Prevalence of severe food insecurity in the population (%)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 30.6 +7.75% 13
Angola Angola 31.9 +2.24% 12
Albania Albania 8.2 +9.33% 53
United Arab Emirates United Arab Emirates 1.4 +16.7% 96
Argentina Argentina 13.1 0% 39
Armenia Armenia 0 105
Antigua & Barbuda Antigua & Barbuda 7.1 0% 58
Australia Australia 4.2 +23.5% 72
Austria Austria 1.8 +12.5% 93
Azerbaijan Azerbaijan 0.7 103
Burundi Burundi 20.9 26
Belgium Belgium 2 +33.3% 91
Benin Benin 15.8 +2.6% 33
Burkina Faso Burkina Faso 7.2 +1.41% 57
Bangladesh Bangladesh 11.4 +3.64% 43
Bulgaria Bulgaria 2.5 -28.6% 86
Bahamas Bahamas 3.4 0% 78
Bosnia & Herzegovina Bosnia & Herzegovina 2.8 -9.68% 83
Belize Belize 5.9 0% 65
Brazil Brazil 6.6 -22.4% 61
Barbados Barbados 7.4 0% 56
Botswana Botswana 26.4 -1.12% 19
Central African Republic Central African Republic 61.8 0% 2
Canada Canada 1.5 +25% 95
Switzerland Switzerland 1.1 +83.3% 99
Chile Chile 3.7 +2.78% 75
Côte d’Ivoire Côte d’Ivoire 8.9 -5.32% 50
Cameroon Cameroon 25.4 -4.87% 22
Congo - Kinshasa Congo - Kinshasa 41.7 +3.47% 6
Congo - Brazzaville Congo - Brazzaville 38.3 0% 7
Colombia Colombia 5.3 0% 68
Comoros Comoros 27.4 0% 15
Cape Verde Cape Verde 6 -1.64% 64
Costa Rica Costa Rica 2.8 -3.45% 83
Czechia Czechia 2.2 -4.35% 89
Germany Germany 1.5 +7.14% 95
Djibouti Djibouti 16.5 0% 32
Dominica Dominica 5.8 0% 66
Denmark Denmark 1.9 +5.56% 92
Dominican Republic Dominican Republic 19 -13.6% 30
Algeria Algeria 5.6 0% 67
Ecuador Ecuador 12.7 -2.31% 40
Egypt Egypt 10.4 +18.2% 46
Spain Spain 1.5 -16.7% 95
Estonia Estonia 1 +42.9% 100
Ethiopia Ethiopia 19.7 -6.64% 29
Finland Finland 3 +15.4% 81
Fiji Fiji 8.5 +34.9% 51
France France 2.3 +43.8% 88
United Kingdom United Kingdom 2.5 +56.3% 86
Georgia Georgia 7.5 -22.7% 55
Ghana Ghana 8.2 +17.1% 53
Gambia Gambia 25.5 -5.56% 21
Guinea-Bissau Guinea-Bissau 9 +1.12% 49
Greece Greece 1.5 0% 95
Grenada Grenada 5.8 -12.1% 66
Guatemala Guatemala 21.1 0% 25
Guyana Guyana 4.7 0% 70
Honduras Honduras 26.9 +14.5% 17
Croatia Croatia 1.4 -26.3% 96
Haiti Haiti 42.4 -1.17% 5
Hungary Hungary 3.6 +20% 76
Indonesia Indonesia 0 105
Ireland Ireland 1.6 -33.3% 94
Iran Iran 6.4 -13.5% 62
Iceland Iceland 1.9 +18.8% 92
Italy Italy 0 105
Jamaica Jamaica 26.6 +3.91% 18
Japan Japan 1.2 +33.3% 98
Kazakhstan Kazakhstan 0.6 +20% 104
Kenya Kenya 28 0% 14
Kyrgyzstan Kyrgyzstan 1.1 0% 99
Cambodia Cambodia 13.9 -6.08% 37
Kiribati Kiribati 8 0% 54
St. Kitts & Nevis St. Kitts & Nevis 5.6 0% 67
South Korea South Korea 0.9 +12.5% 101
