Nations Dataset Charts

library(tidyverse)

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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
library(dplyr)
library(readr)
library(highcharter)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo 
## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental use

library(RColorBrewer)

setwd("C:/Users/dylan/OneDrive/Documents/Data 110 Summer")
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.

head(nations)

## # A tibble: 6 × 10
##   iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
##   <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
## 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  
## 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
## # ℹ 2 more variables: region <chr>, income <chr>

nations <- nations %>%
  mutate(gdp_trillions = (gdp_percap * population) / 10^12)

big4 <- nations %>%
  filter(iso3c == "AND" | iso3c == "ARE" | iso3c == "AFG" | iso3c == "ALB") %>%
  arrange(year)

cols <- RColorBrewer::brewer.pal(n = 4, name = "Set1")

highchart() %>%
  hc_add_series(data = big4,
                type = "line",
                hcaes(x = year, y = gdp_trillions, group = country)) %>%
  hc_colors(cols) %>%
  hc_xAxis(title = list(text = "Year")) %>%
  hc_yAxis(title = list(text = "GDP ($ trillion)")) %>%
  hc_title(text = "GDP in Trillions for Selected Countries")

Second Chart

processed_data <- nations %>%
  group_by(region, year) %>%
  summarise(GDP = sum(gdp_trillions, na.rm = TRUE)) %>%
  ungroup()

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

head(processed_data)

## # A tibble: 6 × 3
##   region               year   GDP
##   <chr>               <dbl> <dbl>
## 1 East Asia & Pacific  1990  5.52
## 2 East Asia & Pacific  1991  6.03
## 3 East Asia & Pacific  1992  6.50
## 4 East Asia & Pacific  1993  7.04
## 5 East Asia & Pacific  1994  7.64
## 6 East Asia & Pacific  1995  8.29

# set color palette
cols <- brewer.pal(7, "Set2")

# stacked area chart
highchart() %>%
  hc_add_series(data = processed_data, type = "area",
                mapping = hcaes(x = year, y = GDP, group = region)) %>%
  hc_colors(cols) %>%
  hc_chart(style = list(fontFamily = "Georgia", fontWeight = "bold")) %>%
  hc_plotOptions(series = list(stacking = "normal",
                               marker = list(enabled = FALSE,
                                             states = list(hover = list(enabled = FALSE))),
                               lineWidth = 0.5,
                               lineColor = "white")) %>%
  hc_xAxis(title = list(text = "Year")) %>%
  hc_yAxis(title = list(text = "GDP ($ trillion)")) %>%
  hc_legend(align = "right", verticalAlign = "top", layout = "vertical")