- A data visualisation story of the impact of COVID-19 in South Korea. Developed using Jupyter Notebook and R.
- The dataset for the visualisation came from Kaggle here.
- The dataset contains continuous, categorical, location, temporal and text data across several CSV files.
- Data was wrangled and organised using R with the lubridate library helping out tremendously with the time series data and ggplot2 for plotting the data.
- The goal for this visualisation was to portray how COVID-19 impacted South Korea in the early stages of the pandemic (Feb 2020 - July 2020).
- There are a few charts that describe the number of confirmed cases over time, the ways people were infected, the ages of people infected and the top regions with the most cases.
- Daegu is known for having the largest coronavirus outbreak outside of China which the first chart clearly depicts. PDFs describing the dataset as well as concept of the story have been provided also.
- Judging from the charts, it can be concluded that despite having a very rocky start with the pandemic, the number of COVID-19 cases eventually dwindled down to a few hundred each day thanks to fast-acting and strict testing and regulations. Quite impressive for a country with a population of ~51 million.
