Nations Charts Assignment

library(tidyverse)

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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
nations <- read_csv("nations.csv")

## Rows: 5275 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): iso2c, iso3c, country, region, income
## dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_rate
## 
## 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.

nations <- nations |> mutate(gdp = gdp_percap * population / 10^12)
nations

## # A tibble: 5,275 × 11
##    iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
##    <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
##  1 AD    AND   Andorra  1996         NA      64291       10.9                2.8
##  2 AD    AND   Andorra  1994         NA      62707       10.9                3.2
##  3 AD    AND   Andorra  2003         NA      74783       10.3                2  
##  4 AD    AND   Andorra  1990         NA      54511       11.9                4.3
##  5 AD    AND   Andorra  2009         NA      85474        9.9                1.7
##  6 AD    AND   Andorra  2011         NA      82326       NA                  1.6
##  7 AD    AND   Andorra  2004         NA      78337       10.9                2  
##  8 AD    AND   Andorra  2010         NA      84419        9.8                1.7
##  9 AD    AND   Andorra  2001         NA      67770       11.8                2.1
## 10 AD    AND   Andorra  2002         NA      71046       11.2                2.1
## # ℹ 5,265 more rows
## # ℹ 3 more variables: region <chr>, income <chr>, gdp <dbl>

Chart 1

gdp_by_country <- nations |> filter(country %in% c("China", "Germany", "Japan", "United States")) |> group_by(country, year) |> summarize(GDP = sum(gdp, na.rm = TRUE))

## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.

gdp_by_country

## # A tibble: 100 × 3
## # Groups:   country [4]
##    country  year   GDP
##    <chr>   <dbl> <dbl>
##  1 China    1990  1.11
##  2 China    1991  1.26
##  3 China    1992  1.47
##  4 China    1993  1.71
##  5 China    1994  1.98
##  6 China    1995  2.24
##  7 China    1996  2.51
##  8 China    1997  2.79
##  9 China    1998  3.04
## 10 China    1999  3.32
## # ℹ 90 more rows

gdp_by_country |> ggplot(aes(x = year, y = GDP, color = country)) +
  geom_point() +
  geom_line() +
  labs (
    title = "China's Rise to Become the Largest Economy",
    x = "year",
    y = "GDP ($ trillion)",
  ) +
  theme (
    panel.background = element_rect(fill = "transparent"),
    panel.grid = element_line(color = "gray90")
  ) +
  scale_color_brewer(palette = "Set1", name = NULL)

Chart 2

gdp_by_region <- nations |> group_by(region, year) |> summarise(GDP = sum(gdp, na.rm = TRUE))

## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.

gdp_by_region

## # A tibble: 175 × 3
## # Groups:   region [7]
##    region               year   GDP
##    <chr>               <dbl> <dbl>
##  1 East Asia & Pacific  1990  5.52
##  2 East Asia & Pacific  1991  6.03
##  3 East Asia & Pacific  1992  6.50
##  4 East Asia & Pacific  1993  7.04
##  5 East Asia & Pacific  1994  7.64
##  6 East Asia & Pacific  1995  8.29
##  7 East Asia & Pacific  1996  8.96
##  8 East Asia & Pacific  1997  9.55
##  9 East Asia & Pacific  1998  9.60
## 10 East Asia & Pacific  1999 10.1 
## # ℹ 165 more rows

gdp_by_region |> ggplot(aes(x = year, y = GDP, fill = region)) +
  geom_area(color = "white") +
  labs (
    title = "GDP by World Bank Region",
    x = "year",
    y = "GDP ($ trillion)"
  ) + 
  theme (
    panel.background = element_rect(fill = "transparent"),
    panel.grid = element_line(color = "gray90")
  ) +
  scale_fill_brewer(palette = "Set2", name = "region")