch test by default, which does not assume equal variances, while SPSS prints both rows. Type I sums of squares are the default in base R’s aov(), while SPSS uses Type III; in unbalanced designs with several factors, use car::Anova(model, type = 3) with appropriate contrasts. Describe the function and package that produced each result in your methodology chapter. When you use R for thesis statistics alongside a supervisor who uses SPSS, agree these defaults early so your numbers match theirs.
How do you cite R in your thesis?
Run citation() in the console to get the reference for R itself, and citation("lavaan") (or any package name) for each package you used. Report the R version (R.version.string) and key package versions. A sentence such as “Analyses were conducted in R version 4.x using the psych and lavaan packages” is enough in the methodology chapter, with the full references in the bibliography. Many supervisors also ask for the script as an appendix or in a repository, so keep it tidy and commented from the first day.
What mistakes do beginners make?
Most problems with R for thesis statistics come from habits, not from the statistics:
- Working in the console without saving a script.
- Using absolute file paths that break on another computer.
- Forgetting that R is case-sensitive (
Burnoutis notburnout). - Treating labeled SPSS codes as numbers when they should be factors.
- Copying code from the internet without understanding what each argument does, which leaves you unable to explain it at the defense.
📱 Ask on WhatsApp now — 5 years of experience across all disciplines and 14 academic services. Two-stage payment (80/20); revisions are free until supervisor approval, within 30% of the chapter.
Frequently asked questions
How long does it take to learn R for thesis statistics?
Most beginners can import data and run common tests after two to four weeks of regular practice. Complex models take longer, so start before your data collection ends.
Is R accepted by examiners?
Yes. R is widely used in published research across fields. Examiners care that the method is appropriate and correctly reported, not which software produced it.
Can I open my SPSS file in R?
Yes. The haven package reads .sav files with variable and value labels. Check that missing-value codes such as 99 were converted to NA.
Do I need RStudio?
It is not required, but it makes R much easier to use with its script editor, environment pane and plot viewer. Both R and RStudio Desktop are free.
What is the best free book for starting?
*R for Data Science* (2nd edition) by Wickham, Çetinkaya-Rundel and Grolemund is free online and covers importing, cleaning and visualizing data.
Sources
- R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://cran.r-project.org/ (accessed November 2026)
- Posit. RStudio Desktop. https://posit.co/download/rstudio-desktop/ (accessed November 2026)
- Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science (2nd ed.). O’Reilly. https://r4ds.hadley.nz/
- Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23. https://doi.org/10.18637/jss.v059.i10
- Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. SAGE.
Sources last checked: November 2026. This article is general educational guidance and does not replace your university regulations or your supervisor’s instructions. How we prepare our content.