Country

Indicator

Value type

Values present

Antiretroviral therapy coverage (% of people living with HIV)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	9	-10%	66
Angola	46	+12.2%	49
Albania	57	+7.55%	39
United Arab Emirates	47	+2.17%	48
Armenia	53	+10.4%	43
Azerbaijan	61	0%	35
Burundi	85	+2.41%	11
Benin	81	+9.46%	15
Burkina Faso	81	+8%	15
Bangladesh	38	+26.7%	56
Bulgaria	60	+7.14%	36
Bahamas	76	0%	20
Belarus	79	+5.33%	17
Belize	44	-4.35%	51
Bolivia	52	0%	44
Brazil	74	+1.37%	22
Barbados	65	-1.52%	31
Bhutan	57	+7.55%	39
Botswana	93	+1.09%	3
Central African Republic	49	+6.52%	47
Chile	74	0%	22
Côte d’Ivoire	72	+2.86%	24
Cameroon	88	+10%	8
Congo - Kinshasa	82	+10.8%	14
Congo - Brazzaville	24	+9.09%	63
Comoros	85	+10.4%	11
Cape Verde	87	+8.75%	9
Cuba	67	+4.69%	29
Denmark	89	+5.95%	7
Dominican Republic	63	+12.5%	33
Algeria	63	-4.55%	33
Ecuador	80	+8.11%	16
Egypt	47	+11.9%	48
Eritrea	72	+4.35%	24
Ethiopia	83	+6.41%	13
Fiji	28	-22.2%	61
Gabon	59	+13.5%	37
Georgia	72	+1.41%	24
Ghana	63	+12.5%	33
Guinea	64	+14.3%	32
Gambia	37	+12.1%	57
Guinea-Bissau	64	+12.3%	32
Equatorial Guinea	42	+7.69%	52
Greece	70	0%	26
Guatemala	77	+4.05%	19
Guyana	67	+3.08%	29
Honduras	67	+6.35%	29
Croatia	79	+2.6%	17
Haiti	77	+2.67%	19
Indonesia	33	+17.9%	58
India	68		28
Iran	37	+5.71%	57
Iraq	30	+15.4%	60
Iceland	85	+1.19%	11
Jamaica	50	+6.38%	46
Jordan	50	+13.6%	46
Kenya	94	+1.08%	2
Kyrgyzstan	54	+8%	42
Cambodia	86	+4.88%	10
Kuwait	93	+8.14%	3
Laos	58	+9.43%	38
Lebanon	80	+11.1%	16
Liberia	72	+18%	24
Libya	64	+3.23%	32
Sri Lanka	68	+15.3%	28
Lesotho	86	+2.38%	10
Lithuania	41	+10.8%	53
Luxembourg	89	+9.88%	7
Latvia	42	0%	52
Morocco	74	+2.78%	22
Moldova	49	+6.52%	47
Madagascar	18	+28.6%	64
Maldives	67	-4.29%	29
Mexico	62	+3.33%	34
Mali	50	+11.1%	46
Malta	65	+4.84%	31
Myanmar (Burma)	74	+7.25%	22
Montenegro	57	+11.8%	39
Mongolia	40	+5.26%	54
Mozambique	81	+14.1%	15
Mauritania	45	+2.27%	50
Malawi	93	+4.49%	3
Malaysia	55	0%	41
Namibia	91	+3.41%	5
Niger	75	+4.17%	21
Nicaragua	56	+12%	40
Nepal	78	+8.33%	18
New Zealand	85	+2.41%	11
Oman	71	+7.58%	25
Pakistan	13	+18.2%	65
Panama	66	+26.9%	30
Peru	82	+5.13%	14
Philippines	41	+7.89%	53
Papua New Guinea	61	+7.02%	35
Paraguay	56	+5.66%	40
Qatar	41	+2.5%	53
Romania	68	-2.86%	28
Rwanda	92	+3.37%	4
Saudi Arabia	90	0%	6
Sudan	28	0%	61
Senegal	80	+5.26%	16
Sierra Leone	76	+24.6%	20
El Salvador	66	+1.54%	30
Serbia	63	+1.61%	33
South Sudan	32	+10.3%	59
São Tomé & Príncipe	89	+11.3%	7
Suriname	44	-6.38%	51
Slovenia	84	+1.2%	12
Eswatini	95	+6.74%	1
Syria	51	0%	45
Chad	77	+6.94%	19
Togo	82	+7.89%	14
Thailand	81	+2.53%	15
Tajikistan	63	+1.61%	33
Timor-Leste	69	+25.5%	27
Trinidad & Tobago	60	-1.64%	36
Tunisia	26	+18.2%	62
Tanzania	94	+6.82%	2
Uganda	84	+1.2%	12
Uruguay	74	+5.71%	22
Venezuela	67	+1.52%	29
Vietnam	73	+2.82%	23
Yemen	39	+11.4%	55
South Africa	75	+1.35%	21
Zambia	90	+5.88%	6
Zimbabwe	94	+4.44%	2

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.HIV.ARTC.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.HIV.ARTC.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

Antiretroviral therapy coverage (% of people living with HIV)

Year: 2022

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