Creating European Statistical Maps with eurostatmap

Introduction to Eurostat Maps

The eurostatmap function in ClinicoPath allows researchers to create choropleth maps using European statistical data. This powerful visualization tool can display various health, demographic, and economic indicators across European countries and regions using official Eurostat data or your own datasets.

Key Features

Dual Data Sources: Use either Eurostat API data or import your own CSV files
Multiple Geographic Levels: Support for NUTS 0 (countries), NUTS 1, NUTS 2, and NUTS 3 regions
Interactive and Static Maps: Choose between static plots or interactive visualizations
Customizable Styling: Various color palettes and classification methods
Comprehensive Statistics: Automatic generation of summary statistics and dataset information

Prerequisites

Before using the eurostatmap function, ensure you have the required packages installed:

library(ClinicoPath)

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library(dplyr)

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library(knitr)

# Load the sample data
data("eurostat_health_data")
data("eurostat_comprehensive_data") 
data("eurostat_timeseries_data")

# Load real Turkey data from Eurostat
data("turkey_nuts2_eurostat")
data("turkey_eastern_anatolia")
data("turkey_nuts1_aggregated")
data("turkey_timeseries_nuts2")

Using Sample Eurostat Data

Basic Map with Default Settings

The simplest way to create a eurostat map is to use a sample dataset with default settings:

# Create a basic map using local sample data
result <- eurostatmap(
  data = eurostat_health_data,
  indicator = "life_expectancy", # Use life expectancy indicator
  geo_level = "nuts0",          # Country level
  year = 2022,                  # Data year
  map_type = "static",          # Static map
  use_local_data = TRUE         # Use local data
)

This creates a map showing life expectancy across European countries using the included sample data.

Customizing Map Appearance

You can customize various aspects of your map:

# Create a customized map with different styling
result_custom <- eurostatmap(
  data = eurostat_health_data,
  indicator = "population_density",
  geo_level = "nuts0",
  year = 2022,
  map_type = "static",
  color_palette = "blues",           # Blue color scheme
  classification_method = "jenks",   # Natural breaks classification
  n_classes = 6,                     # 6 color classes
  map_title = "Population Density in Europe 2022",
  use_local_data = TRUE,
  cache_data = FALSE                 # Disable caching for example
)

Different Geographic Levels

The function supports different administrative levels:

# Country level (NUTS 0)
country_map <- eurostatmap(
  dataset_id = "demo_r_gind3",
  geo_level = "nuts0",
  year = 2022,
  map_title = "Countries (NUTS 0)"
)

# Major regions (NUTS 1)
region_map <- eurostatmap(
  dataset_id = "demo_r_gind3",
  geo_level = "nuts1",
  year = 2022,
  map_title = "Major Regions (NUTS 1)"
)

# Basic regions (NUTS 2)
basic_map <- eurostatmap(
  dataset_id = "demo_r_gind3",
  geo_level = "nuts2",
  year = 2022,
  map_title = "Basic Regions (NUTS 2)"
)

Using Your Own Data

Data Format Requirements

When using your own data, it must include:

A geo column with valid NUTS geographic codes (e.g., ‘DE’, ‘FR’, ‘ES’ for countries)
One or more indicator variables with numeric values
Optionally, a TIME_PERIOD column for year filtering

# Load the sample eurostat data included with ClinicoPath
data("eurostat_health_data")

# Display the structure of the data
knitr::kable(head(eurostat_health_data), 
             caption = "Sample Eurostat Health Data Structure")

The eurostat_health_data contains the following columns: - geo: NUTS country codes (AT, BE, DE, FR, etc.) - country_name: Full country names - life_expectancy: Life expectancy at birth (years) - population_density: Population per km² - gdp_per_capita: GDP per capita (thousands of euros) - healthcare_expenditure: Healthcare expenditure as % of GDP - TIME_PERIOD: Year of data (2022)

Creating Maps with Local Data

# Create a map using local health data
health_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "life_expectancy",
  geo_level = "nuts0",
  year = 2022,
  map_type = "static",
  color_palette = "viridis",
  classification_method = "quantile",
  n_classes = 5,
  map_title = "Life Expectancy in Europe",
  use_local_data = TRUE,
  cache_data = FALSE,
  add_to_data = FALSE
)

Multiple Indicators Analysis

You can analyze different health indicators using the same dataset:

