Class 6 — Welcome to RStudio & the Tidyverse

Part II
Quiz 1 opens the class, then Python and PsychoPy give way to R — read your own pilot data into RStudio as a tibble and compare its shape to a published Stroop dataset.
Published

October 13, 2026

PART II · CLASS 6

This is the Part I → Part II hinge: Python and PsychoPy built the experiment and collected the data, and starting today R and RStudio analyze it. Quiz 1 (covering classes 1–5) opens the class before any new material. You’ll then tour RStudio’s panes and Projects, read your own HW3 pilot CSV in with readr, and compare its shape to a worked published Stroop dataset — diagnosing what’s messy about your file without fixing it yet.

Reading due: Data Skills ch. 1–2 · Due today: HW3 due · Quiz 1

What you’ll be able to do

  1. Navigate RStudio’s four panes (Console, Environment/History, Files/Plots/Packages/Help/Viewer, Source) and explain what an RStudio Project is for.
  2. Read a .csv file into R as a tibble using readr::read_csv(), and inspect it with glimpse() and head().
  3. Define “tidy data” (one row per observation, one column per variable) and identify messy vs. tidy features in your own HW3 pilot file.
  4. Compare your own Stroop-style pilot data’s structure to a worked published Stroop dataset, recognizing the same underlying shape (condition, RT, accuracy) in both.
  5. Recall, via Quiz 1, core Think Python vocabulary and PsychoPy Builder mechanics (routines, conditions files, loops, custom code components, the minimum-observations rule) from Part I.
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