The primary purpose of this application is to display data collected across North America on fish species, using a standardized collection approach and resulting comparable metrics by AFS. Additionally, users can upload their own data to compare to the provided standardized data.

Link to the deployed app: https://viz.datascience.arizona.edu/afs-standard-fish-data/

Dashboard

• app/app.R: file that generates dashboard showing standardized fish data and allows user to upload data to compare
• app/Test_results_full_012723.csv: standardized fish data file
• app/R/functions.R: functions to calculate three metrics of interest
• app/www/AFS_sponsor_3.png: image file with sponsor logo displayed on dashboard "About" page

Data prep

• app/process_user_data.Rmd: generates example user upload data (user_example.csv) shown in app; shows development of metric calculations
• app/user_example.csv: example user upload data
• app/download_map_data.R: code to download the EPA Ecoregions data used in app map
• analysis_scripts: folder with scripts to prepare standardized data; newer version of this is in app/R/functions.R
• input_examples: folder containing additional user upload example datasets

Package versions & dependences

• renv folder
• renv.lock
• .Rprofile

Repository metadata

• .gitignore
• AFS_database_code.Rproj
• LICENSE
• README.md
• CITATION.cff

Please use the citation below if you use or modify this tool for research purposes.

Tracy, E., Guo, J., Riemer, K., & Bonar, S. (2023). Code for "AFS Standard Fish Data App" (Version 1.0.0) [Computer software]. https://doi.org/10.5281/zenodo.8169922

If you would like to suggest or make changes to this app, there are a few ways to do so. You can reach out via email to Scott Bonar or the CCT Data Science team with suggestions.You can also create an issue describing a problem with the code or improvements. Because this is a forked repo, issues need to be made in the upstream repo under the "Issues" tab.

If you can make changes to the code yourself, feel free to fork this repo and make a pull request. To run the code locally, it is necessary to download the ecoregions map data by running app/download_map_data.R and have access to the standardized fish records location data file app/Lat_long_AFSshiny_012023.csv. Package versions and dependencies are tracked with renv.