# Healthcare expenditure map
expenditure_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "healthcare_expenditure",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "reds",
  map_title = "Healthcare Expenditure (% of GDP)",
  use_local_data = TRUE
)

# Hospital beds per capita
beds_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "hospital_beds",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "greens",
  map_title = "Hospital Beds per 100,000 Inhabitants",
  use_local_data = TRUE
)

# Cancer incidence rates
cancer_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "cancer_incidence",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "plasma",
  map_title = "Cancer Incidence Rate per 100,000",
  use_local_data = TRUE
)

Advanced Features

Interactive Maps

Create interactive maps for enhanced exploration:

# Create an interactive map
interactive_result <- eurostatmap(
  data = eurostat_health_data,
  indicator = "life_expectancy",
  geo_level = "nuts0",
  year = 2022,
  map_type = "interactive",        # Enable interactivity
  color_palette = "viridis",
  map_title = "Interactive Life Expectancy Map",
  use_local_data = TRUE
)

Interactive maps allow users to: - Hover over regions to see exact values - Zoom in and out - Pan across the map - Toggle layers on and off

Color Palettes and Classification Methods

Available Color Palettes

# Test different color palettes
palettes <- c("viridis", "plasma", "blues", "reds", "greens")

for (palette in palettes) {
  map_result <- eurostatmap(
    data = eurostat_health_data,
    indicator = "gdp_per_capita",
    color_palette = palette,
    map_title = paste("GDP per Capita -", palette, "palette"),
    use_local_data = TRUE
  )
}

Classification Methods

# Test different classification methods
methods <- c("quantile", "equal", "jenks", "pretty")

for (method in methods) {
  map_result <- eurostatmap(
    data = eurostat_health_data,
    indicator = "population_density",
    classification_method = method,
    map_title = paste("Population Density -", method, "classification"),
    use_local_data = TRUE
  )
}

Time Series Analysis

Working with multiple years of data:

# Load time series data
data("eurostat_timeseries_data")

knitr::kable(head(eurostat_timeseries_data), 
             caption = "Time Series Data Structure")

# Create maps for different years
years <- c(2018, 2020, 2022)

for (year in years) {
  yearly_data <- eurostat_timeseries_data[eurostat_timeseries_data$TIME_PERIOD == year, ]
  
  if (nrow(yearly_data) > 0) {
    map_result <- eurostatmap(
      data = yearly_data,
      indicator = "life_expectancy",
      geo_level = "nuts0",
      year = year,
      map_title = paste("Life Expectancy", year),
      use_local_data = TRUE
    )
  }
}

Practical Applications

Public Health Research

Example 1: Health System Performance

# Analyze healthcare system performance across Europe
healthcare_analysis <- eurostatmap(
  data = eurostat_health_data,
  indicator = "doctors_per_capita",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "blues",
  classification_method = "quantile",
  n_classes = 5,
  map_title = "Doctors per 100,000 Inhabitants",
  use_local_data = TRUE
)

Example 2: Disease Burden Mapping

# Map cardiovascular mortality rates
cvd_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "cardiovascular_mortality",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "reds",
  classification_method = "jenks",
  n_classes = 6,
  map_title = "Cardiovascular Mortality Rate per 100,000",
  use_local_data = TRUE
)

Epidemiological Studies

Example 3: Risk Factor Analysis

# Analyze smoking prevalence across Europe
smoking_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "smoking_prevalence",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "plasma",
  classification_method = "equal",
  n_classes = 5,
  map_title = "Smoking Prevalence (%)",
  use_local_data = TRUE
)

# Analyze alcohol consumption patterns
alcohol_map <- eurostatmap(
  data = eurostat_health_data,
  indicator = "alcohol_consumption",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "greens",
  classification_method = "quantile",
  n_classes = 4,
  map_title = "Alcohol Consumption (Liters per Capita)",
  use_local_data = TRUE
)

Preparing Your Own Data

Data Collection Guidelines

When preparing your own eurostat-compatible data:

Geographic Codes: Use official NUTS codes from Eurostat
Data Quality: Ensure numeric indicators are properly formatted
Missing Values: Handle missing data appropriately
Standardization: Use consistent units and scales

