Country

Indicator

Value type

Values present

Young people (ages 15-24) newly infected with HIV

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	500	0%	30
Angola	4,600	-2.13%	12
Albania	100	0%	32
Armenia	100	0%	32
Azerbaijan	100	0%	32
Burundi	500	0%	30
Belgium	100	0%	32
Benin	500	0%	30
Burkina Faso	500	0%	30
Bangladesh	500	0%	30
Bulgaria	100	0%	32
Bahamas	100	0%	32
Belarus	200	0%	31
Belize	100	0%	32
Bolivia	500	0%	30
Barbados	100	0%	32
Bhutan	100	0%	32
Botswana	1,400	-12.5%	26
Central African Republic	3,000	-9.09%	16
Chile	1,100	0%	28
Côte d’Ivoire	1,900	-13.6%	22
Cameroon	2,600	-21.2%	18
Congo - Kinshasa	2,900	-21.6%	17
Congo - Brazzaville	5,000	+11.1%	10
Colombia	1,000	0%	29
Comoros	100	0%	32
Cape Verde	100	0%	32
Costa Rica	500	0%	30
Cuba	1,000	0%	29
Czechia	100	0%	32
Denmark	100	0%	32
Dominican Republic	1,200	-7.69%	27
Algeria	500	0%	30
Ecuador	1,000	0%	29
Egypt	1,000	0%	29
Eritrea	100	0%	32
Estonia	100	0%	32
Ethiopia	2,400	-11.1%	19
Fiji	100	0%	32
France	1,000	0%	29
Gabon	1,000	0%	29
Georgia	100	0%	32
Ghana	4,600	-9.8%	12
Guinea	1,800	-14.3%	23
Gambia	500	0%	30
Guinea-Bissau	500	0%	30
Equatorial Guinea	1,100	0%	28
Greece	100	0%	32
Grenada	100	0%	32
Guatemala	500	0%	30
Guyana	100	0%	32
Honduras	100	0%	32
Croatia	100	0%	32
Haiti	1,700	0%	24
Indonesia	12,000	-7.69%	4
Iran	1,000	+100%	29
Iraq	200	0%	31
Iceland	100	0%	32
Italy	200	0%	31
Jamaica	500	0%	30
Jordan	100	0%	32
Kenya	7,300	-18.9%	8
Kyrgyzstan	100	0%	32
Cambodia	1,000	0%	29
St. Kitts & Nevis	100	0%	32
Laos	500	0%	30
Liberia	500	-50%	30
Sri Lanka	100	0%	32
Lesotho	1,700	-19%	24
Lithuania	100	0%	32
Luxembourg	100	0%	32
Latvia	100	0%	32
Morocco	100	0%	32
Moldova	200	0%	31
Madagascar	1,600	0%	25
Maldives	100	0%	32
Mexico	8,600	0%	7
North Macedonia	100	0%	32
Mali	1,900	0%	22
Malta	100	0%	32
Myanmar (Burma)	5,800	+1.75%	9
Montenegro	100	0%	32
Mongolia	100	0%	32
Mozambique	35,000	-18.6%	2
Mauritania	100	0%	32
Malawi	3,800	-17.4%	14
Malaysia	1,000	0%	29
Namibia	2,000	-13%	21
Niger	200	0%	31
Nicaragua	500	0%	30
Nepal	100	0%	32
New Zealand	100	0%	32
Oman	100	0%	32
Panama	1,000	0%	29
Peru	1,000	0%	29
Philippines	11,000	+10%	5
Papua New Guinea	1,700	+6.25%	24
Portugal	200	0%	31
Qatar	100	0%	32
Romania	100	0%	32
Rwanda	1,000	0%	29
Saudi Arabia	500	+150%	30
Sudan	1,000	0%	29
Senegal	500	+150%	30
Sierra Leone	1,200	-20%	27
El Salvador	500	0%	30
Serbia	100	0%	32
South Sudan	3,400	0%	15
São Tomé & Príncipe	100	0%	32
Suriname	100	0%	32
Slovakia	100	0%	32
Eswatini	1,700	-19%	24
Syria	100	0%	32
Chad	1,000	-16.7%	29
Togo	1,000	0%	29
Thailand	4,400	-2.22%	13
Tajikistan	200	0%	31
Timor-Leste	100	0%	32
Tanzania	9,600	-20%	6
Uganda	19,000	-5%	3
Uruguay	200	-60%	31
Vietnam	2,100	-4.55%	20
Yemen	500	0%	30
South Africa	56,000	-5.08%	1
Zambia	12,000	-14.3%	4
Zimbabwe	4,900	-5.77%	11

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.YG'

# 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.YG'

# 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

Young people (ages 15-24) newly infected with HIV

Year: 2022

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