EEB330 – Precept 06: Plotting

GitHub Classroom Assignment

• Check out the cheat sheet for ggplot2: Data Visualization with ggplot2: CHEAT SHEET
• Similarly for matplotlib: Matplotlib for Beginners: CHEAT SHEET
• Choose one language (R or Python) to complete the exercises, but complete the first exercise in the opposite language. Submit separate files for each language.
• Deadline is back to one week from the original precept date.
• Please save each plot as an image (.png or .jpg) and upload them to your GitHub repository (4 images total).

# R
library(pak)
packages <- c("ggplot2", "cowplot", "ggthemes", "patchwork")
pak::pkg_install(packages)

lapply(packages, require, character.only = TRUE)

# Python
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

mpg = pd.read_csv("mpg.csv")

Exercises

Exercise 1 – Bar Plot Modification

• Task:
  • Add a title to the plot: “Distribution of Cars by Class”.
  • Change the x-axis label to “Type of Car”.
  • Color the bars in blue.
  • Rotate the x-axis labels by 45 degrees.
• Expected Output: An updated plot with the above specifications.

Initial Plot: A simple bar plot displaying the number of cars for each class in the mpg dataset.

# R
ggplot(data = mpg, aes(x = class)) +
  geom_bar()

# Python
sorted_mpg = mpg['class'].value_counts().sort_index()
plt.bar(sorted_mpg.index, sorted_mpg.values)

Exercise 2 – Histogram Modification

• Task:
  • Add a title to the plot: “Highway Mileage Distribution”.
  • Change the x-axis label to “Miles Per Gallon”.
  • Fill the histogram bars with green but have a black border.
  • Set the bin width to 2.
• Expected Output: An updated plot with the above specifications.

Initial Plot: A histogram showcasing the distribution of highway miles per gallon (hwy) from the mpg dataset.

# R
ggplot(data = mpg, aes(x = hwy)) + 
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Python
plt.hist(mpg['hwy'], bins = 30)

Exercise 3 – Scatter Plot with Facets

• Task:
  • Add a title: “Engine Displacement vs. Highway MPG”.
  • Change the x-axis label to “Engine Size (liters)” and y-axis label to “Highway MPG”.
  • Color the points by class and shape them by the type of drive (e.g., 4wd, fwd, rwd).
  • Add a smooth trend line (with standard error or confidence interval) to the plot. Consider adjusting the alpha of the points for clarity.
  • Facet the plot by cyl (number of cylinders) in a 2x2 grid format.
• Expected Output: An updated plot with the above specifications.

Initial Plot: A scatter plot illustrating the relationship between engine displacement (displ) and highway MPG (hwy).

# R
ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point()

# Python
sns.scatterplot(data = mpg, x = 'displ', y = 'hwy')

Exercise 4: Enhanced Boxplots using after_stat() and patchwork

• Task:

1. Modify plot1:
   • Color the boxes based on median value of cty using a gradient from light blue (low mpg) to dark blue (high mpg).
   • Add a title: “City MPG by Manufacturer”.
   • Rotate x-axis labels by 90 degrees and adjust their size for readability.
   • Apply a theme of your choice from the ggthemes library (R) or .set_theme() in the Seaborn library (Python).
2. Modify plot2:
   • Color the boxes based on median value of hwy using a gradient from light green (low mpg) to dark green (high mpg).
   • Add a title: “Highway MPG by Manufacturer”.
   • Rotate x-axis labels by 90 degrees and adjust their size for readability.
   • Apply the same theme as plot1.
3. Combine the two modified plots side by side using the patchwork library (R) or subplots (Python).

• Expected Output: A plot with the above specifications.

# R
plot1 <- ggplot(data = mpg, aes(x = manufacturer, y = cty)) +
  geom_boxplot()

plot2 <- ggplot(data = mpg, aes(x = manufacturer, y = hwy)) +
  geom_boxplot()

# Python
plot1 = sns.boxplot(data = mpg, x = 'manufacturer', y = 'cty')

plot2 = sns.boxplot(data = mpg, x = 'manufacturer', y = 'hwy')