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Course Outline
Developing Efficient and Modular R Code
- Evaluating the characteristics that support scalable, interpretable, and maintainable R scripts
- Constructing reusable functions with well-defined inputs, outputs, and default parameters
- Mitigating redundancy through optimized function design and structured script architecture
Streamlined Data Transformation Processes
- Establishing transparent analytical pipelines utilizing tidyverse methodologies
- Executing grouped aggregations, data joins, and structural reshaping operations
- Organizing data preparation sequences to ensure analytical reproducibility
Functional Programming for Automated Tasks
- Leveraging iteration tools to reduce reliance on traditional looping structures
- Implementing vectorized workflows via the purrr package
- Enhancing error handling and missing value management in repetitive processes
Troubleshooting and Performance Optimization
- Identifying and resolving common script-level and functional errors
- Employing effective debugging strategies within the R environment and RStudio
- Evaluating computational bottlenecks to implement targeted performance enhancements
Reproducible Reporting and Stakeholder Communication
- Generating dynamic, reproducible reports using R Markdown
- Refining graphical outputs with ggplot2 to facilitate clearer insights
- Preparing analytical findings for dissemination to internal or external stakeholders
Applied Exercise and Forward Planning
- Synthesizing function creation, data workflows, debugging, and reporting in a capstone exercise
- Evaluating core methodologies and standard patterns applicable to routine R operations
- Defining actionable steps for continued professional development in R programming
Requirements
- Comprehensive knowledge of fundamental R syntax, data types, vectors, and data frames
- Practical experience developing scripts in R within the RStudio environment
- Intermediate-level proficiency in R programming, encompassing essential data manipulation and visualization techniques
Target Audience
- Data analysts seeking to enhance the efficiency, reusability, and maintainability of their code for government applications
- Data scientists aiming to strengthen analytical workflows, reporting capabilities, and collaborative processes
- Researchers and technical professionals utilizing R for applied data analysis
14 Hours
Testimonials (1)
The flexible and friendly style. Learning exactly what was useful and relevant for me.
