Week 7 HW Nations Chart

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.6     v dplyr   1.0.4
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(dplyr)
library(ggplot2)

setwd("~/School/MC/DATA 110/Datasets")
nations <- read_csv("nations.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   iso2c = col_character(),
##   iso3c = col_character(),
##   country = col_character(),
##   year = col_double(),
##   gdp_percap = col_double(),
##   population = col_double(),
##   birth_rate = col_double(),
##   neonat_mortal_rate = col_double(),
##   region = col_character(),
##   income = col_character()
## )

head(nations)

## # A tibble: 6 x 10
##   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
## # ... with 2 more variables: region <chr>, income <chr>

#create a new variable, GDP of each country in trillions of dollars
nations_withGDP <- mutate(nations, gdp = gdp_percap * population / 1e12)

Plot 1. EU’s Big Four Economies

nations_withGDP %>%
  filter(country == "Spain" | 
           country == "Germany" | 
           country == "France" | 
           country == "United Kingdom") %>%
  ggplot(mapping = aes(x = year, y = gdp,  color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(x = "Year", 
       y = "GDP in trillions of dollars", 
       title = "EU's Big Four Economies", 
       subtitle = "GDP of Germany, United Kingdom, France, and Spain from 1990 to 2014")

Plot 2. Region’s GDP Area Chart

nations_withGDP %>%
  group_by(region, year) %>%
  summarise(gdp = sum(gdp, na.rm = TRUE)) %>%
  ggplot(mapping = aes(x = year, y = gdp, fill = region)) +
  geom_area() +
  scale_fill_brewer(palette = "Set2") +
  labs(x = "Year", 
       y = "GDP in trillions of dollars", 
       title = "Region's GDP Area Chart", 
       subtitle = "From 1990 to 2014")

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