EU & UK Import Preference Utilisation Rate App
The UK and EU have agreed on tariff-free, quota-free access under the Trade and Cooperation Agreement (TCA),
but this only applies when goods meet the Rules of Origin. As a result, not all trade qualifies for zero tariffs.
Understanding where preferences are being used—and where they are not—through Preference Utilisation Rate (PUR) data
helps inform HMG’s efforts to increase trade and refine the TCA.
This interactive data dashboard analyzes UK import preference utilization rates,
enabling policymakers and businesses to assess the impact of Rules of Origin under the TCA.
I developed this application using R and Shiny, integrating automated data processing,
interactive visualizations, and statistical analysis to track how tariff preferences are used over time.
The app reveals that overall PUR rates for agricultural products are significantly high
and allows users to download customized reports based on their selected filters.
Key Data Science Elements:
- Data Processing & Wrangling: Cleaned, transformed, and structured UK import data using
dplyr, tidyverse, and data.table for efficient querying and filtering.
- Automated Data Pipeline: Developed scripts to automate data extraction, transformation, and updating for real-time accuracy.
- Time-Series Analysis & Visualization: Built
ggplot2 and plotly-powered graphs to analyze PUR trends by HS section and partner country.
- Interactive & Scalable Dashboards: Designed dynamic dashboards with country-specific filtering, user input capabilities, and downloadable reports.
- Geospatial Mapping: Implemented
leaflet and sf to visualize preference utilization across regions.
- Performance Optimization: Used reactive programming in Shiny to ensure the app efficiently handles large datasets.
Impact & Skills Demonstrated:
- Real-World Trade Insights: The app highlights that agricultural products have significantly high PUR rates, helping policymakers assess trade trends.
- Data-Driven Decision Making: Enables policymakers to identify underutilized trade preferences and refine trade policy accordingly.
- End-to-End Data Science Workflow: Showcases expertise in data engineering, visualization, automation, and interactive analytics.
- Technical Problem-Solving: Tackled challenges in handling missing data, optimizing dashboard speed, and structuring high-dimensional trade data.