Nations Chart

Loading Libraries

library(dplyr)
library (ggplot2)

Importing the data set

nations <- read.csv("C:/Users/thilo/OneDrive/Desktop/DATA 110/nations.csv")

head(nations)

  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

Creating a new variable, gdp of each countries in trillions of dollars

nations <- nations |>
  mutate(gdp = (gdp_percap * population)/ 10^12)

Filtering the countries

chart1 <- nations |>
  filter(country %in% c("India","Sri Lanka","Nepal","Pakistan"))

Plotting the chart 1

ggplot(chart1, aes(x= year, y = gdp, color = country))+
  geom_point()+
  geom_line()+
  scale_color_brewer(palette = "Set1")+
  labs(title = "GDP Growth in South Asian Countries", x= "Year", y= "GDP($trillion)",color = "country")+
  theme_light() ##Because I do not like how it looks like with theme_minimal, it looks like empty so I chose theme_light to add some gray lines

Grouping and summarize for chart 2

chart2<- nations |>
  group_by(region , year)|>
  summarize(GDP = sum(gdp,na.rm = TRUE))

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

Plotting the chart 2

ggplot(chart2, aes(x=year,y=GDP, fill = region))+
  geom_area(color = "white", linewidth = 0.2)+ ##to add a thin white line, I used 0.2 linewidth and the color. 
  scale_fill_brewer(palette = "Set2")+
  labs(title = "GDP by World Bank Region", x= "Year", y = "GDP($ trillion)", fill = "region")+
  theme_minimal() ##For this one I think it's better with theme_minimal instead of theme_line.

Citations

Peng, Roger D. Exploratory Data Analysis with R. Leanpub, 2020.

Even though the book says lwd to make the layers I used linewidth because I googled to check if the both same and yes, lwd means linewidth.