DATA606FinalProject

Data Preperation

library(tidyverse)

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✔ purrr     1.2.0     
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✖ dplyr::lag()    masks stats::lag()
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# Load the data
df <- read_csv("Life Expectancy Data.csv")

Rows: 2938 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): Country, Status
dbl (20): Year, Life expectancy, Adult Mortality, infant deaths, Alcohol, pe...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Check what we have
glimpse(df)

Rows: 2,938
Columns: 22
$ Country                           <chr> "Afghanistan", "Afghanistan", "Afgha…
$ Year                              <dbl> 2015, 2014, 2013, 2012, 2011, 2010, …
$ Status                            <chr> "Developing", "Developing", "Develop…
$ `Life expectancy`                 <dbl> 65.0, 59.9, 59.9, 59.5, 59.2, 58.8, …
$ `Adult Mortality`                 <dbl> 263, 271, 268, 272, 275, 279, 281, 2…
$ `infant deaths`                   <dbl> 62, 64, 66, 69, 71, 74, 77, 80, 82, …
$ Alcohol                           <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, …
$ `percentage expenditure`          <dbl> 71.279624, 73.523582, 73.219243, 78.…
$ `Hepatitis B`                     <dbl> 65, 62, 64, 67, 68, 66, 63, 64, 63, …
$ Measles                           <dbl> 1154, 492, 430, 2787, 3013, 1989, 28…
$ BMI                               <dbl> 19.1, 18.6, 18.1, 17.6, 17.2, 16.7, …
$ `under-five deaths`               <dbl> 83, 86, 89, 93, 97, 102, 106, 110, 1…
$ Polio                             <dbl> 6, 58, 62, 67, 68, 66, 63, 64, 63, 5…
$ `Total expenditure`               <dbl> 8.16, 8.18, 8.13, 8.52, 7.87, 9.20, …
$ Diphtheria                        <dbl> 65, 62, 64, 67, 68, 66, 63, 64, 63, …
$ `HIV/AIDS`                        <dbl> 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0…
$ GDP                               <dbl> 584.25921, 612.69651, 631.74498, 669…
$ Population                        <dbl> 33736494, 327582, 31731688, 3696958,…
$ `thinness  1-19 years`            <dbl> 17.2, 17.5, 17.7, 17.9, 18.2, 18.4, …
$ `thinness 5-9 years`              <dbl> 17.3, 17.5, 17.7, 18.0, 18.2, 18.4, …
$ `Income composition of resources` <dbl> 0.479, 0.476, 0.470, 0.463, 0.454, 0…
$ Schooling                         <dbl> 10.1, 10.0, 9.9, 9.8, 9.5, 9.2, 8.9,…

colnames(df)

 [1] "Country"                         "Year"                           
 [3] "Status"                          "Life expectancy"                
 [5] "Adult Mortality"                 "infant deaths"                  
 [7] "Alcohol"                         "percentage expenditure"         
 [9] "Hepatitis B"                     "Measles"                        
[11] "BMI"                             "under-five deaths"              
[13] "Polio"                           "Total expenditure"              
[15] "Diphtheria"                      "HIV/AIDS"                       
[17] "GDP"                             "Population"                     
[19] "thinness  1-19 years"            "thinness 5-9 years"             
[21] "Income composition of resources" "Schooling"                      

df <- df %>%
  rename(
    life_expectancy = `Life expectancy`,
    adult_mortality = `Adult Mortality`,
    infant_deaths = `infant deaths`,
    pct_expenditure = `percentage expenditure`,
    hepatitis_b = `Hepatitis B`,
    total_expenditure = `Total expenditure`,
    hiv_aids = `HIV/AIDS`,
    thinness_1_19 = `thinness  1-19 years`,
    thinness_5_9 = `thinness 5-9 years`,
    income_composition = `Income composition of resources`,
    under_five_deaths = `under-five deaths`
  ) %>%
  filter(!is.na(life_expectancy))

Research Question

What socioeconomic and health factors are most strongly associated with life expectancy across countries, and do these relationships differ between developed and developing nations?

Cases

Each case represents a single country in a single year. There are 2,938 observations covering 193 countries across 16 years (2000-2015).

Data Collection

The data was collected by the World Health Organization (WHO) and the United Nations. Health-related variables such as life expectancy and mortality rates were collected by the WHO, while economic variables such as GDP were collected by the United Nations. Data spans 193 countries from 2000 to 2015.

Type of Study

This is an observational study. No intervention was applied — the data reflects naturally occurring differences in health and socioeconomic conditions across countries over time.

Data Source

World Health Organization (WHO)

Kaggle: https://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who

Describe Your Variables

Response Variables

- `life_expectancy`: The average life expectancy in years for a given country and year. This is a quantitative variable.

Explanatory Variables:

• `GDP`: GDP per capita in USD. Quantitative.
• `Schooling`: Average years of schooling. Quantitative.
• `total_expenditure`: Government health expenditure as % of total expenditure. Quantitative.
• `Alcohol`: Alcohol consumption per capita in litres. Quantitative.
• `BMI`: Average BMI of the population. Quantitative.
• `Status`: Whether the country is Developed or Developing. Categorical.

Relevant Summary Statistics

summary(df$life_expectancy)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  36.30   63.10   72.10   69.22   75.70   89.00 

summary(df$GDP)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
1.681e+00 4.639e+02 1.765e+03 7.494e+03 5.933e+03 1.192e+05       443 

summary(df$Schooling)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
    0.0    10.1    12.3    12.0    14.3    20.7     160 

summary(df$total_expenditure)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.37    4.26    5.75    5.93    7.49   17.60     226 

summary(df$Alcohol)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.010   0.905   3.770   4.615   7.715  17.870     193 

summary(df$BMI)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1.00   19.30   43.35   38.24   56.10   77.60      32 

df %>% count(Status)

# A tibble: 2 × 2
  Status         n
  <chr>      <int>
1 Developed    512
2 Developing  2416

ggplot(df, aes(x = life_expectancy)) +
  geom_histogram(binwidth = 2, fill = "steelblue", color = "white") +
  labs(title = "Distribution of Life Expectancy",
       x = "Life Expectancy (years)", y = "Count")

ggplot(df, aes(x = GDP, y = life_expectancy, color = Status)) +
  geom_point(alpha = 0.5) +
  scale_x_log10() +
  labs(title = "Life Expectancy vs GDP",
       x = "GDP (log scale)", y = "Life Expectancy (years)")

Warning: Removed 443 rows containing missing values or values outside the scale range
(`geom_point()`).

ggplot(df, aes(x = Schooling, y = life_expectancy, color = Status)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm") +
  labs(title = "Life Expectancy vs Schooling",
       x = "Years of Schooling", y = "Life Expectancy (years)")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 160 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 160 rows containing missing values or values outside the scale range
(`geom_point()`).

ggplot(df, aes(x = Status, y = life_expectancy, fill = Status)) +
  geom_boxplot() +
  labs(title = "Life Expectancy by Development Status",
       x = "Country Status", y = "Life Expectancy (years)")