Life expectancy at birth, total (years)

Life expectancy at birth, total (years) serves as a crucial demographic indicator that reflects the average number of years a newborn is expected to live, assuming that current mortality rates remain constant throughout their lifetime. This metric provides insights into the overall health of a population, highlighting the effectiveness of healthcare systems, social conditions, and economic development. As of 2022, the global median life expectancy is 73.51 years, an important marker that showcases improvements in health standards compared to previous decades.

The significance of life expectancy at birth extends beyond mere numbers; it is an essential determinant of public health policies and economic strategies. Regions with higher life expectancy figures typically enjoy better health infrastructure, access to quality medical care, and healthier lifestyles. For instance, the leading areas for life expectancy include Macao SAR China, with a remarkable 85.38 years, followed closely by Liechtenstein (84.32 years), Japan (84.0 years), Hong Kong SAR China (83.66 years), and French Polynesia (83.55 years). These regions could be perceived as benchmarks in global health, often demonstrating successful healthcare systems accompanied by economic prosperity and social stability.

Conversely, the strikingly lower figures in the bottom five areas—Chad (53.0 years), Lesotho (53.04 years), Nigeria (53.63 years), the Central African Republic (54.48 years), and South Sudan (55.57 years)—underscore the critical issues faced by many developing nations. These countries often suffer from inadequate health services, infectious diseases, malnutrition, conflict, and poverty, which drastically reduce life expectancy. This stark contrast between the top and bottom areas illustrates the disparities in global health and living conditions, further emphasizing the importance of global efforts to bridge such gaps.

Life expectancy is intricately tied to several other socio-economic indicators. For example, higher life expectancy correlates with lower infant mortality rates, better education levels, increased per capita income, and improved sanitation facilities. Nations with investments in healthcare and education consistently achieve longer life spans, suggesting a holistic approach is necessary to enhance overall life expectancy. In past decades, global life expectancy has seen substantial improvement; it has risen from a mere 50.91 years in 1960 to approximately 71.95 years in 2022. This trajectory reflects advances in medical technology, public health initiatives, and a growing understanding of health determinants.

Factors affecting life expectancy include genetics, lifestyle choices, access to healthcare, nutrition, and public health policies. Behavioral factors, such as smoking, alcohol consumption, and physical activity levels also play significant roles. Countries adopting comprehensive wellness initiatives, promoting healthy lifestyles, and prioritizing preventive healthcare are more likely to experience higher life expectancy rates. Environmental factors, like pollution and climate change, also pose long-term threats to health and life expectancy, requiring increasingly proactive strategies from governments and international organizations.

To address disparities in life expectancy, a multifaceted approach is vital. Strategies can include improving healthcare accessibility, particularly in rural and underprivileged areas, enhancing education and community health programs focused on maternal and child health, and promoting awareness about healthy lifestyle choices. Global partnerships, like those with the World Health Organization, emphasize a united front in combatting health disparities. Additionally, innovative solutions such as telemedicine and mobile health clinics can extend healthcare access beyond traditional boundaries, reaching remote populations effectively.

While the upward trend in life expectancy at birth is commendable, it isn't without its flaws. The data can mask underlying socio-economic issues, especially in high-income countries, where life expectancy may hide disparities among different population subgroups affected by race, ethnicity, or economic status. Moreover, during crises such as the COVID-19 pandemic, life expectancy can experience abrupt declines, revealing vulnerabilities in public health infrastructure. The data from 2020 and 2021 shows a dip in global life expectancy due to the pandemic, indicating that resilience and adaptability in health systems are essential in coping with sudden health emergencies.

In conclusion, life expectancy at birth provides invaluable insight into a population's health and wellness and serves as a critical benchmark for evaluating public health initiatives. As demonstrated through the differences between various regions worldwide, it is clear that socio-economic conditions significantly influence health outcomes. By addressing the factors affecting life expectancy, investing in health infrastructure, and implementing comprehensive health policies, nations can work towards improving the quality of life for their citizens and achieving sustainable, equitable health outcomes going forward.

                    
# 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 = 'SP.DYN.LE00.IN'

# 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 <- 'SP.DYN.LE00.IN'

# 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))