Nations Charts HW

#install.packages("haven")

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.2     v dplyr   1.0.6
## v tidyr   1.1.3     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(ggplot2)
library(dplyr)
library(readr)
library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
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##     discard

## The following object is masked from 'package:readr':
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##     col_factor

setwd("C:/Users/tycho/Desktop/DATA110")
gdp <- read.csv(file = "nations.csv")
gdp1 <- mutate(gdp, GDP = ((gdp_percap * population)/1000000000000))

head(gdp)

##   iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
## 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                2.0
## 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
##                  region      income
## 1 Europe & Central Asia High income
## 2 Europe & Central Asia High income
## 3 Europe & Central Asia High income
## 4 Europe & Central Asia High income
## 5 Europe & Central Asia High income
## 6 Europe & Central Asia High income

View(gdp)

gdp2 <- filter(gdp1, country == "China" | country == "Japan" | country == "Korea, Rep." | country == "Hong Kong SAR, China" | country == "Macao SAR, China" | country == "Mongolia" | country == "United States")
ggplot (gdp2, aes(x = year, y = GDP, color = country)) +
  ylab("GDP($ trillion)") +
  theme_minimal(base_size = 12) +
  ggtitle("GDP of East Asian countries compared to the US") +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1")

gdp3 <- gdp1 %>% group_by(region, year) %>% summarize(GDP = sum(GDP, na.rm = TRUE))

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

ggplot(gdp3, aes(year, GDP)) +
         xlab("year") + ylab("GDP ($ trillion)") +
         theme_minimal(base_size = 12) +
         ggtitle("GDP by World Region") +
         geom_area(colour = "white", aes(fill = region)) + 
         scale_fill_brewer(palette = "Set2")