Country

Indicator

Value type

Values present

Intentional homicides, male (per 100,000 male)

Source: worldbank.org, 19.12.2024

Year: 2021

Country	Value	Value change, %	Rank
Albania	3.79	+4.42%	39
United Arab Emirates	0.522	-33.7%	79
Argentina	7.96	-13.1%	31
Armenia	3.74	+31.5%	41
Antigua & Barbuda	24.7	+36.6%	15
Australia	1.1	-6.95%	65
Austria	0.569	-10.9%	76
Azerbaijan	2.52	-15.4%	49
Belgium	1.99	+24%	52
Bulgaria	1.8	+41.5%	54
Bahrain	0		89
Bahamas	57.9	+81.8%	5
Bosnia & Herzegovina	1.55	-33.3%	58
Belize	56.7	+23.8%	6
Bolivia	3.76	-3.63%	40
Barbados	19.3	-31.7%	20
Canada	3.09	+1.2%	48
Switzerland	0.394	-15.6%	81
Chile	6.64	-22%	33
Colombia	51.5	+13.9%	8
Costa Rica	20.6	+4.15%	19
Cyprus	1.44	-10.5%	59
Czechia	0.386	-47.3%	82
Germany	0.87	-15.8%	71
Dominica	22.2	-43.1%	18
Denmark	1.1	-16.2%	64
Dominican Republic	18.5	+22.4%	23
Algeria	2.38	-1.06%	51
Ecuador	25.5	+85.9%	14
Spain	0.829	+7.55%	72
Estonia	3.49	-31.3%	42
France	1.62	+1.54%	57
Ghana	3.1	+3.86%	47
Greece	1.09	-4.45%	66
Grenada	6.41	-63.9%	34
Guatemala	34.1	+9.58%	11
Guyana	31	+2.41%	13
Hong Kong SAR China	0.318	-15.2%	83
Honduras	69.5	+7.9%	3
Croatia	0.961	-8.75%	69
Haiti	24.3	+14.4%	16
Hungary	1.05	+20%	67
India	3.35	-0.553%	44
Ireland	0.607	-44.9%	75
Iceland	0.527	-0.914%	78
Israel	3.29	+40.8%	46
Italy	0.637	+9.31%	74
Jamaica	95.6	+9.66%	1
Jordan	1.44	-1.89%	60
Japan	0.249	+15.9%	87
Kenya	7.95	+45.4%	32
St. Kitts & Nevis	56.5	+30.3%	7
South Korea	0.549	-13.4%	77
St. Lucia	69.7	+31.7%	2
Liechtenstein	5.17		35
Lithuania	3.82	-29.8%	38
Latvia	2.42	-24.2%	50
Macao SAR China	0		89
Morocco	3.31	+63.3%	45
Mexico	50.5	-3.74%	9
Malta	0.73	-67.5%	73
Myanmar (Burma)	49.8	+910%	10
Montenegro	4.25	-13.1%	36
Mongolia	9.81	-2.72%	29
Mauritius	3.43	-8.34%	43
Namibia	18.5	+1.55%	22
Nicaragua	19.1	+36%	21
Netherlands	0.885	+21.7%	70
Norway	0.477	-19.1%	80
Oman	0.29	-18.3%	84
Panama	23.4	+10.9%	17
Poland	1.05	+9.99%	68
Paraguay	13.7	+10.5%	25
Palestinian Territories	1.41	+3.49%	62
Qatar	0.256	-48.7%	86
Romania	1.8	-8.6%	53
Russia	10.8	-7.84%	27
Singapore	0.129	-20.5%	88
El Salvador	33.5	-15.4%	12
Serbia	1.43	-18.7%	61
Suriname	9.17	-38.3%	30
Slovakia	1.17	-5.89%	63
Slovenia	0.282	-40.1%	85
Sweden	1.69	-11%	56
Turkey	4.01	-2.16%	37
Uruguay	16	-11.6%	24
United States	10.8	+4.06%	28
Uzbekistan	1.76	+2.45%	55
St. Vincent & Grenadines	60.1	+14.8%	4
Zimbabwe	10.9	+27.2%	26

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.MA.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.MA.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, male (per 100,000 male)

Year: 2021

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