Kuwait Kuwait 3.5 -22.2% 77
Laos Laos 6.2 -13.9% 63
Lebanon Lebanon 11.7 -7.14% 41
Liberia Liberia 37.3 -0.533% 8
Libya Libya 19.9 -6.13% 28
St. Lucia St. Lucia 4.5 0% 71
Sri Lanka Sri Lanka 1.2 0% 98
Lesotho Lesotho 32.8 0% 10
Lithuania Lithuania 1.3 -38.1% 97
Luxembourg Luxembourg 0.6 0% 104
Latvia Latvia 1.5 +50% 95
Moldova Moldova 5.3 +10.4% 68
Madagascar Madagascar 14.9 +22.1% 35
Maldives Maldives 2.2 0% 89
Mexico Mexico 3 -6.25% 81
North Macedonia North Macedonia 4.8 -30.4% 69
Mali Mali 2.7 0% 84
Malta Malta 2 +5.26% 91
Myanmar (Burma) Myanmar (Burma) 6.9 +38% 59
Montenegro Montenegro 2.5 -24.2% 86
Mongolia Mongolia 0 105
Mauritania Mauritania 11.6 +22.1% 42
Mauritius Mauritius 10.2 -2.86% 48
Malawi Malawi 53.5 +2.49% 3
Malaysia Malaysia 5.8 -3.33% 66
Namibia Namibia 31.9 -3.33% 12
Niger Niger 7.5 -2.6% 55
Nigeria Nigeria 22.6 +6.1% 24
Netherlands Netherlands 1.9 +35.7% 92
Norway Norway 1.4 +16.7% 96
Nepal Nepal 13.5 +2.27% 38
New Zealand New Zealand 3.8 +15.2% 74
Pakistan Pakistan 15.1 +17.1% 34
Peru Peru 20.3 -1.93% 27
Philippines Philippines 5.9 +3.51% 65
Papua New Guinea Papua New Guinea 27 0% 16
Poland Poland 0.9 -10% 101
Portugal Portugal 3.3 -15.4% 79
Paraguay Paraguay 6.6 +8.2% 61
Romania Romania 7.1 +24.6% 58
Russia Russia 0 105
Senegal Senegal 4 -11.1% 73
Singapore Singapore 2.5 +47.1% 86
Sierra Leone Sierra Leone 32.3 +1.25% 11
El Salvador El Salvador 15.8 -2.47% 33
Somalia Somalia 43.5 +0.23% 4
Serbia Serbia 3 -26.8% 81
South Sudan South Sudan 63.2 0% 1
São Tomé & Príncipe São Tomé & Príncipe 14.1 0% 36
Suriname Suriname 7.2 0% 57
Slovakia Slovakia 2 +11.1% 91
Slovenia Slovenia 0.9 0% 101
Sweden Sweden 1.8 +28.6% 93
Eswatini Eswatini 17.2 +10.3% 31
Seychelles Seychelles 3.2 -3.03% 80
Chad Chad 36.4 +1.68% 9
Togo Togo 10.9 -14.8% 45
Thailand Thailand 1.4 +7.69% 96
Tajikistan Tajikistan 6.7 -19.3% 60
Timor-Leste Timor-Leste 8.9 0% 50
Tonga Tonga 2.6 -29.7% 85
Trinidad & Tobago Trinidad & Tobago 10.2 0% 48
Tunisia Tunisia 11.3 -10.3% 44
Tanzania Tanzania 25.4 -3.42% 22
Uganda Uganda 23 -7.63% 23
Ukraine Ukraine 5.3 +23.3% 68
Uruguay Uruguay 2.9 0% 82
United States United States 0.8 +14.3% 102
St. Vincent & Grenadines St. Vincent & Grenadines 10.3 0% 47
Vietnam Vietnam 2.1 +75% 90
Vanuatu Vanuatu 2.4 0% 87
Samoa Samoa 3.4 0% 78
South Africa South Africa 8.4 0% 52
Zimbabwe Zimbabwe 26 -9.09% 20 

                    
# 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.SVFI.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.SVFI.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))