Data Visualization Designer Assessment

This assessment involves reviewing a CORI publication, analyzing the data and graphics used, and creating improved R-based visualizations that tell a more compelling story. It should take approximately 2-3 hours to complete. We want to be respectful of your time and don't expect perfection. If you run into any issues, don't hesitate to reach out!

The Task

For this exercise, review our recent publication on Rural America's Struggle to Access Private Capital. In the material provided, you have access to two high-impact chart graphics (also in docs/ and reproduced below) and their corresponding data (in data/) that were utilized to support the article. Use the data provided to generate R-based graphics that work to enrich the images provided.

Steps

1) Fork this repository and clone it to your local computer

If you'd prefer to keep your assessment private, feel free to clone this repo (rather than fork), create a private repository on GitHub, push your work there, and add us as collaborators when you're ready.

2) Review the publication

Read through Rural America's Struggle to Access Private Capital (which is available in the docs folder of this project) to understand the narrative, key findings, and how the existing graphics support the story.

3) Build improved R graphics

Take the provided data and build improved R graphics that tell a "better" story. The resulting graphics should be built in R. We have provided our organization's style/branding guidelines in the custom built package cori.charts.

# install.packages("devtools")
devtools::install_github("ruralinnovation/cori.charts")

cori.charts contains several useful functions for loading fonts, implementing consistent styling, and exporting graphics:

• load_fonts() loads relevant Google Fonts (Lato) into your local environment.
• theme_cori() applies standardized formatting (consistent margins, fonts, etc.)
• theme_cori_horizontal_bars() extends theme_cori with horizontal bar specific styling.
• theme_cori_map() extends theme_cori with map specific styling.
• save_plot() exports images with a CORI logo and consistent formatting.

Feel free to reference our "cookbook" for chart examples. Save your charts as PNG files in the export/ subfolder using cori.charts::save_plot().

4) Write a narrative for your improved graphics

Following the spirit of the publication, produce a concise but detailed narrative (no longer than 1 page) that tells the story of your improved graphics. Ensure that your story and the graphics meld with the natural flow of the publication. In theory, it should be able to easily replace the current story and graphics.

5) Provide an analysis of your design choices

Apart from the story narrative, provide a short analysis of the improved graphics explaining how they are superior to the previous version (no longer than 2-3 paragraphs). Include:

• Your rationale for your design choices
• How the new graphics better support the article's message
• Any methodological improvements or additional insights revealed

6) Submit your work

Please provide:

• The R code used to generate your graphics
• The resulting graphics (PNG files)
• A markdown file containing:
  • Your narrative (≤1 page)
  • Your design analysis (2-3 paragraphs)

When you are finished, commit and push your changes to GitHub and notify us in Workable. If working in a private repository, also add Drew Rosebush to the repository as a collaborator.

Misc Notes

• This repository is based on our standard data project template. By convention:
  • R scripts go in the R/ folder
  • Data files (e.g., CSVs, Parquet files) go in the data/ folder
  • Exported images (e.g., charts, maps) go in the export/ folder
  • Documentation and narratives go in the root directory or a docs/ folder
• Please comment your code where it helps explain your process or decisions.
• Focus on clarity, insight, and visual storytelling in your improved graphics.