Ores and metals exports (% of merchandise exports)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Albania Albania 5.65 +5.61% 26
Argentina Argentina 0.171 -56.7% 85
Armenia Armenia 15.8 +5.75% 11
Antigua & Barbuda Antigua & Barbuda 0.483 82
Australia Australia 34.2 -3.25% 4
Azerbaijan Azerbaijan 1.03 +34.7% 75
Belgium Belgium 3.51 -4.43% 41
Burkina Faso Burkina Faso 0.247 -23.3% 84
Bulgaria Bulgaria 15.6 +23.7% 12
Bosnia & Herzegovina Bosnia & Herzegovina 8.27 +17.8% 18
Belize Belize 0.435 -39.4% 83
Bolivia Bolivia 43.6 +47.7% 2
Brazil Brazil 12.6 +2.1% 14
Barbados Barbados 7.63 +13.5% 22
Canada Canada 7.22 -1.32% 24
Switzerland Switzerland 1.49 -4.94% 69
Chile Chile 56.1 +7.96% 1
China China 1.51 +14.5% 67
Cyprus Cyprus 4.43 +10.4% 32
Czechia Czechia 1.4 +3.68% 71
Germany Germany 2.66 -2.14% 51
Denmark Denmark 1.97 +4.01% 61
Dominican Republic Dominican Republic 3.28 +21.9% 45
Ecuador Ecuador 6.89 -1.62% 25
Egypt Egypt 5.41 +14.8% 28
Spain Spain 3.43 +7.08% 42
Estonia Estonia 1.87 -12.4% 62
Finland Finland 7.95 +10.6% 20
Fiji Fiji 2.27 +59% 59
United Kingdom United Kingdom 4.66 +3.11% 31
Georgia Georgia 13.6 -37.9% 13
Greece Greece 8.41 +8.5% 17
Grenada Grenada 1.48 +30.8% 70
Guatemala Guatemala 0.8 -9.16% 76
Guyana Guyana 0.695 -38.6% 78
Hong Kong SAR China Hong Kong SAR China 1.83 -15.4% 64
Croatia Croatia 4.34 +8.1% 33
Hungary Hungary 1.38 +1.81% 72
India India 3.69 -5.11% 40
Ireland Ireland 0.61 +4.09% 80
Iceland Iceland 34.3 -5.67% 3
Israel Israel 1.2 -14.5% 74
Italy Italy 2.37 +0.135% 55
Japan Japan 3.41 +1.18% 43
Kyrgyzstan Kyrgyzstan 9.15 -11% 16
South Korea South Korea 2.4 -10.7% 54
Sri Lanka Sri Lanka 0.567 -5.37% 81
Lithuania Lithuania 1.86 +3.85% 63
Luxembourg Luxembourg 4.3 -1.53% 35
Latvia Latvia 1.76 -1.28% 65
Macao SAR China Macao SAR China 32.8 +65.3% 6
Moldova Moldova 2.31 +21.2% 56
Maldives Maldives 4.29 +39.5% 37
Mexico Mexico 2.08 +2.17% 60
North Macedonia North Macedonia 4.69 +17.5% 30
Malta Malta 0.678 -14.6% 79
Myanmar (Burma) Myanmar (Burma) 2.43 +73.5% 53
Montenegro Montenegro 16.4 -0.24% 10
Mauritius Mauritius 2.28 -15.9% 58
Malaysia Malaysia 3.7 -2.47% 39
Namibia Namibia 32.3 +32.6% 7
Netherlands Netherlands 2.31 -1.86% 57
Norway Norway 5.23 +0.672% 29
New Zealand New Zealand 3.31 +1.16% 44
Pakistan Pakistan 4.34 -3.75% 34
Panama Panama 9.59 -87.5% 15
Philippines Philippines 7.51 -4.68% 23
Poland Poland 3.2 +5.12% 46
Portugal Portugal 2.61 +1.6% 52
Paraguay Paraguay 1.5 +36.2% 68
French Polynesia French Polynesia 0.000692 -99.3% 86
Romania Romania 2.69 +8.44% 50
El Salvador El Salvador 1.28 +13.6% 73
Suriname Suriname 3.98 +262% 38
Slovakia Slovakia 1.74 +18.3% 66
Slovenia Slovenia 3.04 -3.21% 47
Sweden Sweden 5.54 +8.43% 27
Togo Togo 19.6 -27.3% 9
Thailand Thailand 2.79 +19.4% 49
Trinidad & Tobago Trinidad & Tobago 0.774 -69.3% 77
Turkey Turkey 4.29 -1.81% 36
Ukraine Ukraine 8.17 +36.5% 19
United States United States 2.88 +0.934% 48
Uzbekistan Uzbekistan 7.69 -8.4% 21
South Africa South Africa 29.2 +0.187% 8
Zimbabwe Zimbabwe 33.8 -21.3% 5 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'TX.VAL.MMTL.ZS.UN'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'TX.VAL.MMTL.ZS.UN'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))