R Functional Programming: across() and map() for Messy Data

The 45-min hands-on session on cleaning messy datasets in R without copy-paste.

Why you need this: Tired of typing `str_remove_all()` for 20 columns? Have 12 NEPA CSVs with the same messy `bill` column? Learn to write the cleaning rule once and apply it to many columns with `across()` and many files with `map()`.

What you’ll do:

1. Write 1 R function to fix `"₦45,000 naira"` → `45000`
2. Use `across()` to clean all price columns in `offa_market.csv` at once
3. Use `map()` to read + clean 12 monthly IBEDC files and stack them into 1 dataset

Who: Offa-R-Users who know `mutate` and `filter`. Never used `across()` or `map()`? This is for you.

Takeaway: Leave with `clean_naira()` function, team `functions.R` template, and code that cleans next month’s data in 5 lines instead of 5 hours.

No loops. No Excel. Just R.