Rail lines (total route-km)

Source: worldbank.org, 01.09.2025

Year: 2021

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 279 +6.9% 63
Armenia Armenia 698 0% 55
Austria Austria 4,962 -0.0805% 22
Azerbaijan Azerbaijan 2,139 -4.48% 40
Belgium Belgium 3,612 -0.083% 31
Burkina Faso Burkina Faso 518 0% 61
Bulgaria Bulgaria 4,031 +0.0496% 26
Belarus Belarus 5,474 +0.376% 21
Botswana Botswana 886 0% 52
Canada Canada 48,150 -0.33% 5
Switzerland Switzerland 4,122 +12.3% 25
Chile Chile 2,396 0% 37
China China 109,767 +3.32% 2
Côte d’Ivoire Côte d’Ivoire 639 0% 59
Cameroon Cameroon 884 0% 53
Congo - Kinshasa Congo - Kinshasa 3,641 0% 29
Czechia Czechia 9,357 -0.213% 18
Germany Germany 33,401 +0.00599% 6
Algeria Algeria 4,001 -0.491% 27
Spain Spain 15,963 +0.726% 13
Finland Finland 5,918 0% 20
France France 27,716 -0.56% 7
Gabon Gabon 648 0% 58
United Kingdom United Kingdom 16,179 -1.05% 11
Georgia Georgia 1,387 -1.7% 49
Greece Greece 2,339 -0.256% 38
Hong Kong SAR China Hong Kong SAR China 230 +104% 65
Croatia Croatia 2,617 0% 35
Hungary Hungary 8,037 +5.92% 19
India India 68,103 +0.216% 4
Ireland Ireland 1,650 -12.6% 47
Iran Iran 9,455 +0.0127% 17
Israel Israel 1,720 +6.24% 45
Italy Italy 17,305 +0.287% 10
Kazakhstan Kazakhstan 16,006 -0.357% 12
Kyrgyzstan Kyrgyzstan 417 -1.65% 62
South Korea South Korea 4,309 +0.555% 24
Lithuania Lithuania 1,919 +0.419% 41
Luxembourg Luxembourg 271 0% 64
Latvia Latvia 1,859 0% 42
Morocco Morocco 2,295 0% 39
Moldova Moldova 1,149 -0.174% 51
Madagascar Madagascar 673 0% 57
North Macedonia North Macedonia 683 0% 56
Mongolia Mongolia 1,821 0% 43
Mauritania Mauritania 728 0% 54
Malaysia Malaysia 1,655 0% 46
Netherlands Netherlands 3,041 -1.2% 34
Norway Norway 3,885 0% 28
Poland Poland 18,620 +0.0484% 9
Portugal Portugal 2,527 +0.0358% 36
Romania Romania 10,769 0% 14
Russia Russia 85,544 -0.0129% 3
Serbia Serbia 3,348 +0.44% 32
Slovakia Slovakia 3,626 -0.011% 30
Slovenia Slovenia 1,209 0% 50
Sweden Sweden 9,714 +0.0309% 16
Tajikistan Tajikistan 620 -6.34% 60
Tunisia Tunisia 1,777 0% 44
Turkey Turkey 10,546 +1.62% 15
Uruguay Uruguay 1,498 0% 48
United States United States 148,553 -0.132% 1
Uzbekistan Uzbekistan 4,732 0% 23
Vietnam Vietnam 3,159 0% 33
South Africa South Africa 20,953 0% 8 

                    
# 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.TOTL.KM'

# 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.TOTL.KM'

# 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))