Advanced Projects
Prerequisites: Level 3 — Advanced
Build these using modeling, tidymodels, Shiny, databases, packages, and the reproducibility practices from Level 3.
1. Predictive Model with tidymodels
Choose a dataset with an outcome to predict (house prices, customer churn).
Split it into training and test sets, build a preprocessing recipe,
compare two models with cross-validation, tune the better one, and report
its performance on the test set. Lock package versions with renv.
2. Interactive Shiny Dashboard
Build a Shiny app that loads data from a database through DBI, lets users filter by date range and category, and shows interactive charts and a summary table. Write tests for the data-processing functions, and deploy it to Posit Connect Cloud or shinyapps.io.
3. An R Package
Package a set of your own helper functions — for example, data-cleaning
utilities — with usethis and devtools: documentation with roxygen2,
tests with testthat, a README with examples, and a GitHub Actions workflow
that runs R CMD check on every push.