Railways, passengers carried (million passenger-km)

Source: worldbank.org, 03.09.2025

Year: 2020

Flag Country Value Value change, % Rank
Albania Albania 0.593 -70.4% 64
Australia Australia 15,053 -20.1% 9
Austria Austria 7,417 -44.4% 19
Azerbaijan Azerbaijan 172 -68.4% 53
Belgium Belgium 7,397 -31.8% 20
Bulgaria Bulgaria 1,119 -26.6% 34
Bosnia & Herzegovina Bosnia & Herzegovina 15 -73% 61
Belarus Belarus 3,741 -40.4% 24
Canada Canada 229 -86.8% 51
Switzerland Switzerland 13,334 -38.7% 10
Chile Chile 517 -51.4% 42
China China 328,251 -77.2% 2
Cameroon Cameroon 225 -11.5% 52
Congo - Kinshasa Congo - Kinshasa 5.36 -80.4% 63
Czechia Czechia 6,665 -39% 21
Germany Germany 58,822 -42.3% 6
Algeria Algeria 348 -76.1% 46
Spain Spain 11,999 -58.4% 13
Estonia Estonia 263 -32.9% 48
Finland Finland 2,820 -42.7% 26
France France 64,859 -42.3% 5
Gabon Gabon 87 -49.2% 55
United Kingdom United Kingdom 24,188 -70.7% 7
Georgia Georgia 247 -63.5% 50
Greece Greece 640 -48.9% 38
Croatia Croatia 449 -38.8% 43
Hungary Hungary 4,854 -37.4% 23
India India 1,050,738 -9.2% 1
Ireland Ireland 834 -65.2% 36
Iran Iran 5,170 -65.2% 22
Israel Israel 1,253 -65% 33
Italy Italy 22,269 -60.6% 8
Japan Japan 263,211 -39.5% 3
Kazakhstan Kazakhstan 8,649 -51.1% 16
Kyrgyzstan Kyrgyzstan 6.8 -81.7% 62
Lithuania Lithuania 260 -45.7% 49
Luxembourg Luxembourg 269 -41.9% 47
Latvia Latvia 413 -35.8% 44
Morocco Morocco 2,409 -49.8% 28
Moldova Moldova 29.2 -60.6% 56
Mexico Mexico 523 -66.7% 41
North Macedonia North Macedonia 25 -59.7% 58
Montenegro Montenegro 27.8 -58.2% 57
Mongolia Mongolia 580 -47.8% 40
Malaysia Malaysia 929 -57.8% 35
Netherlands Netherlands 9,200 -52.6% 15
Norway Norway 1,804 -51.4% 30
New Zealand New Zealand 802 +3.28% 37
Poland Poland 12,487 -43.4% 11
Portugal Portugal 2,552 -48.6% 27
Russia Russia 78,135 -41.4% 4
Serbia Serbia 157 -44.9% 54
Slovakia Slovakia 2,180 -46.7% 29
Slovenia Slovenia 397 -43.1% 45
Sweden Sweden 8,129 -44.4% 18
Tajikistan Tajikistan 21.7 -22.8% 59
Tunisia Tunisia 581 -43.2% 39
Turkey Turkey 8,297 -41.8% 17
Ukraine Ukraine 10,696 -62.4% 14
United States United States 12,460 -61.6% 12
Uzbekistan Uzbekistan 1,795 -59.1% 31
Vietnam Vietnam 1,516 -52.4% 32
South Africa South Africa 3,502 -36.4% 25
Zimbabwe Zimbabwe 17 -91.7% 60 

                    
# 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.PASG.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.PASG.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))