Births attended by skilled health staff (% of total)

Source: worldbank.org, 01.09.2025

Year: 2020

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 61.8 +5.1% 32
Argentina Argentina 98.8 -0.803% 12
Antigua & Barbuda Antigua & Barbuda 99 -1% 11
Australia Australia 96 -2.83% 22
Austria Austria 98.1 -0.305% 16
Azerbaijan Azerbaijan 99.9 0% 2
Burundi Burundi 87.4 -11.4% 28
Bulgaria Bulgaria 94.6 +2.49% 24
Belarus Belarus 99.9 0% 2
Belize Belize 94.6 -0.735% 24
Brazil Brazil 98.4 0% 14
Barbados Barbados 98.4 +0.408% 14
Brunei Brunei 99.7 +0.1% 4
Bhutan Bhutan 99.3 +3.12% 8
Canada Canada 98 0% 17
Dominica Dominica 100 0% 1
Ecuador Ecuador 99.4 +2.26% 7
Spain Spain 99.9 0% 2
Estonia Estonia 99.6 -0.1% 5
Finland Finland 99.6 +0.101% 5
France France 98.2 +0.615% 15
Georgia Georgia 99.8 0% 3
Gambia Gambia 83.8 +1.33% 30
Guyana Guyana 97.6 +2.74% 18
Iceland Iceland 97.4 -0.916% 19
Italy Italy 99.8 0% 3
Japan Japan 99.9 0% 2
Kyrgyzstan Kyrgyzstan 100 +0.2% 1
St. Kitts & Nevis St. Kitts & Nevis 100 0% 1
Liberia Liberia 84.4 +38.1% 29
Lithuania Lithuania 100 0% 1
Moldova Moldova 99.6 -0.1% 5
North Macedonia North Macedonia 100 0% 1
Malta Malta 99.9 +0.201% 2
Mauritius Mauritius 99.7 -0.1% 4
Malawi Malawi 96.4 +7.35% 21
Norway Norway 99.2 -0.101% 9
Oman Oman 99.9 +1.32% 2
Pakistan Pakistan 68 -7.73% 31
Peru Peru 95.7 +1.38% 23
Palau Palau 97.2 -2.8% 20
Poland Poland 99.7 -0.1% 4
Portugal Portugal 98.6 -1.3% 13
Palestinian Territories Palestinian Territories 99.7 +0.1% 4
Romania Romania 93 +1.64% 26
Russia Russia 99.6 -0.1% 5
Rwanda Rwanda 94.2 +3.86% 25
Singapore Singapore 99.6 0% 5
South Sudan South Sudan 39.7 +105% 33
Slovakia Slovakia 98.2 -0.102% 15
Seychelles Seychelles 99.5 +0.709% 6
Turks & Caicos Islands Turks & Caicos Islands 100 0% 1
Tuvalu Tuvalu 99.5 +6.87% 6
Uruguay Uruguay 100 0% 1
United States United States 99.1 +0.101% 10
Uzbekistan Uzbekistan 100 0% 1
Samoa Samoa 88.9 +7.76% 27 

                    
# 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.BRTC.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.BRTC.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))