Data Visualizations

Tools: Shiny, R, GitHub

The project is hosted on shinyapps.io and includes three interactive charts exploring Washington report card enrollment trends using a multi-page layout. I created the bar chart looking at Gender X students by county (second tab).

Summary

This project explores data on enrollments in K-12 education throughout Washington schools. I analyzed trends that can be used to create changes to the education system based on race, familial background, and gender within the 2019-2020 school year.

My group looked at how race impacts enrollment numbers throughout WA counties. Questions we asked throughout include:

  1. How does the number of Gender X students change throughout grade levels?

  2. How does family background affect student enrollment numbers?

  3. How does race impact enrollment numbers throughout counties?

Visualizations take a few seconds to load

I explored trends in CO2 emissions using data compiled by Our World In Data, which examines CO2 and greenhouse gas emissions across various different countries around the world. The variables I examined include: 

  • 'year' - year each annual emission is from

  • ‘country' - which region the data occurred in

  • 'co2' - annual production-based emissions of carbon dioxide in million tonnes

I filtered to the top ten regions with the highest annual emissions of carbon dioxide.

Summary

I was tasked to create an interactive visualization exploring trends from the dataset. I decided the make a line plot showing CO2 emission rates over time. I used ggplot2 to create the chart and plotly for the ability to hover over points.

I used 3 files, ui.R, server.R, and app.R, to clean up my work.

Within the UI, I connected output placeholders to my chart and defined what I wanted each widget to do; for example, I defined the choices for the themes users can pick.

For aesthetic purposes, I downloaded the shinythemes package to change the layout of the website, hosting the website through Shiny for accessibility.