This was mainly a data-visualization exercise in R. I worked with three public time-series datasets and tried to make the comparisons readable without turning descriptive charts into causal claims.
- How did annual CO2 emissions change in the United States, India, and Pakistan after 1920?
- How did global emissions and temperature anomalies move over the same period?
- How did annual space-launch counts change by country after 2010?
The space-launch analysis is a separate question. I am not using it as an explanation for climate change.
- Filtering and reshaping time-series data with
dplyr - Parsing dates with
lubridate - Building the same idea in base R and
ggplot2 - Controlling labels, units, notation, and colors
- Aggregating country-level observations
The input files are in activity03/. Check their embedded metadata and original providers before reusing them for research.
install.packages(c("dplyr", "ggplot2", "lubridate"))The script uses Posit Cloud paths. Replace /cloud/project/activity03/... with repository-relative paths.
These figures are descriptive. Similar movement in two series is not enough to establish causality.