1. Importing the Data Set

First, I imported the data using the readr package and stored it in the “nations” variable, so it is easier to call.

library("readr")
nations <-read_csv("nations.csv")

## Parsed with 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()
## )

I will be using functions from the dplyr and ggplot2 libraries. It can be installed with install.packages() if it is not already on the device.

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.6.1

## 
## 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)

2. Creating a chart for China’s Rise to Become the Largest Economy

This chart will show GDP in trillions, for 4 countries from the time period 1990 to 2015. For this chart, I need to create a new variable using mutate from dyplr. In order to do this, I multiplied gdp_percap by population and divided by a trillion, naming the variable gdp_trill. I filtered by the only countries I was interested in: China, Germany, Japan, and the United States. I labelled the x-axis, y-axis, and title of the chart. I also set the theme and color pallette, and then ran both geom_point and geom_line to create a scatter plot with a line drawn over.

chart1 <- nations %>% mutate(gdp_trill=(gdp_percap*population/1000000000000)) %>% filter(country=="China" | country=="Germany" | country=="Japan" | country=="United States") %>% ggplot(aes(x = year, y = gdp_trill, color=country)) +
  xlab("year") + 
  ylab("GDP ($ trillion)") +
  ggtitle("China's Rise to Become the Largest Economy") +
  scale_color_brewer(palette = "Set1") +
  theme_minimal(base_size = 12) +
  geom_point() +
  geom_line()

chart1

3. Creating a chart for GDP by World Bank Region

This chart will show GDP in trillions by World Bank Region, from the year 1990 to 2015. For this chart, I used the function group_by for region and year. I then used summarize on the mutated gdp_trill variable. I filtered by the regions I was interested in: East Asia & Pacific, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, North America, South Asia, and Sub-Saharan Africa. I labelled the x-axis, y-axis, and title of the chart. I also set the theme and color pallette, and then ran both geom_area with the color white to create a thin white line around each area.

chart2 <- nations %>% group_by(region, year) %>% mutate(gdp_trill=(gdp_percap*population/1000000000000)) %>% summarise(sum = sum(gdp_trill, na.rm = TRUE)) %>% filter(region=="East Asia & Pacific" | region=="Europe & Central Asia" | region=="Latin America & Caribbean" | region=="Middle East & North Africa" | region=="North America" | region=="South Asia" | region=="Sub-Saharan Africa") %>% ggplot(aes(x = year, y = sum, fill=region)) +
  xlab("year") + 
  ylab("GDP ($ trillion)") +
  ggtitle("GDP by World Bank Region") +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal(base_size = 12) +
  geom_area(color="white")

chart2