Example Data Preparation

# Create a custom dataset for mapping
custom_health_data <- data.frame(
  geo = c("AT", "BE", "DE", "FR", "IT", "ES", "NL", "PL", "SE", "DK"),
  country_name = c("Austria", "Belgium", "Germany", "France", "Italy", 
                   "Spain", "Netherlands", "Poland", "Sweden", "Denmark"),
  hospital_readmission_rate = c(8.2, 7.5, 9.1, 6.8, 7.9, 8.5, 6.2, 10.3, 5.8, 7.1),
  patient_satisfaction = c(85, 78, 82, 79, 76, 81, 88, 72, 91, 86),
  wait_time_days = c(45, 38, 52, 41, 67, 58, 28, 89, 22, 34),
  TIME_PERIOD = rep(2022, 10)
)

# Display the custom data
knitr::kable(custom_health_data, 
             caption = "Custom Health Data for Mapping")

# Create maps with custom data
readmission_map <- eurostatmap(
  data = custom_health_data,
  indicator = "hospital_readmission_rate",
  geo_level = "nuts0",
  year = 2022,
  color_palette = "reds",
  map_title = "Hospital Readmission Rate (%)",
  use_local_data = TRUE
)

Data Output and Analysis

Understanding the Results

The eurostatmap function provides several outputs:

Map Visualization: The main choropleth map
Dataset Information: Metadata about the data source
Summary Statistics: Descriptive statistics for the indicator
Downloaded Data: Optional table of the raw data (when using Eurostat API)

Statistical Summary

# The function automatically generates summary statistics
# These include: Min, Max, Mean, Median, Standard Deviation, Count

# Example of manually calculating similar statistics
indicator_stats <- eurostat_health_data %>%
  summarise(
    Min = min(life_expectancy, na.rm = TRUE),
    Max = max(life_expectancy, na.rm = TRUE),
    Mean = mean(life_expectancy, na.rm = TRUE),
    Median = median(life_expectancy, na.rm = TRUE),
    `Std Dev` = sd(life_expectancy, na.rm = TRUE),
    Count = sum(!is.na(life_expectancy))
  )

knitr::kable(indicator_stats, 
             caption = "Life Expectancy Statistics",
             digits = 2)

Tips and Best Practices

Performance Optimization

Use Caching: Enable cache_data = TRUE for repeated analyses
Appropriate Geographic Level: Choose the right NUTS level for your research question
Data Size: Be mindful of large datasets when using interactive maps

Data Quality Considerations

Missing Data: Check for missing values in your geographic codes
Outliers: Consider outlier treatment before mapping
Units: Ensure consistent units across your indicators

Visualization Best Practices

Color Choice: Select colorblind-friendly palettes
Classification: Choose appropriate classification methods for your data distribution
Title and Labels: Use clear, descriptive titles and labels

Troubleshooting

Common Issues and Solutions

Issue 1: Missing geo column

# This will cause an error
faulty_data <- eurostat_health_data
faulty_data$geo <- NULL

# The function will detect this and provide a helpful error message
tryCatch({
  eurostatmap(
    data = faulty_data,
    indicator = "life_expectancy",
    use_local_data = TRUE
  )
}, error = function(e) {
  cat("Error:", e$message, "\n")
})

Issue 2: Invalid geographic codes

Ensure NUTS codes are current and valid
Check for typos in country codes
Verify that codes match the selected geographic level

Issue 3: Network issues with Eurostat API

Check internet connection
Try enabling caching for large datasets
Consider using local data for offline analysis

Turkey Data Analysis with Real Eurostat Data

Turkey, as a candidate country of the European Union, is included in the Eurostat NUTS classification system. This section demonstrates how to use real Turkey data downloaded from Eurostat to create comprehensive regional analyses.

Turkey NUTS Classification Overview

Turkey’s NUTS system consists of: - NUTS-1: 12 regions (TR1, TR2, …, TR9, TRA, TRB, TRC) - NUTS-2: 26 subregions (TR10, TR21, TR22, …, TRA1, TRA2, TRB1, TRB2, TRC1, TRC2, TRC3) - NUTS-3: 81 provinces

The eastern regions TRA (Northeast Anatolia), TRB (Central East Anatolia), and TRC (Southeast Anatolia) are of particular interest for regional development studies.

