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Course Outline
Writing Cleaner and More Reusable R Code
- Reviewing what makes R code scalable, readable, and maintainable
- Creating reusable functions with clear inputs, outputs, and defaults
- Reducing repetition through better function design and script organization
Practical Data Transformation Workflows
- Building clear analysis pipelines with tidyverse tools
- Working with grouped summaries, joins, and reshaping data
- Structuring data preparation steps for repeatable analysis
Functional Programming for Repeated Tasks
- Using iteration tools as an alternative to repetitive loops
- Applying map-style workflows with purrr
- Handling errors and missing values more safely in repeated tasks
Debugging and Improving Performance
- Finding and fixing common coding errors in scripts and functions
- Using practical debugging techniques in R and RStudio
- Benchmarking slow code and making targeted performance improvements
Reproducible Reporting and Communication
- Creating reproducible reports with R Markdown
- Refining visual output with ggplot2 for clearer communication
- Preparing analysis results for sharing with business or research stakeholders
Applied Workshop and Next Steps
- Combining functions, data workflows, debugging, and reporting in a practical exercise
- Reviewing key techniques and common patterns for day-to-day R work
- Identifying next steps for continued improvement in R programming
Requirements
- Solid understanding of core R syntax, data types, vectors, and data frames
- Experience writing scripts in R and working in RStudio
- Intermediate R programming experience, including basic data manipulation and plotting
Audience
- Data analysts who want to write more efficient, reusable, and maintainable R code
- Data scientists who need stronger workflows for analysis, reporting, and collaboration
- Researchers and technical professionals who use R for practical data work
14 Hours
Testimonials (1)
The flexible and friendly style. Learning exactly what was useful and relevant for me.
