Visualization Practice: Make a practice visualization

1. 1. Load in the diamonds dataset. This activity uses the same one the recitation code uses — a 1,000-row sample of ggplot2::diamonds:
   diamonds = ggplot2::diamonds %>% sample_n(1000)
   (load library(ggplot2) and library(dplyr) first). If you would rather read a file, workshops/mydiamonds.csv in the course repository is a small 3-column alternative (price, carat, cut).
2. 2. Pick 1-2 variables.
3. 3. Make a histogram, scatterplot, or boxplot of them.
4. 4. Map ~2 aesthetics and ~2 static attributes. An aesthetic is written inside the aes(...) phrase and names a vector in the data, so it varies by row; a static attribute is written outside aes() as a constant, so it applies uniformly to every point.
5. 5. Submit your visual and code to CANVAS!
6. 6. Be prepared to talk about your approach!

R track: Visualization with ggplot in R — the aesthetic-vs-static-attribute question is Learning Check 3. Python track: Visualization with plotnine in Python.

Submit your visual and code to CANVAS (1 pt) — graded on completion.