Real Turkey Data Examples

Example 1: Turkey NUTS-2 Population Analysis

# Display real Turkey NUTS-2 data structure
knitr::kable(head(turkey_nuts2_eurostat, 8), 
             caption = "Real Turkey NUTS-2 Data from Eurostat")

# Create population map for all Turkey NUTS-2 regions
turkey_population_map <- eurostatmap(
  data = turkey_nuts2_eurostat,
  indicator = "population",
  geo_level = "nuts2",
  year = 2023,
  map_type = "static",
  color_palette = "viridis",
  classification_method = "quantile",
  n_classes = 5,
  map_title = "Population Distribution in Turkey (NUTS-2 Regions, 2023)",
  use_local_data = TRUE
)

Example 2: Economic Development Analysis (GDP per Capita)

# GDP per capita analysis across Turkey
turkey_gdp_map <- eurostatmap(
  data = turkey_nuts2_eurostat,
  indicator = "gdp_per_capita_pps",
  geo_level = "nuts2", 
  year = 2023,
  map_type = "static",
  color_palette = "reds",
  classification_method = "jenks",
  n_classes = 6,
  map_title = "GDP per Capita (PPS) in Turkey - NUTS-2 Regions",
  use_local_data = TRUE
)

# Summary statistics for GDP
gdp_summary <- turkey_nuts2_eurostat %>%
  summarise(
    Min_GDP = min(gdp_per_capita_pps, na.rm = TRUE),
    Max_GDP = max(gdp_per_capita_pps, na.rm = TRUE),
    Mean_GDP = mean(gdp_per_capita_pps, na.rm = TRUE),
    Median_GDP = median(gdp_per_capita_pps, na.rm = TRUE),
    `Std Dev` = sd(gdp_per_capita_pps, na.rm = TRUE)
  )

knitr::kable(gdp_summary, 
             caption = "GDP per Capita Statistics for Turkey",
             digits = 1)

Example 3: Eastern Anatolia Regions Focus (TRA, TRB, TRC)

# Display eastern regions data
knitr::kable(turkey_eastern_anatolia, 
             caption = "Eastern Anatolia Regions (Real Eurostat Data)")

# Create map focusing on eastern regions
eastern_population_map <- eurostatmap(
  data = turkey_eastern_anatolia,
  indicator = "population",
  geo_level = "nuts2",
  year = 2023,
  map_type = "static",
  color_palette = "plasma",
  classification_method = "equal",
  n_classes = 4,
  map_title = "Population in Eastern Anatolia (TRA, TRB, TRC Regions)",
  use_local_data = TRUE
)

# Economic comparison of eastern regions
eastern_gdp_comparison <- turkey_eastern_anatolia %>%
  select(geo, subregion_name, population, gdp_per_capita_pps) %>%
  arrange(desc(gdp_per_capita_pps))

knitr::kable(eastern_gdp_comparison,
             caption = "Economic Indicators - Eastern Anatolia Comparison",
             col.names = c("NUTS Code", "Region", "Population", "GDP per Capita (PPS)"))

Example 4: Regional Development Disparities

# Compare Western vs Eastern Turkey using aggregated NUTS-1 data
regional_comparison <- turkey_nuts1_aggregated %>%
  mutate(
    region_category = case_when(
      geo %in% c("TR1", "TR2", "TR3", "TR4", "TR5") ~ "Western Turkey",
      geo %in% c("TR6", "TR7", "TR8", "TR9") ~ "Central Turkey", 
      geo %in% c("TRA", "TRB", "TRC") ~ "Eastern Turkey",
      TRUE ~ "Other"
    )
  ) %>%
  group_by(region_category) %>%
  summarise(
    total_population = sum(population_total),
    avg_gdp_per_capita = weighted.mean(gdp_per_capita_weighted, population_total, na.rm = TRUE),
    avg_employment_rate = weighted.mean(employment_rate_weighted, population_total, na.rm = TRUE),
    regions_count = n(),
    .groups = "drop"
  )

knitr::kable(regional_comparison,
             caption = "Turkey Regional Development Comparison",
             digits = 1)

# Create aggregated NUTS-1 map
turkey_nuts1_map <- eurostatmap(
  data = turkey_nuts1_aggregated,
  indicator = "gdp_per_capita_weighted",
  geo_level = "nuts1",
  year = 2023,
  map_type = "static",
  color_palette = "blues",
  classification_method = "quantile",
  n_classes = 4,
  map_title = "GDP per Capita by NUTS-1 Regions in Turkey",
  use_local_data = TRUE
)

Example 5: Time Series Analysis (2018-2023)

# Display time series data structure
knitr::kable(head(turkey_timeseries_nuts2, 10),
             caption = "Turkey Population Time Series (2018-2023)")

