Reproducibility is the cornerstone of trustworthy data science. A fully reproducible workflow lets you regenerate analyses on demand, share results with colleagues, and ensure that your findings stand the test of time. In this post, we’ll explore three pillars of reproducible R workflows: R Markdown for dynamic, multi-format reports (HTML, PDF, Word) Git and GitHub for version control and collaboration Building R packages with devtools to package and distribute your code By the end, you’ll have a blueprint for structuring projects that are transparent, maintainable, and easily shareable. 1. R Markdown for Dynamic Reporting R Markdown bridges code and narrative in a single document. It weaves together prose, executable R chunks, and output—tables, plots, and model summaries—into one coherent report. You can render to HTML, PDF, and Word with a single command. 1.1 Creating an R Markdown Document In RStudio: File → New File → R Markdown… Enter a title, author, and default output fo...
Functional programming elevates your R code from one‐off scripts to robust, maintainable workflows. In this post, you’ll learn how to: Apply purrr ’s mapping functions ( map() , map_df() , map2() , and pmap() ) for clean iteration Write custom R functions with clear inputs, outputs, and documentation Handle errors gracefully using tryCatch() and purrr’s safe variants Debug aggressively with browser() , traceback() , and related tools By mastering these patterns and utilities, you’ll build code that scales, adapts, and surprises you with its reliability. 1. Mapping with purrr Instead of writing explicit for loops, purrr promotes a declarative style where you “map” functions over lists or vectors. This yields shorter, more readable code. r install.packages("purrr") library(purrr) 1.1 map(): Apply a function to each element r # Square each number in a vector nums <- list(1, 2, 3, 4, 5) squares <- map(nums, ~ .x ^ 2) # list(1, 4, 9, 16, 25) map() always returns a list...