Introduction: Learning the Basics of R
What is R?
R is a free, open-source programming language and environment designed for statistical computing and graphics. Originally created in 1995, R has grown into one of the most widely used languages in data science, biostatistics, and scientific research worldwide.
Why Learn R?
In the context of cancer informatics, R offers several critical advantages:
- Statistical Power: Built from the ground up for statistical analysis, making it ideal for analyzing clinical trial data and cancer research datasets
- Visualization: Create publication-quality figures and interactive visualizations essential for communicating research findings
- Reproducibility: Write scripts that document your entire analysis workflow, ensuring results can be verified and reproduced
- Community and Resources: Vast ecosystem of packages (over 20,000) created by researchers, including specialized bioinformatics tools
- Integration with Other Tools: Works seamlessly with databases, other programming languages, and scientific computing platforms
- Industry Standard: Widely used in pharmaceutical companies, research institutions, and healthcare analytics
What You'll Learn
This course covers the fundamental concepts you need to perform data analysis in cancer research:
- R Basics: Variables, data types, operators, and fundamental programming concepts
- Data Structures: Vectors, matrices, lists, and data frames - the containers for your data
- Data Manipulation: Filtering, transforming, and organizing cancer datasets for analysis
- Visualization: Creating meaningful plots and figures from complex medical data
- Statistical Analysis: Hypothesis testing, correlation, and regression analysis
- Advanced Topics: Unsupervised learning (clustering), supervised machine learning, and survival analysis
How This Course Is Structured
Each lesson builds upon the previous one, starting with basic syntax and gradually advancing to real-world cancer informatics applications:
- Foundational Chapters teach core R concepts and data manipulation skills
- Applied Chapters demonstrate these skills using actual cancer datasets (breast cancer, ovarian cancer, cervical cancer)
- Hands-on Examples include real code that you can run and experiment with
Prerequisites
This course assumes you have:
- A computer able to run R and RStudio
- Basic comfort with using a computer and navigating files
- An interest in data analysis and cancer research
No prior programming experience is required — we'll start from the very beginning.
What You'll Be Able to Do
By the end of this course, you will be able to:
- Load and explore cancer datasets
- Clean and prepare data for analysis
- Create compelling visualizations
- Perform statistical tests relevant to clinical research
- Conduct exploratory data analysis on genomic and clinical data
- Apply machine learning techniques to predict cancer outcomes
- Write reproducible, well-organized analysis scripts
Getting Started
Each lesson includes:
- Explanations of concepts with examples
- Code samples you can copy and run
- Datasets to practice with (many using real cancer research data)
- Exercises to reinforce what you've learned
Take your time with each section. Programming is a skill best learned by doing, so don't just read the code—run it, modify it, and experiment.
Ready to begin? Let's start with installing R.