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Course Outline
Writing Cleaner, More Reusable R Code
- Examining the principles that make R code scalable, readable, and maintainable.
- Building reusable functions with well-defined inputs, outputs, and default values.
- Minimizing repetition through improved function design and organized scripting.
Practical Data Transformation Workflows
- Constructing clear analysis pipelines using tidyverse tools.
- Performing grouped summaries, joins, and data reshaping operations.
- Organizing data preparation steps to ensure repeatable analysis.
Functional Programming for Repetitive Tasks
- Utilizing iteration tools as a robust alternative to traditional loops.
- Implementing map-style workflows with the purrr package.
- Safely managing errors and missing values in repeated processes.
Debugging and Performance Optimization
- Identifying and resolving common coding errors in scripts and functions.
- Applying practical debugging techniques within R and RStudio.
- Profiling slow code and executing targeted performance enhancements.
Reproducible Reporting and Communication
- Generating reproducible reports using R Markdown.
- Refining visual outputs with ggplot2 to enhance message clarity.
- Preparing analysis results for effective sharing with business and research stakeholders.
Applied Workshop and Future Steps
- Integrating functions, data workflows, debugging, and reporting in a comprehensive practical exercise.
- Reviewing essential techniques and common patterns for daily R usage.
- Identifying pathways for continued growth in R programming skills.
Requirements
- A solid grasp of core R syntax, data types, vectors, and data frames.
- Experience in writing R scripts and navigating the RStudio interface.
- Intermediate R programming skills, including basic data manipulation and plotting.
Target Audience
- Data analysts seeking to produce more efficient, reusable, and maintainable R code.
- Data scientists requiring robust workflows for analysis, reporting, and collaboration.
- Researchers and technical professionals utilizing R for practical data-driven tasks.
14 Hours
Testimonials (1)
The flexible and friendly style. Learning exactly what was useful and relevant for me.
