Class 8 — Visualizing the Effect

Turn your HW3 summary table into a picture using ggplot2 to create a figure layer by layer, then adapt the code to your own data and share it in a gallery walk.

Last class you turned your messy pilot file into a clean condition-level table. Today that table becomes something a reader can understand in two seconds. Working incrementally with ggplot() and aes(), you’ll build a labeled figure layer by layer on instructor sample data, try a few variations, then adapt the same code to your own HW3 summary table and share it with classmates in a gallery walk.

Reading due: Data Skills ch. 3 · Due the night before (Mon 10/26, 11:59 PM): HW3

What you’ll be able to do

1. Map variables to visual properties using ggplot() and aes() (x, y, and optionally fill/color), building on the tibble/column fluency from Classes 6–7.
2. Build a single clean, labeled figure — a boxplot or a bar chart with error bars — showing a condition-level RT (or accuracy) difference.
3. Explain what a “layer” is in ggplot2’s grammar of graphics and add at least one layer beyond the base geom (labels, a second geom, or a theme).
4. Adapt working plotting code to a new dataset by swapping in your own column names, applying it to your own HW3-wrangled summary table rather than instructor sample data.
5. Give and receive brief, specific peer feedback on a finished data visualization.