Production checklist

Shipping R in production means pinned packages (renv), scripted pipelines—not ad hoc notebook clicks—logging, tests with testthat, and clear handoffs from SQL extracts.

Checklist

• renv.lock committed; no manual library installs on servers
• Rscript entry points with main() functions
• testthat for critical transforms
• Document cohort SQL and R preprocessing
• Static reports rendered in CI; Shiny behind auth if interactive

Track summary

You covered R fundamentals: vectors, factors, data frames, missing data, visualization concepts (base + ggplot2 locally), statistics, modeling, packages, reproducibility, and interview context. Continue with local tidyverse projects and explore Data Science for SQL + R/Python workflows—or compare patterns with the Python track.

Next steps

1. Create a renv project and knit an R Markdown report locally
2. Practice with a CSV export queried via SQL
3. Build ggplot2 dashboards locally; try Shiny for interactivity
4. Review CRAN task views for your domain (ClinicalTrials, Econometrics, etc.)

Important interview questions and answers

1. Q: renv vs global install?
   A: Global libraries drift; renv reproduces exact package versions in CI and prod.
2. Q: Rscript in cron?
   A: Schedule batch reports—log stdout/stderr; alert on failure.

Self-check

1. What file pins R package versions?
2. Why testthat in pipelines?

Tip: Use scripted Rscript pipelines with renv—not click-only notebooks—in production. Next: Data Science.