# Required packages
packages <- c(
  "tidyverse",
  "gt",
  "gapminder",
  "srvyr",
  "srvyrexploR",
  "fst",
  "ggridges"
)
# For data manipulation and ggplot2
# For formatted tables
# For gapminder dataset
# For survey data
# For ANES 2020 dataset
# For reading ESS data
# For density ridge plots
# Install and load packages
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])] 
if(length(new_packages)) install.packages(new_packages)
lapply(packages, library, character.only = TRUE)

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Task 1. Global Life Expectancy Changes (3 points) a. Data Manipulation (gapminder data) • Filter for years 1987 and 2007 • Calculate mean life expectancy by continent for each year • Calculate the change between years • In a separate step, filter to five focal countries (Niger, Bangladesh, El Salvador, Iraq, Zimbabwe) Note: My Columns Are: country, continent, year, lifeExp, pop, gdpPercap

life_exp_summary <- gapminder %>%
  filter(year %in% c(1987, 2007)) %>%
  group_by(continent, country) %>%
  summarize(
    start_life = first(lifeExp),
    end_life = last(lifeExp),
    change = end_life - start_life,
    avg_life = mean(lifeExp),
    .groups = "drop"
  ) %>%
  arrange(avg_life)
life_exp_summary

## # A tibble: 142 × 6
##    continent country       start_life end_life  change avg_life
##    <fct>     <fct>              <dbl>    <dbl>   <dbl>    <dbl>
##  1 Africa    Sierra Leone        40.0     42.6  2.56       41.3
##  2 Africa    Angola              39.9     42.7  2.83       41.3
##  3 Asia      Afghanistan         40.8     43.8  3.01       42.3
##  4 Africa    Mozambique          42.9     42.1 -0.779      42.5
##  5 Africa    Guinea-Bissau       41.2     46.4  5.14       43.8
##  6 Africa    Rwanda              44.0     46.2  2.22       45.1
##  7 Africa    Liberia             46.0     45.7 -0.349      45.9
##  8 Africa    Somalia             44.5     48.2  3.66       46.3
##  9 Africa    Zambia              50.8     42.4 -8.44       46.6
## 10 Africa    Nigeria             46.9     46.9 -0.0270     46.9
## # ℹ 132 more rows

key_cases <- life_exp_summary %>%
  filter(country %in% c("Niger", "Bangladesh", "El Salvador", "Iraq",
"Zimbabwe"))

key_cases

## # A tibble: 5 × 6
##   continent country     start_life end_life change avg_life
##   <fct>     <fct>            <dbl>    <dbl>  <dbl>    <dbl>
## 1 Africa    Niger             44.6     56.9  12.3      50.7
## 2 Africa    Zimbabwe          62.4     43.5 -18.9      52.9
## 3 Asia      Bangladesh        52.8     64.1  11.2      58.4
## 4 Asia      Iraq              65.0     59.5  -5.50     62.3
## 5 Americas  El Salvador       63.2     71.9   8.72     67.5