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Course Outline

Writing Cleaner and More Reusable R Code

  • Reviewing the factors that make R code scalable, readable, and maintainable.
  • Creating reusable functions with clear inputs, outputs, and default values.
  • Reducing repetition through improved function design and script organization.

Practical Data Transformation Workflows

  • Building clear analysis pipelines using 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

  • Identifying and fixing common coding errors in scripts and functions.
  • Utilizing practical debugging techniques in R and RStudio.
  • Benchmarking slow code and implementing 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

  • A solid understanding of core R syntax, data types, vectors, and data frames.
  • Experience writing scripts in R and working within RStudio.
  • Intermediate R programming experience, including basic data manipulation and plotting.

Audience

  • Data analysts seeking to write more efficient, reusable, and maintainable R code.
  • Data scientists requiring stronger workflows for analysis, reporting, and collaboration.
  • Researchers and technical professionals who utilize R for practical data work.
 14 Hours

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