Nations Dataset Charts

Loading up the nations dataset for the work ahead

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(RColorBrewer)
nations <- read_csv("nations.csv")

## Rows: 5275 Columns: 10

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (5): iso2c, iso3c, country, region, income
## dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_rate
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Creating a new variable in the data and Filtering the data for the four desired countries

nations_new <- nations %>%
  select(country, year, gdp_percap, population, region) %>%
  mutate(gdp_p1 = ((gdp_percap * population) / 1e+12) ) %>%
  arrange(year)

str(nations_new)

## tibble [5,275 x 6] (S3: tbl_df/tbl/data.frame)
##  $ country   : chr [1:5275] "Andorra" "United Arab Emirates" "Afghanistan" "Antigua and Barbuda" ...
##  $ year      : num [1:5275] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
##  $ gdp_percap: num [1:5275] NA 74017 NA 11087 2749 ...
##  $ population: num [1:5275] 54511 1811458 12067570 61906 3286542 ...
##  $ region    : chr [1:5275] "Europe & Central Asia" "Middle East & North Africa" "South Asia" "Latin America & Caribbean" ...
##  $ gdp_p1    : num [1:5275] NA 0.134079 NA 0.000686 0.009033 ...

nations_new_c1 <-  nations_new %>%
  filter(country %in% c("China", "Germany", "Japan", "United States")) %>%
  ggplot(aes(x = year, y = gdp_p1))+ 
  labs(title = "China's Rise to Become the Largest Economy") +
       xlab("Year") +
       ylab("GDP ($ trillion)") +
  theme_minimal(base_size = 13)  #changing the font size to 13 and theme to theme_minimal

nations_new_c1 +
  geom_line(aes( color = country)) +
  geom_point(aes(color = country)) +
  labs(color = "Country") +
  scale_color_brewer(palette = "Set1") 

Creating the second chart

Grouping by region and year

by_regionyear <- nations %>%
  mutate(gdp_p2= ((gdp_percap * population) / 1e+12) ) 

Summarizing the mutated value for gdp

regionyear <-  by_regionyear %>%
  group_by(region, year) %>%
  summarise(gdp_p2 = sum(gdp_percap, na.rm = TRUE)) %>%
  arrange(region , year) 

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

p2 <- ggplot() +
 geom_area(data = regionyear,aes(x = year, y = gdp_p2, fill = region), color = "white") +
  scale_fill_brewer(name = "Region", palette = "Set2") +
  ggtitle("GDP by World Bank Region" ) +
  labs(
    x = "Year",
    y = "GDP (in trillion $)") +
  theme_minimal(base_size = 13) #changing the font size to 13 and theme to theme_minimal

p2