This repository documents my weekly “for fun” coding projects during my sabbatical year. The goal is simple: stay accountable, sharp and avoid getting rusty in R, Python, and wider data technologies. This repository is my public record.

I’m treating this year as a chance to practise the full analytical workflow:

• Finding or scraping data
• Cleaning and validating it
• Understanding the schema
• Analysing and visualising
• Writing up insights
• Experimenting with new tools, packages, and techniques

These projects range from small exploratory analyses to building pipelines or learning new libraries.

Each weekly folder contains: the code, the data, a short write‑up summarising what I did and what I learned

Below is a high‑level summary of each week’s project.

1. R web‑scrape using vrest and building a robust automated data pipeline
   Scraped multi‑year trade preference utilisation data, cleaned and structured it, and built a repeatable pipeline for future updates.

2. R data analysis of utilisation trade data
   Analysed the scraped dataset: top countries, commodity drivers, time‑series trends, and utilisation patterns. Basic but foundational R data wrangling.

3. APIs in R (basic)
   Tried, failed and succeeded in creating multi-dimensional API calls to extract real-world trade data in R.

4. API workflow for UK-Nordic trade analysis in R Created workflow to extract API trade data, process, transform and disseminate via a markdown doc.

5. R EU trade deficit automation and fun ggploting R ggplot practice and exploration of a data point (UK-EU trade deficits) I was interested in.

6. Python basic notebook practice Project was set up for basic python, plots and setting up VS code.

7. Python web-scrape for UK preference utilisation data. Project to create a full ETL style pipeline and workflow for extracting and transforming trade data to create new data for analysis.

8. Premier League Top Scorers Wiki-Scrape and Data Wrangle Useful web-scrape using Python to extract for fun football data and then basic wrangling and plots using pandas/seaborn/matplotlib

9. Premier League Top Scorers R plotting tbc

10. World Cup Results plotting in R Using ggflags package and some IMF data to combine making a ok-ish looking chart with some statistics

11. World Cup Results Statistic plotting in R tbc