About

About the Statistics & Bioinformatics Tutorial Collection

About This Tutorial Collection

This website provides a comprehensive collection of tutorials covering the most important topics in statistics and bioinformatics. Every tutorial includes practical R code examples that you can reproduce and adapt for your own analyses.

Who Is This For?

These tutorials are designed for:

• Students in biostatistics, bioinformatics, data science, and related fields
• Researchers who need to apply statistical methods in their work
• Practitioners looking to expand their analytical toolkit
• Anyone interested in learning data analysis with R

What You Will Find

The tutorials span a wide range of topics, from foundational statistical theory to advanced bioinformatics workflows:

• Statistical Foundations – core concepts, probability theory, and distributions
• Descriptive Statistics – summarising and exploring data
• Inferential Statistics – hypothesis testing, confidence intervals, and more
• Sample Size & Power – planning studies with adequate statistical power
• Data Visualisation – creating publication-quality graphics with ggplot2
• Regression & Modelling – linear models, GLMs, mixed models, and beyond
• Multivariate Methods – PCA, clustering, factor analysis
• Bayesian Statistics – Bayesian inference and computational methods
• Survival Analysis – time-to-event data analysis
• Time Series – temporal data analysis and forecasting
• Machine Learning – supervised and unsupervised methods in R
• Bioinformatics – genomics, transcriptomics, proteomics, and more
• Clinical Biostatistics – clinical trial methodology and diagnostic testing
• Meta-Analysis – systematic evidence synthesis
• Experimental Design – planning experiments and studies
• Reproducible Research – best practices for reproducible data analysis

Technical Requirements

All tutorials use R as the primary programming language. To follow along, you will need:

• R (version 4.0 or later recommended)
• RStudio or another R IDE
• The specific R packages mentioned in each tutorial

Author

These tutorials are maintained by R. Heller. For more information, visit the main website.

References

The following sources informed the methodological choices, worked examples, and recommendations across the tutorial collection. Full BibTeX entries are available in references.bib.

Books

• Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.
• Agresti, A. (2013). Categorical Data Analysis (3rd ed.). Wiley.
• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum.
• Faraway, J. J. (2015). Linear Models with R (2nd ed.). Chapman and Hall/CRC.
• Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE.
• Fox, J., & Weisberg, S. (2019). An R Companion to Applied Regression (3rd ed.). SAGE.
• Harrell, F. E. (2015). Regression Modeling Strategies (2nd ed.). Springer.
• Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric Statistical Methods (3rd ed.). Wiley.
• Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
• Kaufman, L., & Rousseeuw, P. J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.
• Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing Experiments and Analyzing Data (3rd ed.). Routledge.
• Rousseeuw, P. J., & Leroy, A. M. (2005). Robust Regression and Outlier Detection. Wiley.

Articles

• Agresti, A., & Coull, B. A. (1998). Approximate is better than “exact” for interval estimation of binomial proportions. The American Statistician, 52(2), 119–126.
• Bakeman, R. (2005). Recommended effect size statistics for repeated measures designs. Behavior Research Methods, 37(3), 379–384.
• Bishara, A. J., & Hittner, J. B. (2012). Testing the significance of a correlation with nonnormal data. Psychological Methods, 17(3), 399–417.
• Conover, W. J., Johnson, M. E., & Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances. Technometrics, 23(4), 351–361.
• Delacre, M., Lakens, D., & Leys, C. (2017). Why psychologists should by default use Welch’s t-test. International Review of Social Psychology, 30(1), 92–101.
• Dinno, A. (2015). Nonparametric pairwise multiple comparisons in independent groups using Dunn’s test. The Stata Journal, 15(1), 292–300.
• Divine, G., Norton, H. J., Hunt, R., & Dienemann, J. (2013). A review of analysis and sample size calculation considerations for Wilcoxon tests. Anesthesia and Analgesia, 117(3), 699–710.
• Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
• Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489.
• Gueorguieva, R., & Krystal, J. H. (2004). Move over ANOVA: Progress in analyzing repeated-measures data. Archives of General Psychiatry, 61(3), 310–317.
• Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93.
• Kerby, D. S. (2014). The simple difference formula: An approach to teaching nonparametric correlation. Comprehensive Psychology, 3.
• Newson, R. (2002). Parameters behind “nonparametric” statistics. The Stata Journal, 2(1), 45–64.
• Sharpe, D. (2015). Chi-square test is statistically significant: Now what? Practical Assessment, Research and Evaluation, 20(8).

Software

• Kassambara, A. (2023). rstatix: Pipe-friendly framework for basic statistical tests.
• Patil, I. (2021). Visualizations with statistical details: The ggstatsplot approach. Journal of Open Source Software, 6(61), 3167.
• Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
• Sjoberg, D. D., Whiting, K., Curry, M., Lavery, J. A., & Larmarange, J. (2021). Reproducible summary tables with the gtsummary package. The R Journal, 13(1), 570–580.
• Wickham, H., et al. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686.

Acknowledgements

This tutorial collection builds on the work of the wider R, Quarto, and statistical-computing communities. Particular thanks to:

• The R Core Team and CRAN maintainers for the language and package ecosystem that underpins every example.
• The Posit / Quarto team for the publishing system used to render this site.
• The authors of the tidyverse, rstatix, ggstatsplot, lavaan, gtsummary, and the many Bioconductor packages cited in individual tutorials.
• The textbook authors listed above, whose pedagogy shaped how methods are introduced and compared here.
• Readers and contributors who reported issues, suggested corrections, and proposed new topics through the project’s GitHub repository.

Citation

If you use this tutorial collection in teaching, research, or other written work, please cite it as:

@misc{heller2026tutorials,
  author       = {Heller, R.},
  title        = {{\#}tutorials: Comprehensive Tutorials in Statistics and Bioinformatics with {R}},
  year         = {2026},
  howpublished = {\url{https://cttir.github.io/tutorials/}},
  note         = {CTTIR project}
}