library(tidyverse)
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library(RColorBrewer)
library(ggplot2)
library(dplyr)
setwd(("/Users/janithrithilakasiri/Downloads"))
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.

Create a new column with the GDP of all the countries listed in trillions of dollars

gdp_1 <- nations %>%
 mutate(gdp = gdp_percap * population / 1e12)

Filter the data for Sri Lanka, India, Pakistan, and Nepal(South Asian Countires Playing in T20 World Cup, 2024).

gdp_tn_asia <- gdp_1 %>% 
  filter(country == "Sri Lanka" | country =="India"| country== "Pakistan"| country == "Nepal")

Create a plot to see over time growth for these four countries.

dot_plot <- gdp_tn_asia |> ggplot() +
          geom_point(aes(x = year, y = gdp, color = country)) + scale_color_brewer(palette = "Set1") +
          geom_line(aes(x = year, y = gdp, color = country)) +
  labs(title = "South Asian Economy", 
       x = "Year",
       y = "Gross Domestic Product ($ Trillion)",
      color = "Country") +
  theme_classic(base_size = 12) +
  theme(legend.position = c(0.2, 0.7)) 

dot_plot

Since 1993, India’s Gross Domestic Product grew at a faster rate than the other countries listed. Between 1990 - 2005 other countries were mostly at the same rate but in 2006, we can see a little bit of a growth in Pakistan. However, Sri Lanka and Nepal rate of growths remained similar from 1990 - 2005. We can see a little bit of a growth from Sri Lanka in 2008 (That’s when our Sinhala and Tamil war ended in the country).I could say India had the most growth rate in South Asia between 1990 - 2015.

Group_by region and year, and then summarize on your mutated value for gdp

region_year <- gdp_1 |> 
  group_by(region, year) |>
  summarise(gdp = sum(gdp, na.rm = TRUE))
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.

Plot GDP inn times for each region.

reigon_y_p <- region_year |>
  ggplot() +
  geom_line(aes(x = year, y = gdp)) +
  geom_area(aes(x = year, y = gdp, fill = region), color = "black") +
  scale_fill_brewer(palette = "PRGn") +
  labs(title = "Gross Domestic Prodct by World Bank Region", 
       x = "Year",
       y = "Gross Domestic Product in Trillion",
      color = "Region") +
  theme_dark(base_size = 10) +
  theme(plot.title = element_text(hjust = 0.9))
reigon_y_p

Sub - Saharan Africa & South Asia were the regions that had the lowest gdp since 1993. East Asia & pacific , Europe & Central Asia had the highest gdp from 1995 - 2015. We could also see that North america and Europe & Central Asia has a little bit of a similar growth until around 2005. I also decided to make a little bit of a change and I chose the PRGn color palette instead of the Set2. I also chose to do the outline from black instead of the white color because it highlights the regions better in the graph.

Below I attached the right format.

dot_plot_ry <- region_year |>
  ggplot(text = paste("Region:", region)) +
  geom_line(aes(x = year, y = gdp)) +
  geom_area(aes(x = year, y = gdp, fill = region), colour = "white") +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "Gross Domestic Product by World Bank Region", 
       x = "Year",
       y = "GDP in Trillion)",
      color = "Region") +
  theme_dark(base_size = 09) +
  theme(plot.title = element_text(hjust = 0.10))
dot_plot_ry