DS4B-final-project

RShiny Dashboard

Link to RShiny Dashboard: https://0uexv8-johann-wagner.shinyapps.io/invasive_species_dashboard/

Purpose

This is a repository to store the R Codebase used to produce the Final Project for DS4B 2023. The details of the Assignment instructions are further down this README.md file.

Project Management using GitHub

• GitHub Issues were used to manage and implement new features and documentation.
• GitHub Branching was used for most major Issues (see Branch History below).
• GitHub Releases were used to distinguish major developments of the RShiny App.
• GitHub Projects and Milestones were used to manage work timelines Data Science for Biologists (DS4B).

Repository Content

• The invasive_species_dashboard folder contains the entire self-contained RShiny dashboard codebase and data.

• The processed_data folder includes:

  • The data_cleaning.Rmd file, which showcases the data cleaning thought process and highlights some of the initial data issues with the raw data.
  • There are also several .csv files, including:
    • tidied_data.csv is the binded rows of the raw data.
    • cleaned_data.csv is the cleaned data, which removed the excluded data.
    • dashboard_data.csv is the cleaned data with a few extra columns.
    • excluded_data.csv is the excluded data which were identified in data_cleaning.Rmd.

• The raw_data folder includes the seven raw data files. The respective DOIs are listed below:

  1. European Rabbits (Oryctolagus cuniculus)
  2. European Red Foxes (Vulpes vulpes)
  3. Cane Toads (Rhinella marina)
  4. Feral Cats (Felis catus)
  5. Feral Horses (Equus (Equus) caballus)
  6. Feral Pigs (Sus scrofa)
  7. Red Imported Fire Ants (Solenopsis invicta)

• The screenshots folder contains .png files that are used in final_project.Rmd

• The scripts folder contains .R files that load, clean, wrangle, manipulate, and visualise the raw data.

  • The custom-functions sub-folder contains a bunch of .R files that are functions used in the main .R files.

The Brief

Your task is to:

• choose a dataset or combination of datasets
• develop some questions/aims related to that dataset, including appropriate background research
• use R to address those questions/aims
• write your findings into a report of up to 2000 words and 10 figures
• summarise your findings in a poster which you will present in the week 12 workshop

Ideally, you should choose a combination of dataset and questions/aims that are interesting to you, because the more interesting you find it, the better your work will be!

Branch History

Version control using Git was trialed for this assignment, as I wanted to learn more about the uses of Pull Requests and Merging Branches. I used SourceTree to visualise all of my commits/pushes/branch merges.