Country

Indicator

Value type

Values present

Prevalence of moderate or severe food insecurity in the population (%)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	80.9	+2.28%	7
Angola	79.2	+0.892%	11
Albania	32.2	+6.62%	64
United Arab Emirates	10	+2.04%	109
Argentina	36.1	-2.17%	57
Armenia	7.8	-2.5%	117
Antigua & Barbuda	33	0%	62
Australia	12.9	+13.2%	102
Austria	4.9	+14%	132
Azerbaijan	12.2	+20.8%	105
Burundi	70.8		17
Belgium	7.3	+25.9%	119
Benin	63.3	+0.957%	20
Burkina Faso	40.7	+0.494%	49
Bangladesh	30.5	-1.93%	70
Bulgaria	14.8	-6.33%	97
Bahamas	17.2	0%	91
Bosnia & Herzegovina	13.3	-0.746%	100
Belize	45.5	0%	43
Brazil	18.4	-16.7%	89
Barbados	31.1	0%	67
Botswana	54.8	-2.66%	34
Central African Republic	81.3	0%	5
Canada	8.5	+10.4%	114
Switzerland	2.5	+19%	137
Chile	17.6	-3.3%	90
Côte d’Ivoire	39.4	-3.9%	52
Cameroon	59.6	+1.88%	24
Congo - Kinshasa	80.2	+5.67%	8
Congo - Brazzaville	79.9	0%	9
Colombia	30.7	-2.85%	69
Comoros	79.7	0%	10
Cape Verde	34.3	-1.44%	60
Costa Rica	16.2	0%	94
Czechia	10	+17.6%	109
Germany	4	+5.26%	135
Djibouti	49.2	0%	40
Dominica	34.4	0%	59
Denmark	7.1	+4.41%	121
Dominican Republic	46.1	-11.5%	42
Algeria	18.9	-2.58%	88
Ecuador	36.9	-1.07%	55
Egypt	29.8	+4.56%	71
Spain	6.9	-13.8%	123
Estonia	9.3	+9.41%	110
Ethiopia	59	+1.55%	25
Finland	12.6	+20%	103
Fiji	29.2	+20.7%	73
France	7.9	+19.7%	116
United Kingdom	5.7	+39%	128
Georgia	32.4	-11.2%	63
Ghana	42.4	+9.28%	47
Gambia	59	-2.8%	25
Guinea-Bissau	62.5	+0.16%	21
Greece	6.4	+1.59%	125
Grenada	19.9	-5.69%	85
Guatemala	59.8	0%	23
Guyana	25.5	0%	77
Honduras	56	-0.178%	31
Croatia	7.9	-18.6%	116
Haiti	82.8	+0.242%	3
Hungary	15	+19%	96
Indonesia	4.9	0%	132
Ireland	4.2	-22.2%	134
Iran	39.9	-2.21%	51
Iceland	7	+14.8%	122
Italy	2	-9.09%	139
Jamaica	55.1	+1.29%	33
Japan	5.5	+25%	129
Kazakhstan	2.2	-8.33%	138
Kenya	72.8	+0.692%	14
Kyrgyzstan	7	+1.45%	122
Cambodia	50.5	-1.17%	38
Kiribati	41	0%	48
St. Kitts & Nevis	29.8	0%	71
South Korea	5.7	+1.79%	128
Kuwait	8.7	-20.2%	113
Laos	36.3	+6.45%	56
Lebanon	40.1	+9.86%	50
Liberia	81	-0.246%	6
Libya	37.9	-4.77%	53
St. Lucia	22.2	0%	81
Sri Lanka	11.4	+4.59%	106
Lesotho	56.7	0%	30
Lithuania	6.1	-28.2%	126
Luxembourg	2.6	-3.7%	136
Latvia	10.2	+8.51%	108
Moldova	24.7	+5.11%	78
Madagascar	68.6	+5.7%	19
Maldives	13.4	0%	99
Mexico	20.7	-11.9%	82
North Macedonia	20.2	-15.8%	83
Mali	20	-2.91%	84
Malta	8.2	+13.9%	115
Myanmar (Burma)	32	+9.22%	65
Montenegro	12.3	-4.65%	104
Mongolia	5.3	-7.02%	131
Mauritania	61.2	+14%	22
Mauritius	31.2	-2.5%	66
Malawi	81.7	-0.85%	4
Malaysia	16.7	+4.38%	92
Namibia	56.8	-1.56%	29
Niger	50.3	+0.199%	39
Nigeria	73.9	+6.03%	13
Netherlands	5.5	+22.2%	129
Norway	6.8	+30.8%	124
Nepal	37	-1.07%	54
New Zealand	16.4	+8.61%	93
Pakistan	44.9	+6.15%	44
Peru	51.7	-0.193%	37
Philippines	44.1	-1.34%	45
Papua New Guinea	57.3	0%	27
Poland	5.4	-28%	130
Portugal	12.3	-0.806%	104
Paraguay	26.2	+1.16%	76
Romania	19.1	+17.2%	87
Russia	4.6	-4.17%	133
Senegal	29.4	-2.97%	72
Singapore	7.7	+16.7%	118
Sierra Leone	88.6	-0.673%	1
El Salvador	46.9	-3.1%	41
Somalia	79.7	+0.252%	10
Serbia	13	-12.2%	101
South Sudan	87.3	0%	2
São Tomé & Príncipe	54.6	0%	35
Suriname	35.8	0%	58
Slovakia	9	+8.43%	112
Slovenia	7.9	+12.9%	116
Sweden	6	+11.1%	127
Eswatini	55.9	-3.12%	32
Seychelles	14.3	-2.72%	98
Chad	76.6	+1.32%	12
Togo	57	+1.79%	28
Thailand	7.2	+1.41%	120
Tajikistan	28	-9.68%	74
Timor-Leste	53.7	0%	36
Tonga	14.8	-15.9%	97
Trinidad & Tobago	43.3	0%	46
Tunisia	26.7	-6.32%	75
Tanzania	58.2	-0.852%	26
Uganda	71.2	-4.04%	16
Ukraine	31	+9.93%	68
Uruguay	15.7	+3.29%	95
United States	9.1	+5.81%	111
St. Vincent & Grenadines	33.3	0%	61
Vietnam	10.8	+20%	107
Vanuatu	23.3	0%	80
Samoa	23.6	0%	79
Yemen	72.5	+7.89%	15
South Africa	19.4	+1.04%	86
Zimbabwe	70.7	-3.94%	18

Python
R

                    
# 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.MSFI.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.MSFI.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))

Compare

Prevalence of moderate or severe food insecurity in the population (%)

Year: 2022

Acquire full dataset with Python or R from source