Country

Indicator

Value type

Values present

People practicing open defecation, urban (% of urban population)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	0		83
Angola	0		83
Albania	0		83
Andorra	0		83
United Arab Emirates	0		83
Argentina	0		83
Armenia	0		83
Antigua & Barbuda	0.698	0%	51
Austria	0		83
Azerbaijan	0		83
Burundi	0.0793	0%	72
Belgium	0		83
Benin	31.2	-0.746%	2
Burkina Faso	5.55	-3.3%	16
Bangladesh	0		83
Bulgaria	0		83
Bosnia & Herzegovina	0		83
Belarus	0		83
Belize	0.722	0%	50
Bermuda	0		83
Bolivia	0		83
Brazil	0		83
Bhutan	0		83
Botswana	1.21	0%	36
Central African Republic	6.84	0%	14
Canada	0		83
Switzerland	0		83
Chile	0		83
China	0.164	-0.197%	65
Côte d’Ivoire	6.5	+0.295%	15
Cameroon	0.822	0%	47
Congo - Kinshasa	3.9	0%	23
Colombia	0.671	-7.1%	52
Cape Verde	4.19	0%	21
Costa Rica	0.086	-17.8%	71
Cuba	0.0878	0%	69
Cayman Islands	0		83
Cyprus	0		83
Czechia	0		83
Germany	0		83
Djibouti	2.71	0%	27
Denmark	0		83
Dominican Republic	1.19	-4.78%	37
Algeria	0		83
Ecuador	0		83
Egypt	0.0159	0%	78
Spain	0		83
Estonia	0		83
Ethiopia	3.28	0%	26
Finland	0		83
Fiji	0		83
France	0		83
Gabon	1.88	+0.516%	31
United Kingdom	0		83
Georgia	0.00548	+5.5%	81
Ghana	8.44	+0.258%	10
Gibraltar	0		83
Guinea	0.925	-3.77%	43
Gambia	0.102	-27.1%	68
Guinea-Bissau	0.417	0%	57
Greece	0		83
Guatemala	0.607	-18.5%	53
Guyana	0.0484	-34.3%	76
Hong Kong SAR China	0		83
Honduras	1.28	-8.05%	34
Haiti	8.12	0%	11
Hungary	0		83
Indonesia	2.18	-19.3%	28
India	0.555	-67.5%	54
Ireland	0		83
Iran	0		83
Iraq	0.00627	0%	80
Iceland	0		83
Israel	0		83
Italy	0		83
Jamaica	0.962	0%	42
Jordan	0.078	0%	73
Kazakhstan	0		83
Kenya	0.865	-10.4%	46
Kyrgyzstan	0		83
Cambodia	0		83
Kiribati	21.7	-1.13%	3
Kuwait	0		83
Laos	0		83
Liberia	15.7	-3.56%	6
St. Lucia	2.12	0%	29
Sri Lanka	0		83
Lesotho	3.84	-5.72%	24
Lithuania	0		83
Luxembourg	0		83
Macao SAR China	0		83
Saint Martin (French part)	0		83
Morocco	0		83
Monaco	0		83
Moldova	0		83
Madagascar	16.7	-0.763%	5
Maldives	0		83
Mexico	0	-100%	83
Marshall Islands	5.25	+2.75%	17
North Macedonia	0		83
Mali	1.15	-8.08%	38
Malta	0		83
Myanmar (Burma)	0.813	0%	48
Montenegro	0		83
Mongolia	0.346	-13.5%	58
Mozambique	5.21	-14.2%	18
Mauritania	5.05	-12.1%	19
Mauritius	0.27	+4.25%	59
Malawi	1.1	-4.96%	39
Malaysia	0		83
Namibia	20.5	0%	4
Niger	9.59	-5.63%	9
Nigeria	7.37	-3.4%	13
Netherlands	0		83
Norway	0		83
Nepal	3.45	0%	25
New Zealand	0		83
Oman	0		83
Pakistan	0.141	-59.1%	67
Panama	0.441	0%	56
Peru	1.05	-21.2%	41
Philippines	1.61	-9.8%	33
Palau	0		83
Papua New Guinea	4.05	0%	22
Poland	0		83
North Korea	0		83
Portugal	0		83
Paraguay	0	-100%	83
Palestinian Territories	0		83
Romania	0		83
Russia	0		83
Rwanda	1.26	0%	35
Saudi Arabia	0		83
Senegal	1.08	-9.59%	40
Singapore	0		83
Sierra Leone	4.22	-2.71%	20
El Salvador	0		83
Somalia	0.178	0%	64
Serbia	0.0479	0%	77
South Sudan	8.06	0%	12
São Tomé & Príncipe	38.6	0%	1
Suriname	0.182	0%	63
Slovakia	0		83
Sweden	0		83
Eswatini	0.923	-4.82%	44
Syria	0.0527	0%	75
Turks & Caicos Islands	0		83
Chad	15.4	0%	7
Togo	11.4	-4.4%	8
Thailand	0.0579	0.000000%	74
Tajikistan	0		83
Turkmenistan	0		83
Timor-Leste	0		83
Tonga	0.0871	+0.548%	70
Tunisia	0.21	-5.51%	62
Turkey	0.00192	0%	82
Tuvalu	2.11	-6.91%	30
Tanzania	0.452	-18.8%	55
Uganda	1.77	-0.643%	32
Ukraine	0		83
Uruguay	0.159	0%	66
United States	0		83
Uzbekistan	0		83
Vietnam	0		83
Vanuatu	0.216	-4.68%	61
Samoa	0.0126	+6.59%	79
Yemen	0.898	0%	45
South Africa	0.218	-35%	60
Zambia	0.784	0%	49
Zimbabwe	0		83

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 = 'SH.STA.ODFC.UR.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.STA.ODFC.UR.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

People practicing open defecation, urban (% of urban population)

Year: 2022

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