Nations Dataset Chart Assignment

library(tidyverse)

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setwd("C:/Users/senay/OneDrive/Desktop/Scoo/Spring 2025/DATA 110/Datasets")
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.

Checking the first few lines of the dataset

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>

Creating a new variable called gdp using mutate function

nationsgdp <- mutate(nations, gdp = (gdp_percap * population)/10^12 )

To make the first chart, we need to filter for the desired four countries

nationsp1 <- nationsgdp |> filter(country %in% c("China", "United States", "Japan", "Germany"))
head(nationsp1)

# A tibble: 6 × 11
  iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
  <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
1 CN    CHN   China    1992      1260. 1164970000       18.3               29.4
2 CN    CHN   China    2005      5053. 1303720000       12.4               14  
3 CN    CHN   China    2000      2915. 1262645000       14.0               21.2
4 CN    CHN   China    1991      1091. 1150780000       19.7               29.7
5 CN    CHN   China    2013     12219. 1357380000       12.1                6.3
6 CN    CHN   China    1999      2650. 1252735000       14.6               22.2
# ℹ 3 more variables: region <chr>, income <chr>, gdp <dbl>

Now we plot the chart using geom_point and geom_line layers. Additionally, I call in the plotly library to incorporate interactivity

library(plotly)

Warning: package 'plotly' was built under R version 4.4.3

Attaching package: 'plotly'

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

    last_plot

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

    filter

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

    layout

p1 <- ggplot(nationsp1, aes(x = year, y = gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "RdBu") +
  theme_minimal(base_size = 10) +
  labs(title = "China's Rise to Become the Largest Economy", 
       x = "Year", 
       y = "GDP ($ trillion)")
p1 <- ggplotly(p1)
p1

To make the second chart, I group by region and year, then summarize

nationsp2 <- nationsgdp |> group_by(region, year) |> summarise(gdp = sum(gdp, na.rm = T))

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

head(nationsp2)

# A tibble: 6 × 3
# Groups:   region [1]
  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

p2 <- ggplot(nationsp2, aes(x = year, y = gdp, fill = region)) + 
  scale_fill_brewer(palette = "RdBu") +
  geom_area(color= "black") +
  labs(x = "Year",
       y = "GDP ($ trillion)",
       title = "GDP by World Bank Region")
p2