Biodiversity explorer proof of concept for Appsilon.

You use the app to select a specific specie that interests you. You can either search by Scientific Names or Common Names, but not both.

1. If you search by scientific names however, the common name is deployed as a plot title for convinence.
2. You can see the occurences/sightings of these animals over time. Individual sightings aren't plotted, rather the aggregated counts are.

1. The App uses shiny modules to modularize code.
2. It is made up of Four main Modules in total. The "DataServer", "Input" module, "Map" Module, "Timeline" Module.
3. The time line modules have both UI and Server components. Both of which are composed of the Input and Data Server Modules.
4. The Input and Data Server modules serve has the fundamental building blocks of the app and can be thought of as root modules.

• The Input module contains the UI element for the sidebar where inputs are provided. The inputs here are required by the DataServer Module and as such they share a name space.

• The MapUI and TimelineUI sub modules are composed of the Input module where a distinct namespace ID is hardcoded in the Input modules in each of this cases, this enforces that the inputs for the two modules never crash.

• In the MapServer and Timeline server, the Data Module inherents the namespace ids described earlier. Again this ensure they both have unique computations and consistent data.

Bootstraplib is used here.

• A green theme was carefully selected to compliment the purpose of the app.
• A Navbar page is used to show that the logic is decoupled.
• A bootstrap 4 was used as it is more stable than 5.

• I had setup a cloud-based Postgres sql server. However, firewall restrictions with my company laptop made experimenting with this architecture impossible.
• To make this architecture work however. Set up a database connection with DBI, then simply replace the "test" dataset everywhere in the app with the database.
• Use DBPLYR to query the database and keep the current syntax constant. Or use SQL directly
• Cache the plots to decrease load time.
• Perform some of the logic in a different R session perharps with Plumber.
• Alternatively, use connect with a Spark Cluster and use sparklyr

• UI test, the ui components where "snapshotted" and can be used to check for changed behaivours in the future.

• The DataServer logic is checked for reactivity and to ensure that implemented logic for each search term is decoupled.