Sales Trends Over Time


Author Gender Distribution

Top Genres by Sales

Top Publishers

Top Selling Books

Filters


Comparisons

Tip: The Publication Year Range slider in Filters applies to both the Books list and the Comparisons below.

Books

Author & Gender Analysis

Comprehensive analysis of author performance, gender disparities, and career metrics.

Analysis Controls

to

Hint: try 'cloth', 'paper'

Analysis Results

Key Insights

Primary Visualization

Comparative Analysis

Author Network Analysis

Explore relationships between authors based on shared publishers, publication periods, and collaboration patterns using the new author_id field.


Network Controls

Network Statistics

Author Network

Author Details

Royalty Structure Analysis

Analyze royalty rates, tier structures, and payment schemes across different books, authors, and publishers.


Analysis Controls

Filtering for books with sliding scale royalty structures only.

Summary Statistics

Royalty Analysis

Royalty Tier Details

Book Royalty Details

Royalty Income Query

Calculate royalty income from book sales within a specified date range. Choose between book-specific queries or author-wide summaries.

Query Type

Query Parameters

Select the date range for sales data to include in the royalty calculation.
Search and select a specific book title.
Choose a specific binding type or leave blank for all bindings. Click × to clear selection.
Search and select an author surname.
Optional: Select specific author ID if multiple exist.



Royalty Income Results


Author Summary Statistics

Book Royalty Analysis


Calculation Details


Formula:
Book Royalty Income = Sales Count × Retail Price × Royalty Rate
Author Total Royalty = sum over books of (Sales × Retail Price × Royalty Rate)

Note: For books with complex royalty tiers, different rates may apply to different sales volumes.

How to Use

Step-by-Step Instructions:

  1. Select the date range for your query using the slider
  2. Search and select a book title from the dropdown
  3. Optionally choose a specific binding state, or leave blank to see all formats (use × to clear selection)
  4. Click 'Calculate Royalty Income' to run the query
  5. View the results in the table below
  6. Download the results as CSV if needed

Understanding the Results:

  • Total Sales: Number of copies sold in the specified date range
  • Retail Price: Price per copy at retail
  • Royalty Income: Total royalty earned (Sales × Price × Rate)

Genre & Market Analysis

Comprehensive analysis of literary genre trends, market dynamics, and publishing patterns.

Analysis Controls

to



Choose the main category to analyze.
Optionally split bars by gender.
to
to

Analysis Results

Market Insights

Primary Visualization

Market Trends

Supported By

About This Dashboard

This dashboard enables interactive exploration of a "Database of American Authorship, 1860-1920," drawing upon publishing and sales data from major American publishing houses during the transformative period of the late 19th and early 20th centuries.


Wherever possible, the database includes actual sales figures (or informed estimates) and royalty information for a broad sample of authors and works, allowing for a much more accurate reconstruction of the literary marketplace and facilitating comparative analyses across specific periods and according to variables such as authorship gender, literary genre, binding states, and retail price.


This dynamic, accessible data dashboard aggregates and visualizes economic data for American authorship, from book sales to authorial earnings. This resource will provide a robust foundation for scholars to investigate trends and disparities in the literary marketplace across variables like gender, genre, and historical periods. With this data, researchers will be able to perform longitudinal analyses that reveal shifts over time, offering a valuable perspective on the socio-economic landscape of American literature during this era.


Existing scholarship on this period has often relied on anecdotal evidence from publishers' memoirs and "official" house histories, almost all lacking comprehensive quantitative data. This project, however, moves beyond these sources, building a database grounded in empirical evidence derived from prominent publishers' archives.

Acknowledgments

Shortly after the inception of this project, two research assistants helped code data from the primary sources: Stephen Szaraz (at Harvard) and Matthew Inman (at Penn State). Steve Maczuga at Penn State constructed a preliminary version of the database. As platforms evolved, Mason Slingerland converted the original data files into a form now compatible with Microsoft Excel. Since then, their work has been enhanced by new design possibilities incorporated by other Penn State affiliates: Jennifer Isasi, Nick Mclean, and Siyang Ni. Funding to support this research has come from the College of the Liberal Arts and the Center for the Study of Data Analytics at Penn State.

Project Information

Principal Investigator:
Dr. Michael Anesko
Penn State University

Data Period:
1860-1920

Total Records:
627 books, 27,771 sales records

Version:
1.0.0

Last Updated:
February 22, 2026

Technical Details

Built with:

  • R Shiny
  • PostgreSQL Database
  • Interactive Plotly Charts
  • Responsive Bootstrap UI

For technical support or questions about this dashboard, please contact the development team.