Nation Charts Assignment J.Z

library(tidyverse)

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library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(ggfortify)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

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The following object is masked from 'package:graphics':

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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.

nations <- mutate(nations, GDP = (gdp_percap * population) / (10^12))
filtered <- filter(nations, country == "China" | country == "Germany" | country == "Japan" | country == "United States")

plot1 <- ggplot(filtered, aes(x = year, y = GDP, color = country)) +
  geom_point() +
  geom_line() +
  labs(
    title = "China's Rise to Become the Largest Economy",
    x = "year",
    y = "GDP ($ trillion)") +
  scale_color_brewer(palette = "Set1")
plot1

grouping <- group_by(nations, region, year)
summarised_nations <- summarise(grouping, sum_GDP = sum(GDP, na.rm = TRUE))

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

plot2 <- ggplot(summarised_nations, aes(x=year, y=sum_GDP, fill = region)) +
  geom_area(color = 'White', linewidth = 0.1, alpha = 0.9) +
labs(
    title = "China's Rise to Become the Largest Economy",
    x = "year",
    y = "GDP ($ trillion)") +
  scale_fill_brewer(palette = "Set2")
plot2