Newborns protected against tetanus (%)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 60 0% 27
Angola Angola 63 -3.08% 26
Albania Albania 96 0% 5
Burundi Burundi 87 0% 13
Benin Benin 83 0% 17
Burkina Faso Burkina Faso 95 0% 6
Bangladesh Bangladesh 98 0% 3
Bahrain Bahrain 100 0% 1
Bahamas Bahamas 100 0% 1
Belize Belize 95 0% 6
Bolivia Bolivia 92 0% 9
Brazil Brazil 96 0% 5
Brunei Brunei 97 0% 4
Bhutan Bhutan 95 0% 6
Botswana Botswana 95 +4.4% 6
Central African Republic Central African Republic 60 -7.69% 27
Côte d’Ivoire Côte d’Ivoire 84 +1.2% 16
Cameroon Cameroon 81 0% 18
Congo - Kinshasa Congo - Kinshasa 78 -2.5% 21
Congo - Brazzaville Congo - Brazzaville 85 -2.3% 15
Colombia Colombia 97 0% 4
Comoros Comoros 85 +2.41% 15
Cape Verde Cape Verde 96 0% 5
Djibouti Djibouti 99 0% 2
Dominican Republic Dominican Republic 99 0% 2
Algeria Algeria 97 0% 4
Ecuador Ecuador 90 0% 11
Egypt Egypt 88 0% 12
Eritrea Eritrea 99 0% 2
Ethiopia Ethiopia 80 -5.88% 19
Fiji Fiji 98 0% 3
Gabon Gabon 79 -4.82% 20
Ghana Ghana 90 0% 11
Guinea Guinea 75 -6.25% 23
Gambia Gambia 96 0% 5
Guinea-Bissau Guinea-Bissau 83 +3.75% 17
Equatorial Guinea Equatorial Guinea 63 +5% 26
Guatemala Guatemala 90 0% 11
Guyana Guyana 96 -3.03% 5
Honduras Honduras 99 0% 2
Haiti Haiti 78 0% 21
Indonesia Indonesia 83 0% 17
India India 93 0% 8
Iran Iran 97 0% 4
Iraq Iraq 75 +2.74% 23
Jamaica Jamaica 93 0% 8
Jordan Jordan 91 -1.09% 10
Kenya Kenya 85 0% 15
Cambodia Cambodia 95 +2.15% 6
Kiribati Kiribati 93 0% 8
Kuwait Kuwait 99 0% 2
Laos Laos 90 -3.23% 11
Liberia Liberia 91 +1.11% 10
Sri Lanka Sri Lanka 99 0% 2
Lesotho Lesotho 83 0% 17
Morocco Morocco 93 0% 8
Madagascar Madagascar 75 0% 23
Maldives Maldives 99 0% 2
Mexico Mexico 98 0% 3
Mali Mali 83 0% 17
Myanmar (Burma) Myanmar (Burma) 88 0% 12
Mozambique Mozambique 84 0% 16
Mauritania Mauritania 79 -2.47% 20
Mauritius Mauritius 97 0% 4
Malawi Malawi 93 +3.33% 8
Malaysia Malaysia 96 +1.05% 5
Namibia Namibia 90 0% 11
Niger Niger 80 -3.61% 19
Nigeria Nigeria 69 +2.99% 25
Nicaragua Nicaragua 90 -2.17% 11
Nepal Nepal 93 +2.2% 8
Oman Oman 99 0% 2
Pakistan Pakistan 86 0% 14
Peru Peru 97 0% 4
Philippines Philippines 90 -1.1% 11
Papua New Guinea Papua New Guinea 63 -3.08% 26
North Korea North Korea 98 0% 3
Paraguay Paraguay 95 -1.04% 6
Rwanda Rwanda 97 0% 4
Sudan Sudan 79 -2.47% 20
Senegal Senegal 96 0% 5
Solomon Islands Solomon Islands 93 0% 8
Sierra Leone Sierra Leone 90 -3.23% 11
El Salvador El Salvador 94 0% 7
Somalia Somalia 50 -12.3% 28
South Sudan South Sudan 63 -3.08% 26
São Tomé & Príncipe São Tomé & Príncipe 99 0% 2
Suriname Suriname 95 0% 6
Eswatini Eswatini 90 0% 11
Seychelles Seychelles 100 0% 1
Syria Syria 88 -2.22% 12
Chad Chad 73 -2.67% 24
Togo Togo 83 0% 17
Thailand Thailand 96 -3.03% 5
Timor-Leste Timor-Leste 83 -2.35% 17
Tunisia Tunisia 98 0% 3
Turkey Turkey 97 0% 4
Tanzania Tanzania 90 0% 11
Uganda Uganda 81 0% 18
Venezuela Venezuela 69 +2.99% 25
Vietnam Vietnam 96 0% 5
Vanuatu Vanuatu 80 0% 19
Yemen Yemen 76 +4.11% 22
South Africa South Africa 88 0% 12
Zambia Zambia 83 0% 17
Zimbabwe Zimbabwe 90 +1.12% 11 

                    
# 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 = 'SH.VAC.TTNS.ZS'

# 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 <- 'SH.VAC.TTNS.ZS'

# 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))