Country

Indicator

Value type

Values present

Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	0.06	+20%	60
Angola	0.7	-6.67%	22
Albania	0.04	0%	62
Argentina	0.16	-5.88%	50
Armenia	0.37	+2.78%	38
Azerbaijan	0.09	0%	57
Burundi	0.14	-6.67%	52
Belgium	0.08	0%	58
Benin	0.13	-18.8%	53
Burkina Faso	0.09	-18.2%	57
Bangladesh	0.01	0%	65
Bulgaria	0.07	0%	59
Bahamas	0.22	-12%	45
Belarus	0.2	-4.76%	46
Belize	0.56	-5.08%	27
Bolivia	0.28	-3.45%	41
Brazil	0.39	0%	36
Barbados	0.41	-4.65%	34
Bhutan	0.12	0%	54
Botswana	3.32	-12.4%	8
Central African Republic	2.65	-9.86%	10
Chile	0.45	0%	32
Côte d’Ivoire	0.45	-13.5%	32
Cameroon	0.46	-24.6%	31
Congo - Kinshasa	0.15	-21.1%	51
Congo - Brazzaville	4.76	+5.08%	5
Colombia	0.29	-12.1%	40
Cape Verde	0.55	-5.17%	28
Costa Rica	0.25	0%	43
Cuba	0.37	0%	38
Czechia	0.03	0%	63
Dominican Republic	0.59	-10.6%	26
Algeria	0.08	0%	58
Ecuador	0.18	-5.26%	48
Egypt	0.08	0%	58
Eritrea	0.09	-10%	57
Estonia	0.19	-5%	47
Ethiopia	0.12	-7.69%	54
Fiji	0.54	+10.2%	29
France	0.2	0%	46
Gabon	1.3	-9.72%	14
Georgia	0.28	0%	41
Ghana	0.81	-12.9%	20
Guinea	0.63	-14.9%	24
Gambia	0.95	-3.06%	17
Guinea-Bissau	0.84	-11.6%	19
Equatorial Guinea	4.7	-1.47%	6
Greece	0.17	+30.8%	49
Guatemala	0.1	-9.09%	56
Guyana	1.06	+3.92%	15
Honduras	0.08	0%	58
Croatia	0.04	0%	62
Haiti	0.81	-1.22%	20
Indonesia	0.15	-6.25%	51
Iran	0.05	0%	61
Iraq	0.03	+50%	63
Iceland	0.08	0%	58
Italy	0.08	0%	58
Jamaica	0.76	-9.52%	21
Jordan	0.01	0%	65
Kenya	0.68	-19%	23
Kyrgyzstan	0.2	0%	46
Cambodia	0.15	0%	51
Laos	0.23	0%	44
Liberia	0.37	-15.9%	38
Sri Lanka	0.01	0%	65
Lesotho	5.04	-20.5%	4
Lithuania	0.12	-7.69%	54
Luxembourg	0.12	-7.69%	54
Latvia	0.4	-2.44%	35
Morocco	0.03	-25%	63
Moldova	0.68	+1.49%	23
Madagascar	0.5	-1.96%	30
Maldives	0.01	0%	65
Mexico	0.28	0%	41
North Macedonia	0.05	0%	61
Mali	0.42	-2.33%	33
Malta	0.17	0%	49
Myanmar (Burma)	0.35	+2.94%	39
Montenegro	0.05	-16.7%	61
Mongolia	0.02	0%	64
Mozambique	5.54	-20.4%	2
Mauritania	0.19	0%	47
Malawi	1.3	-20.2%	14
Malaysia	0.15	-6.25%	51
Namibia	4.2	-12.1%	7
Niger	0.08	0%	58
Nicaragua	0.13	-7.14%	53
Nepal	0.02	-33.3%	64
Oman	0.05	0%	61
Panama	0.6	-1.64%	25
Peru	0.29	-6.45%	40
Philippines	0.39	+11.4%	36
Papua New Guinea	0.99	+4.21%	16
Qatar	0.05	0%	61
Romania	0.05	-16.7%	61
Rwanda	0.38	-13.6%	37
Saudi Arabia	0.06	+20%	60
Sudan	0.14	+7.69%	52
Senegal	0.13	0%	53
Sierra Leone	0.54	-25%	29
El Salvador	0.23	0%	44
Serbia	0.05	0%	61
São Tomé & Príncipe	0.07	-22.2%	59
Suriname	1.31	+9.17%	13
Slovakia	0.04	0%	62
Eswatini	7.68	-20.3%	1
Syria	0.01	0%	65
Chad	0.28	-15.2%	41
Togo	0.38	-9.52%	37
Thailand	0.27	0%	42
Tajikistan	0.17	-5.56%	49
Timor-Leste	0.12	-7.69%	54
Tanzania	0.88	-21.4%	18
Uganda	2.19	-6.81%	11
Uruguay	0.45	0%	32
Vietnam	0.11	-8.33%	55
Yemen	0.04	0%	62
South Africa	5.21	-8.27%	3
Zambia	3.25	-16.5%	9
Zimbabwe	1.68	-9.19%	12

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.INCD.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.INCD.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

Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)

Year: 2022

Acquire full dataset with Python or R from source