Individuals using the Internet, male (% of male population)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 84.6 +1.05% 44
United Arab Emirates United Arab Emirates 100 0% 1
Argentina Argentina 89.4 +0.846% 31
Austria Austria 96.5 +2.27% 10
Belgium Belgium 94.6 -0.0377% 15
Bangladesh Bangladesh 51.7 +14.2% 54
Bulgaria Bulgaria 81 +1.13% 48
Bahrain Bahrain 100 0% 1
Bosnia & Herzegovina Bosnia & Herzegovina 83.7 +3.61% 45
Belarus Belarus 90.7 +2.3% 25
Brazil Brazil 82.6 +3.36% 46
Switzerland Switzerland 98.2 +2.03% 6
Chile Chile 95.1 +13.2% 13
Costa Rica Costa Rica 84.8 +3.13% 43
Cyprus Cyprus 90.6 +2.62% 26
Czechia Czechia 87.2 +0.259% 35
Germany Germany 93.5 +0.754% 18
Denmark Denmark 98.9 +1.21% 5
Ecuador Ecuador 72.4 +3.7% 52
Spain Spain 95.3 +0.813% 12
Estonia Estonia 91.9 +0.965% 21
Finland Finland 94.4 +1.27% 16
France France 87.4 +1.77% 33
Georgia Georgia 81.6 +3.54% 47
Greece Greece 85.2 +1.74% 42
Croatia Croatia 86.8 +3.57% 37
Hungary Hungary 91.1 +2.71% 22
Indonesia Indonesia 72.1 +3.86% 53
Italy Italy 88 +1.88% 32
Kazakhstan Kazakhstan 94.1 +1.45% 17
South Korea South Korea 98.1 +0.337% 9
Kuwait Kuwait 99.6 +0.00743% 2
Lithuania Lithuania 86.1 -1.03% 40
Luxembourg Luxembourg 99.4 +0.439% 3
Latvia Latvia 92.1 +1.86% 20
Mexico Mexico 81 +2.16% 49
Malta Malta 90 -1.98% 28
Malawi Malawi 19.5 55
Malaysia Malaysia 98.2 -0.684% 7
Netherlands Netherlands 98.1 +4.75% 8
Norway Norway 99 0% 4
Poland Poland 86.1 -1.85% 41
Portugal Portugal 86.6 +1.36% 38
Paraguay Paraguay 76.9 +2.64% 51
Palestinian Territories Palestinian Territories 87 -2.36% 36
Romania Romania 90 +4.29% 29
Russia Russia 92.4 +1.67% 19
Saudi Arabia Saudi Arabia 100 0% 1
Singapore Singapore 94.6 -2.38% 14
Serbia Serbia 86.2 +0.297% 39
Slovakia Slovakia 87.4 -1.09% 34
Slovenia Slovenia 90.9 +1.34% 24
Sweden Sweden 95.8 +1.44% 11
Thailand Thailand 90.5 +1.37% 27
Turkey Turkey 89.8 +2.53% 30
Uzbekistan Uzbekistan 90.9 +4.61% 23
Vietnam Vietnam 80.3 -1.57% 50 

                    
# 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 = 'IT.NET.USER.MA.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 <- 'IT.NET.USER.MA.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))