Exercises

Norton Anthology & SPL Checkouts Data Exploration

These exercises explore checkout data from the Seattle Public Library for authors in the post-1945 volumes of the Norton Anthology of American Literature, one of the most widely used literary anthologies in U.S. college classrooms. The dataset was compiled as part of the research paper “The Canon in Circulation” (Gupta et al., 2025). The code and data are available on GitHub, and you can explore more of the analysis here. For more context, see the Library Checkouts for the Top 500 “Greatest” Novels data essay.

Concepts covered:

Groupby and aggregation (sum of checkouts)
Sorting and ranking (top N values)
Horizontal bar charts
Filtering by a specific author
Time series line plots (monthly checkouts over time)

Load the data

Code

library(dplyr)
library(ggplot2)

norton_df <- read.csv("https://seattle-library-checkout-data.s3.us-west-2.amazonaws.com/norton-anthology_spl-checkouts_2005-2025.csv",
  stringsAsFactors = FALSE)
head(norton_df)

Exercise 1

Find the top 10 authors by total checkouts in the Norton Anthology SPL checkouts dataset. Create a horizontal bar chart to visualize the results.

Save the top authors as top_authors.

Code

# Your code here

Discuss/consider: Which authors from the Norton Anthology are most popular at the Seattle Public Library? Are these authors you expected to see at the top?

Exercise 2

Find the top 10 titles by total checkouts. Create a horizontal bar chart to visualize the results.

Save the top titles as top_titles.

Code

# Your code here

Discuss/consider: Which titles are checked out most frequently? How do these compare to the most popular authors?

Exercise 3

Create a time series line plot showing monthly checkouts for Octavia E. Butler over time.

Filter the data for Octavia E. Butler, group by year and month, and plot the results.

Code

# Your code here

Discuss/consider: What patterns do you notice in the checkout trends for Octavia E. Butler? Are there any notable spikes or dips? What might explain them?

--- title: "Norton Anthology & SPL Checkouts Data Exploration (Exercise)" date: "2026-02-25" categories: [dplyr, exercise] format: html: default code-overflow: wrap code-fold: show editor: visual df-print: kable R.options: warn: false code-tools: true execute: eval: false --- # <span style="color:green;"> Exercises </span> ## Norton Anthology & SPL Checkouts Data Exploration <span style="color:red;"> [Solutions](Norton-Anthology-SPL-Checkouts-Solutions.qmd) </span> These exercises explore checkout data from the Seattle Public Library for authors in the post-1945 volumes of the *Norton Anthology of American Literature*, one of the most widely used literary anthologies in U.S. college classrooms. The dataset was compiled as part of the research paper ["The Canon in Circulation"](https://doi.org/10.63744/P6qPH135jhY2) (Gupta et al., 2025). The code and data are available on [GitHub](https://github.com/melaniewalsh/Canon-in-Circulation), and you can explore more of the analysis [here](https://melaniewalsh.github.io/whats-seattle-reading/posts/norton-anthology-american/). For more context, see the [Library Checkouts for the Top 500 "Greatest" Novels data essay](../index.qmd). **Concepts covered:** - Groupby and aggregation (sum of checkouts) - Sorting and ranking (top N values) - Horizontal bar charts - Filtering by a specific author - Time series line plots (monthly checkouts over time) # Load the data ```{r} #| message: false library(dplyr) library(ggplot2) norton_df <- read.csv("https://seattle-library-checkout-data.s3.us-west-2.amazonaws.com/norton-anthology_spl-checkouts_2005-2025.csv", stringsAsFactors = FALSE) head(norton_df) ``` # Exercise 1 Find the top 10 authors by total checkouts in the Norton Anthology SPL checkouts dataset. Create a horizontal bar chart to visualize the results. Save the top authors as `top_authors`. ```{r} # Your code here ``` Discuss/consider: Which authors from the Norton Anthology are most popular at the Seattle Public Library? Are these authors you expected to see at the top? # Exercise 2 Find the top 10 titles by total checkouts. Create a horizontal bar chart to visualize the results. Save the top titles as `top_titles`. ```{r} # Your code here ``` Discuss/consider: Which titles are checked out most frequently? How do these compare to the most popular authors? # Exercise 3 Create a time series line plot showing monthly checkouts for Octavia E. Butler over time. Filter the data for Octavia E. Butler, group by year and month, and plot the results. ```{r} # Your code here ``` Discuss/consider: What patterns do you notice in the checkout trends for Octavia E. Butler? Are there any notable spikes or dips? What might explain them?