# Population growth analysis
population_growth <- turkey_timeseries_nuts2 %>%
  group_by(geo, city_name, region_type) %>%
  summarise(
    pop_2018 = population[TIME_PERIOD == 2018],
    pop_2023 = population[TIME_PERIOD == 2023],
    growth_rate = ((pop_2023 - pop_2018) / pop_2018) * 100,
    .groups = "drop"
  ) %>%
  arrange(desc(growth_rate))

knitr::kable(population_growth,
             caption = "Population Growth Rates (2018-2023)",
             digits = 2)

# Create maps for different years
years <- c(2018, 2020, 2023)
for (year in years) {
  yearly_data <- turkey_timeseries_nuts2 %>%
    filter(TIME_PERIOD == year)
  
  yearly_map <- eurostatmap(
    data = yearly_data,
    indicator = "population",
    geo_level = "nuts2",
    year = year,
    map_type = "static",
    color_palette = "viridis",
    map_title = paste("Turkey Population", year),
    use_local_data = TRUE
  )
}

Using Live Eurostat Data for Turkey

The eurostatmap function can also download real-time Turkey data directly from Eurostat:

# Download live population data for Turkey
live_turkey_map <- eurostatmap(
  dataset_id = "tgs00096",  # Population by NUTS-2 regions
  geo_level = "nuts2",
  year = 2023,
  map_type = "static",
  color_palette = "viridis",
  map_title = "Live Turkey Population Data from Eurostat",
  use_local_data = FALSE,  # Download from Eurostat API
  cache_data = TRUE
)

# Download GDP data for Turkey
live_gdp_map <- eurostatmap(
  dataset_id = "tgs00006",  # GDP per capita by NUTS-2
  geo_level = "nuts2", 
  year = 2022,
  map_type = "static",
  color_palette = "reds",
  map_title = "Live Turkey GDP Data from Eurostat",
  use_local_data = FALSE,
  cache_data = TRUE
)

Turkey Data Insights

Based on the real Eurostat data, key findings include:

Population Distribution: İstanbul (TR10) dominates with ~16 million people
Economic Disparities: Western regions show higher GDP per capita than eastern regions
Regional Development: TRA, TRB, TRC regions show lower economic indicators
Growth Patterns: Different growth trajectories between western and eastern Turkey

Available Turkey Datasets in Eurostat

Real Turkey data is available in these Eurostat datasets: - tgs00096: Population by NUTS-2 regions (2013-2024) - tgs00006: GDP per capita by NUTS-2 (2012-2023)
- tgs00007: Employment rates by NUTS-2 (2013-2024) - demo_r_gind3: Demographic balance (2000-2024) - demo_r_d3dens: Population density (2001-2023) - demo_r_mlifexp: Life expectancy (2009-2023)

Advanced Examples

Comparative Analysis

# Create comprehensive data for comparison
data("eurostat_comprehensive_data")

# Show structure of comprehensive data
knitr::kable(head(eurostat_comprehensive_data[, 1:8]), 
             caption = "Comprehensive Eurostat Data (First 8 Columns)")

# Multiple indicator comparison maps would be created here
# This demonstrates the versatility of the eurostatmap function

Regional Analysis

# Load NUTS1 regional data
data("eurostat_nuts1_data")

# Create regional-level maps
regional_map <- eurostatmap(
  data = eurostat_nuts1_data,
  indicator = "life_expectancy",
  geo_level = "nuts1",
  year = 2022,
  color_palette = "viridis",
  map_title = "Regional Life Expectancy (NUTS 1)",
  use_local_data = TRUE
)

Conclusion

The eurostatmap function provides a powerful and flexible tool for creating European statistical maps. Whether you’re conducting public health research, epidemiological studies, or policy analysis, this function enables you to:

Visualize complex spatial patterns in health and demographic data
Compare indicators across European countries and regions
Create both static and interactive maps for different audiences
Integrate official Eurostat data or use your own datasets
Generate publication-ready visualizations with minimal code

Next Steps

Explore different Eurostat datasets using the API functionality
Experiment with various visualization options and color schemes
Combine eurostatmap with other ClinicoPath functions for comprehensive analysis
Consider time-series analysis for longitudinal studies

Additional Resources

For more examples and advanced usage, refer to the ClinicoPath package documentation and other vignettes in this series.

ClinicoPath

2025-07-13