Air transport, freight (million ton-km)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 12 -39.5% 80
Angola Angola 31 +7.39% 64
United Arab Emirates United Arab Emirates 15,301 +25.7% 5
Argentina Argentina 88.3 +8.81% 53
Armenia Armenia 1.35 +1,053% 101
Australia Australia 1,245 -5.45% 23
Austria Austria 125 +67.9% 51
Azerbaijan Azerbaijan 3,174 +56.3% 18
Belgium Belgium 1,827 +44.5% 19
Burkina Faso Burkina Faso 0.116 +141% 117
Bangladesh Bangladesh 83.7 -28.9% 56
Bulgaria Bulgaria 0.0581 -94.3% 122
Bahrain Bahrain 523 +173% 38
Belarus Belarus 2.26 +19.9% 96
Bolivia Bolivia 20.3 +12.9% 70
Brazil Brazil 1,294 +7.01% 21
Brunei Brunei 23.1 -42.6% 67
Bhutan Bhutan 0.358 -29.8% 113
Botswana Botswana 0.0633 -90.6% 121
Canada Canada 3,240 +40.5% 17
Switzerland Switzerland 1,231 +46% 24
Chile Chile 1,284 -12% 22
China China 20,961 +8.81% 2
Côte d’Ivoire Côte d’Ivoire 4.03 -6.7% 86
Cameroon Cameroon 0.0261 +5.53% 126
Congo - Kinshasa Congo - Kinshasa 0.949 -1.44% 106
Colombia Colombia 1,605 +7.26% 20
Cape Verde Cape Verde 0.0162 -48.4% 128
Costa Rica Costa Rica 2.17 +34.2% 97
Cuba Cuba 22.3 +66.3% 68
Cyprus Cyprus 0.106 -67.9% 120
Czechia Czechia 0.473 -89.1% 111
Germany Germany 11,533 +25.8% 6
Algeria Algeria 12.7 -18.8% 78
Ecuador Ecuador 4.29 -59.5% 85
Egypt Egypt 589 +34.3% 36
Spain Spain 851 +72.1% 29
Ethiopia Ethiopia 3,717 +28.3% 15
Finland Finland 750 +56.2% 31
Fiji Fiji 6.4 -70.7% 82
France France 4,107 +66.4% 13
United Kingdom United Kingdom 4,097 +11.7% 14
Georgia Georgia 0.113 -15.9% 118
Greece Greece 12.5 +23.4% 79
Guatemala Guatemala 0.00012 -81.4% 134
Guyana Guyana 0.022 -79.7% 127
Hong Kong SAR China Hong Kong SAR China 9,028 +11.7% 9
Honduras Honduras 1.02 -11.1% 103
Croatia Croatia 0.149 -17.2% 116
Indonesia Indonesia 773 +14.5% 30
India India 908 +3.75% 28
Ireland Ireland 86.4 -34.5% 55
Iran Iran 274 +35.4% 44
Iceland Iceland 149 +10.3% 50
Israel Israel 716 -12.4% 32
Italy Italy 1,151 +17.6% 25
Jordan Jordan 103 +38.4% 52
Japan Japan 10,947 +39.6% 7
Kazakhstan Kazakhstan 31.4 +33.6% 63
Kenya Kenya 300 +165% 43
Kyrgyzstan Kyrgyzstan 0.00233 -60.2% 129
South Korea South Korea 15,370 +23.4% 4
Kuwait Kuwait 200 +67.2% 48
Lebanon Lebanon 22.2 +16.3% 69
Libya Libya 13.6 +2,097% 77
Sri Lanka Sri Lanka 412 +82.1% 40
Luxembourg Luxembourg 8,589 +16.9% 10
Latvia Latvia 2.32 -46.4% 95
Macao SAR China Macao SAR China 1.1 -87.8% 102
Morocco Morocco 58.9 +27.4% 59
Moldova Moldova 0.607 +34.4% 110
Madagascar Madagascar 2.72 -49.5% 92
Maldives Maldives 1.74 -24% 98
Mexico Mexico 963 +31.4% 27
Marshall Islands Marshall Islands 0.0514 -39.4% 123
Malta Malta 2.75 -5.25% 91
Myanmar (Burma) Myanmar (Burma) 18.4 +1,378% 72
Montenegro Montenegro 0.0323 -78.9% 125
Mongolia Mongolia 0.169 -87.7% 115
Mozambique Mozambique 2.63 +8.48% 93
Mauritius Mauritius 87.4 +40.8% 54
Malawi Malawi 0.00059 +584% 132
Malaysia Malaysia 1,119 +37.1% 26
Namibia Namibia 0.00029 -100% 133
Niger Niger 0.756 -30.1% 108
Nigeria Nigeria 1.56 -7.07% 100
Netherlands Netherlands 4,346 +4.86% 12
Nepal Nepal 17.3 +26.4% 74
Nauru Nauru 3.31 +21.4% 88
New Zealand New Zealand 318 -59% 42
Oman Oman 254 +390% 46
Pakistan Pakistan 75.6 -21.1% 57
Panama Panama 150 +105% 49
Peru Peru 258 +168% 45
Philippines Philippines 530 +47.1% 37
Papua New Guinea Papua New Guinea 38.2 +19.9% 61
Poland Poland 215 +53% 47
North Korea North Korea 0.00219 -81.5% 130
Portugal Portugal 500 +78.7% 39
Paraguay Paraguay 0.312 -34.7% 114
Qatar Qatar 15,862 +17.1% 3
Romania Romania 1.62 +82.6% 99
Russia Russia 5,888 +36.5% 11
Saudi Arabia Saudi Arabia 679 +16.7% 33
Sudan Sudan 25.8 +34,091% 66
Senegal Senegal 3.31 +28.7% 87
Singapore Singapore 3,667 +21.4% 16
El Salvador El Salvador 11.3 +57.6% 81
Serbia Serbia 14.1 +73.7% 76
São Tomé & Príncipe São Tomé & Príncipe 0.974 +60% 104
Suriname Suriname 19.5 -27.4% 71
Sweden Sweden 349 +14.5% 41
Eswatini Eswatini 0.0374 +143% 124
Seychelles Seychelles 0.798 +5.31% 107
Syria Syria 3.14 +76% 89
Togo Togo 18.3 +86.6% 73
Thailand Thailand 604 -11.7% 35
Tajikistan Tajikistan 0.951 -50.4% 105
Turkmenistan Turkmenistan 0.684 -84.3% 109
Trinidad & Tobago Trinidad & Tobago 58.6 +80.6% 60
Tunisia Tunisia 5.6 -14.4% 83
Turkey Turkey 9,338 +35.9% 8
Tanzania Tanzania 4.36 +408% 84
Ukraine Ukraine 28.2 +101% 65
United States United States 46,005 +12.8% 1
Uzbekistan Uzbekistan 38 +90.1% 62
Venezuela Venezuela 0.106 -96.3% 119
Vietnam Vietnam 677 +18.3% 34
Vanuatu Vanuatu 0.437 -15.7% 112
Samoa Samoa 0.00211 -48.2% 131
Yemen Yemen 2.48 0% 94
South Africa South Africa 15.1 -85.2% 75
Zambia Zambia 75.2 +7.45% 58
Zimbabwe Zimbabwe 2.97 -70.2% 90 

                    
# 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.AIR.GOOD.MT.K1'

# 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.AIR.GOOD.MT.K1'

# 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))