Railways, goods transported (million ton-km)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 25.1 -3.55% 50
United Arab Emirates United Arab Emirates 1,380 -3.23% 39
Australia Australia 453,091 +1.26% 4
Austria Austria 21,781 +6.26% 15
Azerbaijan Azerbaijan 5,316 +9.36% 28
Bulgaria Bulgaria 4,658 +3.44% 30
Belarus Belarus 44,478 +4.85% 9
Canada Canada 430,170 -3.12% 5
Switzerland Switzerland 12,024 +8.69% 20
Cameroon Cameroon 874 -8.53% 42
Congo - Kinshasa Congo - Kinshasa 192 +12.1% 47
Czechia Czechia 16,326 +7.05% 17
Germany Germany 123,067 +13.5% 6
Spain Spain 10,299 +14.8% 23
Estonia Estonia 2,128 +23.1% 38
Finland Finland 10,749 +6.03% 22
France France 35,751 +14.3% 10
Georgia Georgia 3,322 +13.6% 33
Greece Greece 579 +4.32% 44
Croatia Croatia 3,172 -3.26% 34
Hungary Hungary 11,346 -2.15% 21
India India 719,762 +1.71% 3
Ireland Ireland 70 -5.41% 49
Iran Iran 32,920 -8.46% 11
Israel Israel 1,085 -13.2% 40
Italy Italy 24,262 +16.9% 13
Kyrgyzstan Kyrgyzstan 1,003 +7.04% 41
South Korea South Korea 6,757 +1.58% 27
Lithuania Lithuania 14,566 -8.19% 19
Luxembourg Luxembourg 176 +8.64% 48
Latvia Latvia 7,367 -7.68% 25
Morocco Morocco 3,148 +1.19% 35
Moldova Moldova 665 +10.9% 43
Mexico Mexico 92,437 +7.21% 7
North Macedonia North Macedonia 375 +9.75% 46
Mongolia Mongolia 18,345 -3.3% 16
Netherlands Netherlands 7,188 +7.85% 26
Norway Norway 4,305 +4.74% 32
New Zealand New Zealand 4,444 +7.58% 31
Poland Poland 54,387 +6.44% 8
Portugal Portugal 2,699 +12.4% 37
Russia Russia 2,638,562 +3.68% 1
Serbia Serbia 2,925 +6.48% 36
Slovakia Slovakia 8,580 +18.1% 24
Slovenia Slovenia 4,937 +4.46% 29
Sweden Sweden 23,246 +5.21% 14
Tunisia Tunisia 413 +23.4% 45
Turkey Turkey 15,900 +3.06% 18
United States United States 2,239,401 +6.53% 2
Uzbekistan Uzbekistan 24,619 +4.18% 12 

                    
# 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 = 'IS.RRS.GOOD.MT.K6'

# 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 <- 'IS.RRS.GOOD.MT.K6'

# 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))