Table Contest 2025 Submission : Countries Exploration

This is a step-by-step walkthrough demonstrating how to recreate my 192 UN’s countries table, which presents information’s such as: demographics, macroeconomic data, icons, maps, audio and video content, graphics, and an AI chatbot; for potential destinations/country exploration

reactable,
R
Shiny
modal
audio
video
AI chatbot
map
image
icon
Author

Conrad MESSINA

Published

October 16, 2025

Abstract

I developed an R Shiny application designed to recommend destinations to migrants who wish to move abroad whether for scholarships, residency, refugee or asylum, or tourism.

The application provides users with a comprehensive overview of potential destinations, presented through an interactive and informative table. This table goes beyond traditional numbers by integrating rich, real-world content such as: National flag and anthem, Capital city map, Population data and age pyramid, YouTube videos, AI Assistant for instant guidance … and more.

This project highlights how a table can be much more than a numerical tool: it can serve as a gateway to meaningful, multidimensional information that reflects our modern, digital world.
Keywords

reactable, R, Shiny

I. Introduction

I.1 Context

Since the dawn of humanity, people have always felt the need to move from one place to another (whether voluntarily or not) in search of better living conditions, fertile land, hunting opportunities, favorable climates for agriculture, or to escape conflicts. Today, migration remains highly relevant, driven by factors such as globalization, fewer border restrictions, technological advances in transportations, wars, family planning, population aging, corporate relocation etc.

“In 2005, there were approximately 191 million international migrants worldwide. Projections estimate this number will rise to 290 million by 2025, an increase of nearly 52% over two decades.”

United Nations, Department of Economic and Social Affairs, Population Division (2009). International Migration Report 2009: A Global Assessment

People migrate for various reasons, including economic factors (business opportunities, employment, company relocation, taxation, education), tourism (discovery, love, vacations, sports, entertainment), scholarships (universities), or as refugees and asylum seekers. The destination knowledge is essential for better integration and a successful stay.

I.2 Problem

With the rise of Big Data, the volumetry and variety (images, maps, audio, videos…) of available information’s through databases and social medias, make migration decisions more complex to be taken based on so various and unstructured data.

I.3 Our solution

To help simplify this process, I developed a Migration Recommender System using R and Shiny. It presents for each country/destination, all relevant information’s which a potential migrant needs in a single interactive row table (reactable), enriched with an AI assistant, designed to easy user self-service deepdive.

II. Data Preparation

Not a full Data preparation process but more data sets descriptions

We used a lot of data preparation methods (packages data, web scrapping, AI Data, YouTube, missing value imputation etc.) in this work. But we will not present them here as it is not the purpose of this contest. So we will just present and describe the final data sets used.

All the data needed are inside the folder “www/DATA”. let’s begin with .csv data files. These datasets together provide a rich multi-dimensional profile of countries, combining: Socio-demographic indicators (Literacy_Rate), Geographic & multimedia resources (Capital), Macroeconomic data (Macro1), Economic trends over time (growth_inflation). They are particularly suitable for applications country profiling,

II.1 Excel/csv data files

II.1.1 Literacy_Rate dataset

This dataset provides an overview of literacy and urbanization indicators by country.

Column Name Type Description
Country Character Full name of the country or region.
iso2c Character Two-letter ISO code of the country.
urban Numeric Percentage of the population living in urban areas.
rural Numeric Percentage of the population living in rural areas.
labor_force Numeric Labor force participation rate (in % of total population).
Literacy Rate Numeric Proportion of the literate population.

II.1.2 Capital dataset

This dataset contains geographical and media information about the capital cities of each country.

Column Name Type Description
country Character Full country name.
iso2c Character Two-letter ISO code.
iso3c Character Three-letter ISO code.
latitude Numeric Latitude coordinate of the capital city.
longitude Numeric Longitude coordinate of the capital city.
Places Character Link (e.g., YouTube) showcasing the capital city.

II.1.3 Macro1 dataset

This dataset contains macroeconomic indicators for each country for the year 2024.

Column Name Type Description
iso2c Character Two-letter ISO code.
country Character Country name.
year Numeric Year of observation (2024).
GDP Numeric Gross Domestic Product (current US dollars).
GDP_per_capita_PPP Numeric GDP per capita, adjusted for Purchasing Power Parity.
Unemployment Numeric Unemployment rate (%).
Exchange_rate Numeric Exchange rate (local currency per USD).

II.1.4 growth_inflation dataset

This dataset records historical macroeconomic trends (growth and inflation) for multiple years per country.

Column Name Type Description
country Character Country name.
iso2c Character Two-letter ISO code.
iso3c Character Three-letter ISO code.
year Numeric Year of observation.
GDP_GR Numeric Annual GDP growth rate (%).
Inflation Numeric Inflation rate (%).

II.2 .rds data files

II.2.1 resultdataset

A large dataset (204 columns) containing a wide range of international migration indicators for each country or region.

Column Name Type Description
Child migrants (<19) Numeric Percentage or number of child migrants under the age of 19.
International students (destination count) Numeric Number of international students hosted by the country.
Older migrants (>65) Numeric Percentage or number of migrants over the age of 65.
Share of female immigrants Numeric Percentage of female migrants.
Working age migrants (20-64) Numeric Percentage or number of working-age migrants.
Permanent migration inflows Numeric Total number of permanent immigration inflows.
Asylum seekers (host country/region) Numeric Number of asylum seekers hosted in the country.
Refugees (host country/region) Numeric Number of refugees hosted in the country.
Migration_to_<Country> Numeric Bilateral migration inflows to specific countries (e.g., Migration_to_Australia).

Contains demographic, social, and migration-related indicators. Allows analysis of migration composition by age, gender, and migrant type (e.g., students, refugees, asylum seekers). Supports bilateral migration flow analysis between countries.

II.2.2 pays_continentdataset

A simple reference dataset that maps each country to its corresponding continent along with its ISO 2-letter code.

Column Name Type Description
continent String Name of the continent to which the country belongs (e.g., Africa, Asia).
country String Official country name.
iso2c String Two-letter ISO 3166-1 alpha-2 country code.

II.2.3 pyramide_plotdataset

This dataset describes population pyramids by age and sex for different countries, including variables needed for visual plotting (population pyramid chart).

Variable Type Description
name chr Country name (e.g., Andorra).
code chr ISO 2-letter country code (e.g., AD).
AGE fct Age group categories in 5-year intervals (e.g., 0_4, 5_9, 10_14).
SEX fct Sex of the population (Male or Female).
population dbl Absolute number of people in this age/sex group.
age_num dbl Numeric representation of the age group (1 for 0–4, 2 for 5–9, etc.). Useful for ordering or plotting.
pop_abs dbl Same as population (redundant or prepared for further calculations).
xmin, xmax dbl Minimum and maximum x-coordinates for the bar segment representing the population group (used for plotting the pyramid).
ymin, ymax dbl Minimum and maximum y-coordinates for the bar segment. This suggests a rectangle-based plotting approach in ggplot or similar.
flag_colors list Vector of colors derived from the country’s flag (e.g., 3 dominant colors). Used to color the pyramid bars.
n_colors int Number of colors extracted from the flag.
gradient list Gradient colors computed for the age/sex group (likely for smoother visualization).
color chr Final color assigned to the segment (e.g., "#ff0000").

III. The Table

IV. Challenges & Future Improvements