library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     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)
#Set the working directory
setwd("C:/Users/Haley/Desktop/Data Visualization")
#Open the csv for Nations
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()
## )
summary(nations)
##     iso2c              iso3c             country               year     
##  Length:5275        Length:5275        Length:5275        Min.   :1990  
##  Class :character   Class :character   Class :character   1st Qu.:1996  
##  Mode  :character   Mode  :character   Mode  :character   Median :2002  
##                                                           Mean   :2002  
##                                                           3rd Qu.:2008  
##                                                           Max.   :2014  
##                                                                         
##    gdp_percap         population          birth_rate    neonat_mortal_rate
##  Min.   :   239.7   Min.   :9.004e+03   Min.   : 6.90   Min.   : 0.70     
##  1st Qu.:  2263.6   1st Qu.:7.175e+05   1st Qu.:13.40   1st Qu.: 6.70     
##  Median :  6563.2   Median :5.303e+06   Median :21.60   Median :15.00     
##  Mean   : 12788.8   Mean   :2.958e+07   Mean   :24.16   Mean   :19.40     
##  3rd Qu.: 17195.0   3rd Qu.:1.757e+07   3rd Qu.:33.88   3rd Qu.:29.48     
##  Max.   :141968.1   Max.   :1.364e+09   Max.   :55.12   Max.   :73.10     
##  NA's   :766        NA's   :14          NA's   :295     NA's   :525       
##     region             income         
##  Length:5275        Length:5275       
##  Class :character   Class :character  
##  Mode  :character   Mode  :character  
##                                       
##                                       
##                                       
##

First need to create a new variable in the data, using mutate from dplyr, giving the GDP of each country in trillions of dollars, by multiplying gdp_percap by population and dividing by a trillion.

#mutate with add and preserve variables
nations_mutate <- nations %>% 
  mutate(gdp = gdp_percap * population/10^12) %>%
  filter(country == "China" | country == "Japan" | country == "United States" | country == "Germany")

Draw both charts

#Set up plot
nationsdotplot <- ggplot(nations_mutate, aes(x=year, y=gdp, group = country)) +
  geom_line(aes(color = country))+
  geom_point(aes(color = country))+
  labs(title = "China's Rise to Become the Largest Economy")+
  xlab("Year")+
  ylab("GPD ($Trillion)")+
  scale_color_brewer(palette = "Set1") +
  theme_minimal()
nationsdotplot

Need to group_by region and year, then summarize on mutated value of gdp using line of code below. summarise(GDP = sum(gdp, na.rm = TRUE)). (There will be null values, or NAs, in this data, so you will need to use na.rm = TRUE)

nations2 <- nations %>%
  mutate(gdp = gdp_percap * population/10^12)%>%
  group_by(region,year) %>%
  summarise(GDP = sum(gdp, na.rm = TRUE))
## `summarise()` has grouped output by 'region'. You can override using the `.groups` argument.
chart2 <- ggplot(nations2,aes(x=year,y = GDP, fill = region))+
  #use color = "white" to add white outlines to the graph
  geom_area(color = "white")+
  #add color brewer set2
  scale_fill_brewer(palette = "Set2") +
  #give a dark theme to add dark gray background
  theme_minimal()+
  #include labels for x and y axis
  labs(title = "GDP by World Band Region")+
  xlab("Year")+
  ylab("GPD ($Trillion)")

chart2