Project overview
In this project, you’ll assume the role of a data scientist tasked with analyzing a COVID-19 dataset containing daily and cumulative numbers of tests, cases, recoveries and deaths by country. Your goal is to identify which countries were most affected by the pandemic and quantify their testing efforts.
You’ll apply your skills in data manipulation using dplyr and data visualization with ggplot2 in R. Techniques you’ll employ include filtering data, aggregating daily to cumulative numbers, and scaling statistics to population level. This comprehensive real-world project will solidify your data analysis skills and provide you with a structured workflow to tackle similar problems.
Objective: Analyze COVID-19 data using R to identify countries most affected and quantify testing efforts on a population level.
Key skill required
To complete this project, it's recommended to build these foundational skills in R
- Importing data into R
- Manipulating data using dplyr
- Visualizing data using ggplot2
- Combining R skills to perform a data analysis project
Projects steps
Step 1: Guided Project Introduction
Step 2: Understanding the Data
Step 3: Isolating the Data We Need
Step 4: Identifying Countries with the Highest Number of Deaths
Step 5: Extracting the Top Ten Tested Cases Countries
Step 6: Identifying the Highest Positive Against Tested Cases
Step 7: Scaling of Data to Population Level
Step 8: Ranking Countries Related to their Population
Step 9: Identifying Affected Countries Related to their Population
Step 10: Putting all together
Step 11: Next steps
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