Country

Indicator

Value type

Values present

Incidence of malaria (per 1,000 population at risk)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	13.3	+44.3%	48
Angola	225	-5.34%	21
United Arab Emirates	0		79
Argentina	0		79
Armenia	0		79
Burundi	245	-8.43%	15
Benin	363	-3.84%	1
Burkina Faso	353	-0.78%	2
Bangladesh	1.1	-10.6%	63
Belize	0		79
Bolivia	2.51	-1.57%	57
Brazil	3.81	+18.7%	54
Bhutan	0		79
Botswana	0.47	+38.2%	67
Central African Republic	306	-0.271%	6
Côte d’Ivoire	251	-5.64%	14
Cameroon	259	-1.58%	11
Congo - Kinshasa	313	-1.28%	4
Congo - Brazzaville	215	-0.093%	22
Colombia	10.6	+19%	49
Comoros	24.8	-0.121%	44
Cape Verde	0		79
Costa Rica	0.3	+30.4%	69
Djibouti	45	-5.52%	40
Dominican Republic	0.04	-20%	76
Algeria	0		79
Ecuador	0.06	-53.8%	75
Egypt	0		79
Eritrea	63.4	+55%	35
Ethiopia	109	+82.4%	30
Gabon	229	+1.21%	18
Ghana	194	-8.41%	25
Guinea	308	-1%	5
Gambia	87.5	-8.39%	32
Guinea-Bissau	104	-6.19%	31
Equatorial Guinea	229	-2.64%	19
Guatemala	0.22	+57.1%	73
Guyana	42.7	+32.4%	41
Honduras	0.27	-27%	71
Haiti	2.29	-10.2%	58
Indonesia	3.88	-6.28%	53
India	1.52	-10.1%	61
Iran	2.73	+73.9%	56
Iraq	0		79
Kenya	59.5	-4.12%	36
Cambodia	0.49	-66%	66
South Korea	0.18	+63.6%	74
Laos	0.29	-69.1%	70
Liberia	187	-1.32%	26
Sri Lanka	0		79
Morocco	0		79
Madagascar	200	+70.8%	24
Mexico	0.02	-66.7%	77
Mali	346	0%	3
Myanmar (Burma)	18	+44%	45
Mozambique	275	+0.887%	10
Mauritania	38.3	-20.1%	43
Malawi	228	+0.516%	20
Malaysia	0		79
Namibia	8.24	+12.4%	52
Niger	305	0%	7
Nigeria	299	0%	8
Nicaragua	1.42	-58.7%	62
Nepal	0		79
Oman	0		79
Pakistan	17.6	+58.9%	47
Panama	3.18	+97.5%	55
Peru	2.19	-19.5%	59
Philippines	0.23	+91.7%	72
Papua New Guinea	147	-9.53%	28
North Korea	0.31	+47.6%	68
Rwanda	53.6	-37.3%	38
Saudi Arabia	0		79
Sudan	68.1	0%	33
Senegal	66.4	+15.4%	34
Solomon Islands	237	+12.4%	16
Sierra Leone	293	-5.89%	9
El Salvador	0		79
Somalia	58.9	+1.01%	37
South Sudan	253	-1.8%	13
São Tomé & Príncipe	10.2	-42%	50
Suriname	0		79
Eswatini	1.73	+175%	60
Chad	207	-1.46%	23
Togo	230	-3.04%	17
Thailand	0.67	+45.7%	65
Tajikistan	0		79
Timor-Leste	0		79
Tanzania	128	-2.32%	29
Uganda	258	-3.37%	12
Venezuela	9.53	-12.7%	51
Vietnam	0.01	0%	78
Vanuatu	17.9	+108%	46
Yemen	38.9	-0.461%	42
South Africa	0.84	+155%	64
Zambia	177	-1.29%	27
Zimbabwe	49.4	+70.9%	39

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.MLR.INCD.P3'

# 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.MLR.INCD.P3'

# 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 malaria (per 1,000 population at risk)

Year: 2023

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