#Loading……

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(readr)
library(ggplot2)
library(dplyr)
library(RColorBrewer)

#Read Nations Dataset

nations <- read.csv("Data110Code/nations.csv")

#COLORS

display.brewer.all()

#Filter the data with dplyr for the four desired countries

Nations1 <- nations %>%
  select(year, birth_rate, population, gdp_percap, country) %>%
  mutate(GDP = ((gdp_percap * population) / 1e+12) ) %>% 
  arrange(year)

#Dimensions and Structure

dim(Nations1)
## [1] 5275    6
str(Nations1)
## 'data.frame':    5275 obs. of  6 variables:
##  $ year      : int  1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
##  $ birth_rate: num  11.9 25.8 49 18.8 24.8 ...
##  $ population: num  54511 1811458 12067570 61906 3286542 ...
##  $ gdp_percap: num  NA 74017 NA 11087 2749 ...
##  $ country   : chr  "Andorra" "United Arab Emirates" "Afghanistan" "Antigua and Barbuda" ...
##  $ GDP       : num  NA 0.134079 NA 0.000686 0.009033 ...

Nations_Country <- Nations1 %>%
  filter(country %in% c("Cambodia", "Thailand", "Lao PDR", "Vietnam")) %>%
  ggplot(aes (x= year, y= GDP)) +
  labs(title= "Thailand's Economic Growth Comparared to Surrounding Countries") +
  xlab("Years")+
  ylab("GDP by Trillion") +
  theme_minimal(base_size = 10)

#ADD points, lines, color

FNC <- Nations_Country +
geom_point(aes(color= country)) + geom_line(aes(color= country)) 
  labs(color= "Country") +
  scale_color_brewer(palette = "Set1") 
## NULL

#Visualization Here

FNC
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 row(s) containing missing values (geom_path).

#2 Group by region and year, and then summarize on your mutated value for gdp

RegionalData <- nations %>%
  mutate(GDP2 = (gdp_percap * population / 1e+12))
RegionalYear <- RegionalData %>%
  group_by(year, region) %>%
  summarise(GDP2 = sum(gdp_percap, na.rm = TRUE)) %>%
  arrange(year, region) 
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

FRD <- ggplot() +
 geom_area(data = RegionalYear, aes(x = year, y = GDP2, fill = region), color = "white") +
  ggtitle("Gross Domestic Product by World Bank (by Region)" ) +
  labs(
    xlab = "Year",
    ylab = "GDP by trillions") +
  theme_minimal(base_size = 10) +
  scale_fill_brewer(name = "Region", palette = "Set2")

#Visualization Here

FRD