Vitamin A supplementation coverage rate (% of children ages 6-59 months)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0 -100% 39
Burundi Burundi 9 -88.9% 33
Benin Benin 4 -95% 37
Burkina Faso Burkina Faso 95 -4.04% 2
Bangladesh Bangladesh 0 -100% 39
Bolivia Bolivia 0 -100% 39
Botswana Botswana 0 -100% 39
Côte d’Ivoire Côte d’Ivoire 72 -15.3% 14
Cameroon Cameroon 87 +2.35% 8
Congo - Kinshasa Congo - Kinshasa 91 +62.5% 5
Congo - Brazzaville Congo - Brazzaville 6 -25% 36
Comoros Comoros 16 +60% 30
Ethiopia Ethiopia 68 -6.85% 16
Gabon Gabon 0 39
Ghana Ghana 41 +17.1% 24
Guinea Guinea 97 +1.04% 1
Gambia Gambia 24 -7.69% 28
Guinea-Bissau Guinea-Bissau 77 12
Equatorial Guinea Equatorial Guinea 7 +40% 35
Kenya Kenya 84 -2.33% 10
Cambodia Cambodia 61 0% 18
Laos Laos 36 +5.88% 25
Liberia Liberia 7 -61.1% 35
Lesotho Lesotho 19 +5.56% 29
Madagascar Madagascar 34 +41.7% 26
Mali Mali 76 -10.6% 13
Mozambique Mozambique 72 -13.3% 14
Mauritania Mauritania 0 39
Malawi Malawi 11 -85.7% 32
Namibia Namibia 60 +1.69% 19
Niger Niger 87 -6.45% 8
Nigeria Nigeria 67 +17.5% 17
Nepal Nepal 94 +4.44% 3
Pakistan Pakistan 86 -6.52% 9
Philippines Philippines 26 -10.3% 27
Papua New Guinea Papua New Guinea 13 -61.8% 31
North Korea North Korea 0 39
Sudan Sudan 1 38
Senegal Senegal 57 0% 20
Sierra Leone Sierra Leone 69 +4.55% 15
Somalia Somalia 79 -5.95% 11
South Sudan South Sudan 89 0% 7
São Tomé & Príncipe São Tomé & Príncipe 8 -87.9% 34
Eswatini Eswatini 51 +54.5% 22
Chad Chad 93 4
Togo Togo 90 -6.25% 6
Tajikistan Tajikistan 93 -2.11% 4
Turkmenistan Turkmenistan 0 39
Tanzania Tanzania 90 -6.25% 6
Uganda Uganda 55 +44.7% 21
Yemen Yemen 11 +83.3% 32
South Africa South Africa 44 +4.76% 23
Zambia Zambia 97 +2.11% 1
Zimbabwe Zimbabwe 68 +325% 16 

                    
# 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 = 'SN.ITK.VITA.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 <- 'SN.ITK.VITA.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))