Literacy rate, adult male (% of males ages 15 and above)

Source: worldbank.org, 01.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 52 +3.56% 43
United Arab Emirates United Arab Emirates 99 +1.02% 13
Benin Benin 56.5 +10.6% 42
Burkina Faso Burkina Faso 44.1 +26.1% 44
Bangladesh Bangladesh 79 +1.28% 35
Bulgaria Bulgaria 98.7 -0.313% 14
Brunei Brunei 98.3 +1.3% 16
Côte d’Ivoire Côte d’Ivoire 60.2 -35.3% 41
Congo - Brazzaville Congo - Brazzaville 85.9 -0.572% 32
Costa Rica Costa Rica 98 +1.02% 17
Cuba Cuba 99.6 +0.601% 8
Cyprus Cyprus 99.6 +0.586% 9
Ecuador Ecuador 95 0% 23
Estonia Estonia 99.9 -0.13% 2
Gabon Gabon 90.8 +6.97% 30
Guinea Guinea 61.2 +13.2% 40
Gambia Gambia 65.3 +0.52% 39
Croatia Croatia 99.7 +0.697% 7
Hungary Hungary 99.1 -0.201% 12
Cambodia Cambodia 81.5 -6.35% 33
Sri Lanka Sri Lanka 93 0% 26
Lithuania Lithuania 99.8 -0.19% 4
Latvia Latvia 99.9 -0.14% 3
Macao SAR China Macao SAR China 98.5 +0.48% 15
Moldova Moldova 99.7 -0.3% 6
Madagascar Madagascar 77.9 -0.123% 36
Maldives Maldives 97.6 -0.603% 19
Malta Malta 93.4 +1.57% 25
Montenegro Montenegro 99.4 +0.305% 10
Mauritania Mauritania 71.8 +2.39% 38
Mauritius Mauritius 93.9 -1.15% 24
Namibia Namibia 92.2 +3.55% 28
Nigeria Nigeria 73.7 +3.47% 37
Nepal Nepal 81 +1.55% 34
Poland Poland 99.8 +0.201% 5
Puerto Rico Puerto Rico 92.4 +0.435% 27
Portugal Portugal 97.8 +1.83% 18
Romania Romania 99 0% 13
Russia Russia 100 0% 1
Singapore Singapore 99 0% 13
Suriname Suriname 96.5 +1.58% 21
Syria Syria 97 +5.9% 20
Thailand Thailand 95.5 +3.27% 22
Tonga Tonga 99.4 +18.6% 11
Ukraine Ukraine 100 +0.00606% 1
Uzbekistan Uzbekistan 100 0% 1
Vanuatu Vanuatu 89.8 +4.2% 31
Samoa Samoa 99 +0.697% 13
South Africa South Africa 91 -5.21% 29 

                    
# 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 = 'SE.ADT.LITR.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 <- 'SE.ADT.LITR.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))