Country

Indicator

Value type

Values present

Intentional homicides (per 100,000 people)

Source: worldbank.org, 01.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Afghanistan	4.02	-39%	42
Albania	2.31	+8.66%	54
United Arab Emirates	0.47	-32.9%	93
Argentina	4.62	-13.9%	41
Armenia	2.19	+17.9%	55
Antigua & Barbuda	17.2	+76.7%	16
Australia	0.745	-13.5%	81
Austria	0.729	-0.16%	82
Azerbaijan	1.91	-14.6%	60
Belgium	1.08	-65.1%	69
Bulgaria	1.28	+29.3%	65
Bahrain	0.0683		104
Bahamas	29.2	+62.4%	9
Bosnia & Herzegovina	0.978	-24.5%	73
Belize	31.2	+21%	5
Bermuda	10.9	-0.241%	27
Bolivia	3.49	-0.00158%	47
Barbados	11.4	-22.1%	25
Botswana	10.5	+21.5%	28
Canada	2.07	+3.1%	56
Switzerland	0.483	-11.2%	92
Chile	3.63	-24.3%	46
Colombia	27.5	+13.4%	12
Costa Rica	11.4	+2.54%	24
Cyprus	1.29	+6.1%	64
Czechia	0.447	-38.8%	94
Germany	0.833	-11.2%	76
Dominica	13.8	-33.7%	19
Denmark	0.803	-15%	78
Dominican Republic	10.5	+20.7%	29
Algeria	1.57	+4.52%	62
Ecuador	14	+79.8%	18
Spain	0.611	-2.94%	87
Estonia	1.96	-38.1%	57
France	1.14	+5.98%	67
Ghana	1.84	+6.49%	61
Greece	0.852	+14.8%	75
Grenada	4.01	-64.6%	43
Guatemala	20	+5.43%	13
Guyana	16.3	-17.3%	17
Hong Kong SAR China	0.307	+4.63%	100
Honduras	38.3	+7.12%	4
Croatia	0.813	-16.8%	77
Haiti	13	+14.9%	20
Hungary	0.772	-5.86%	80
India	2.94	+0.863%	49
Ireland	0.441	-33.9%	95
Iceland	0.54	-60.4%	88
Israel	1.94	+36.2%	58
Italy	0.511	+6.78%	91
Jamaica	52.1	+10.3%	1
Jordan	1.02	+3.48%	71
Japan	0.229	-9.92%	102
Kenya	5.27	+47.5%	38
St. Kitts & Nevis	29.4	+40.1%	7
South Korea	0.521	-12.3%	90
St. Lucia	39	+34.3%	3
Liechtenstein	5.12	+98.5%	39
Lithuania	2.58	-27.9%	51
Luxembourg	0.626	+31.5%	86
Latvia	3.04	-17.6%	48
Macao SAR China	0.437	+47.7%	96
Morocco	1.93	+54.7%	59
Mexico	28.2	-3.46%	11
Malta	0.38	-72%	98
Myanmar (Burma)	28.4	+867%	10
Montenegro	2.39	-16.5%	53
Mongolia	6.15	-1.12%	35
Mauritius	2.62	-2.94%	50
Malaysia	0.724	-5.02%	83
Namibia	12.4	+2.27%	23
Nicaragua	11	+42.4%	26
Netherlands	0.651	+6.13%	85
Norway	0.537	-6.85%	89
Oman	0.243	-21%	101
Pakistan	3.98	+6.47%	44
Panama	12.7	+9.35%	21
Poland	0.713	+2.95%	84
Portugal	0.797	+1.31%	79
Paraguay	7.83	+7.76%	32
Palestinian Territories	0.877	-8.33%	74
Qatar	0.335	-23%	99
Romania	1.26	-12.7%	66
Russia	6.8	-7.44%	34
Singapore	0.101	-40.3%	103
El Salvador	18.2	-14.8%	15
Serbia	1.06	-7.56%	70
Suriname	5.71	-37%	37
Slovakia	1.01	-12.6%	72
Slovenia	0.425	-18.3%	97
Sweden	1.08	-9.73%	68
Eswatini	12.7	+12.2%	22
Seychelles	4.7	-44.9%	40
Trinidad & Tobago	29.4	+11.7%	8
Turkey	2.52	+0.563%	52
Uganda	8.53	-15.1%	31
Ukraine	3.84	-0.616%	45
Uruguay	8.9	-9.69%	30
United States	6.81	+6.02%	33
Uzbekistan	1.41	+4.94%	63
St. Vincent & Grenadines	30.7	-2.75%	6
Venezuela	19.3	-34.4%	14
South Africa	41.9	+23.3%	2
Zimbabwe	6.14	+23.3%	36

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 = 'VC.IHR.PSRC.P5'

# 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 <- 'VC.IHR.PSRC.P5'

# 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

Intentional homicides (per 100,000 people)

Year: 2021

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