nations

library(tidyverse)

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library(ggfortify)
library(htmltools)
library(plotly)##

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China’s rise to become the largest economy

df <- read.csv("nations.csv")
df$gdp <- df$gdp_percap * df$population / 1000000000000
df1<-df%>%
  filter(country %in% c("China","Germany","Japan","United States"))
ggplot(df1,aes(x = year,y = gdp,color = country))+
  geom_point()+
  geom_line()+
  labs(y = "GDP($trillion)")+
  theme_minimal(base_size = 12)+
  scale_color_brewer(palette = "Set1")

GDP by world bank region

df2 <- df%>%
  group_by(region,year)%>%
  summarise(GDP = sum(gdp, na.rm = TRUE))

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

ggplot(df2,aes(x = year,y = GDP,fill = region))+
  geom_area()+
  theme_minimal(base_size = 12)+
  scale_fill_brewer(palette = "Set2")