Programming with dplyr

Editor's Take

While `dplyr` makes data manipulation in R intuitive, wrapping its functions inside custom functions often triggers unexpected errors due to tidy evaluation. This course demonstrates how to program cleanly within the tidyverse ecosystem.

Who It's For

Geared toward intermediate R programmers who want to write robust, reusable functions built on top of `dplyr` without hitting evaluation roadblocks.

Key Takeaways

• Resolve non-standard evaluation issues using the rlang package and tidy evaluation principles.
• Write custom functions that wrap core `dplyr` verbs seamlessly.
• Analyze real-world global macroeconomic datasets from the World Bank and IMF.