R for Analysis

Produce R analysis code that another analyst can re-run a year later and get the same answer. The costly mistake this prevents: interactive one-off scripts with silent type inference, implicit grouping, and unseeded randomness - code that works once on one laptop and never again.

Inputs to collect

Before writing code, establish:

1. The data source and its shape - file, database, or in-memory; roughly how many rows (millions of rows change the tool choice: consider data.table or duckdb backends past ~10M rows).
2. The analytical question in one sentence. If the user cannot state it, help them state it first - code without a question produces plots without a point.
3. The output target - exploratory notebook, Quarto report, or a table/figure for a deck. This decides how much polish the code needs.

Label any assumption about column meanings as a guess and confirm against a glimpse() of the real data.

Operating procedure

Work in this order - each step protects the ones after it.

Step 1: Reproducible setup