People with basic handwashing facilities including soap and water (% of population)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 48.2 +1.79% 42
Armenia Armenia 94.4 +0.00984% 5
Burundi Burundi 6.3 +0.879% 75
Benin Benin 12.1 +0.404% 71
Burkina Faso Burkina Faso 9.16 +0.814% 73
Bangladesh Bangladesh 61.7 +0.126% 38
Belize Belize 90.3 +0.00746% 11
Bolivia Bolivia 27 +0.0947% 52
Bhutan Bhutan 93.2 -0.0477% 9
Central African Republic Central African Republic 22.2 +0.498% 59
China China 97.2 +0.0324% 2
Côte d’Ivoire Côte d’Ivoire 21.8 +0.449% 60
Cameroon Cameroon 36.7 +0.4% 47
Congo - Kinshasa Congo - Kinshasa 19.4 +0.468% 63
Colombia Colombia 70.2 +0.201% 35
Costa Rica Costa Rica 86 +0.0232% 16
Cuba Cuba 93.4 +0.00796% 8
Dominican Republic Dominican Republic 48.3 +0.235% 41
Algeria Algeria 84.8 +0.0764% 20
Ecuador Ecuador 87.2 +0.0324% 13
Ethiopia Ethiopia 8.32 +0.911% 74
Fiji Fiji 86.5 +0.0621% 14
Georgia Georgia 91.8 +0.0332% 10
Ghana Ghana 41.7 +0.176% 45
Guinea Guinea 20.6 +0.394% 61
Gambia Gambia 12.9 +0.0769% 70
Guinea-Bissau Guinea-Bissau 19.7 +0.19% 62
Guyana Guyana 83.5 +0.907% 24
Honduras Honduras 85 +0.0078% 19
Haiti Haiti 22.6 +0.461% 57
Indonesia Indonesia 79 +0.22% 28
India India 76.3 +2.74% 29
Iraq Iraq 97.4 +0.00176% 1
Kenya Kenya 37.6 +0.138% 46
Cambodia Cambodia 83.4 +2.64% 25
Kiribati Kiribati 55.7 +0.0957% 40
Liberia Liberia 3.44 +8.46% 78
Sri Lanka Sri Lanka 85.3 +0.0194% 17
Lesotho Lesotho 5.57 +0.564% 77
Madagascar Madagascar 23.4 -0.177% 56
Mexico Mexico 93.9 +0.39% 7
Mali Mali 17.3 +0.797% 68
Myanmar (Burma) Myanmar (Burma) 74.6 +0.0536% 31
Mongolia Mongolia 86.4 +0.0127% 15
Mauritania Mauritania 41.8 -3.18% 44
Malawi Malawi 15.3 +0.0354% 69
Niger Niger 24.6 +0.102% 55
Nigeria Nigeria 31.1 +0.383% 48
Nepal Nepal 63.5 +0.104% 37
Pakistan Pakistan 84.7 +3.53% 21
Philippines Philippines 81.8 +0.0242% 26
Papua New Guinea Papua New Guinea 29.9 +0.16% 50
Palestinian Territories Palestinian Territories 94.9 -0.00232% 4
Rwanda Rwanda 18.4 +0.111% 64
Sudan Sudan 10.8 0% 72
Senegal Senegal 22.2 +0.534% 58
Sierra Leone Sierra Leone 17.8 +2.65% 66
Somalia Somalia 25.1 +0.305% 54
South Sudan South Sudan 5.6 0% 76
São Tomé & Príncipe São Tomé & Príncipe 58.1 +0.232% 39
Suriname Suriname 72.1 +0.00972% 33
Syria Syria 84.4 +0.0515% 22
Turks & Caicos Islands Turks & Caicos Islands 95 +0.0148% 3
Chad Chad 26.2 +0.112% 53
Togo Togo 17.3 +0.558% 67
Thailand Thailand 85 +0.03% 18
Tajikistan Tajikistan 73 +0.0636% 32
Tonga Tonga 69.5 +0.00427% 36
Tunisia Tunisia 84.3 +0.0934% 23
Tuvalu Tuvalu 94 -0.0284% 6
Tanzania Tanzania 28.9 +0.457% 51
Uganda Uganda 30.9 +4.86% 49
Uzbekistan Uzbekistan 81.6 +0.00615% 27
Vietnam Vietnam 88.7 +0.365% 12
Vanuatu Vanuatu 75.9 +6.13% 30
Samoa Samoa 72 -0.0251% 34
Zambia Zambia 18.2 +0.615% 65
Zimbabwe Zimbabwe 42.5 +0.0425% 43 

                    
# 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.HYGN.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.HYGN.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))