Day 1 — Foundations, Data Acquisition & the AI Toolkit

Monday 31 August · 09:30–16:30

Where the day goes

You arrive with no R. You leave having read a .sav, a .dta, an .xlsx and a live API series, cleaned the column names, reshaped the frame, and driven part of it with an AI assistant you checked rather than trusted.

Sessions

Time Session Material
09:30–10:00 Opening, roadmap, quick poll 00-orientation.qmd
10:00–11:15 R and Positron fundamentals — objects, types, functions, projects, here::here() 01-r-fundamentals.qmd · scripts/r-fundamentals.R · 02-rstudio-git-session.qmd
11:30–13:00 Data acquisition beyond clean downloads — CSV/Excel, haven labels, APIs, pdftools, rvest 03-data-import-export.qmd · scripts/import-alifailan.R · scripts/import-alifailan-part2.R
14:00–15:15 AI toolkit + AI-assisted cleaning — Positron Assistant, Copilot, chattr, gander AI prompt library · scripts/clean-dirty-excel.R
15:30–16:30 AI-assisted wrangling — filter, case_when(), .by=, join_by(), pivots 04-dplyr-wrangling-floods.qmd · scripts/wide-to-long.R

Take home

Scripts in this folder

Script What it does
r-fundamentals.R first script for new users, on the district MPI 2019-20 file
import-alifailan.R reading each of the four common file formats
import-alifailan-part2.R the follow-on cleaning and recoding pass
clean-dirty-excel.R janitor::clean_names() and friends on a real messy sheet
wide-to-long.R pivot_longer() / pivot_wider()
create-practice-data.R generates the synthetic practice dataset
data-packages.R, data-packages-fundamentals.R the package ecosystem, oriented

By 16:30 you should be able to

  • open a project and explain why here::here() beats setwd()
  • read a labelled .sav file and keep the labels
  • rename 40 columns in one line
  • describe what is missing in a dataset before analysing it
  • write a prompt that produces cleaning code, and name two ways to check that code
  • make one commit and push it
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