Working directory and Packages

First setting up working directory and downloading the necessary files

setwd("D:/Medium/Hispaniola_Fourth_article/API_NY.GDP.PCAP.CD_DS2_en_csv_v2_2627294/API_NY.GDP.PCAP.CD_DS2_en_csv_v2_2627294")
library(readr)

library (tidyverse)
library(ggbreak)
library(plotly)

data <- read_csv("API_NY.GDP.PCAP.CD_DS2_en_csv_v2_2627294.csv", 
                                                     skip = 3)

The data set contains GDP per capita for the entire world. We need to filter out Haiti and Dominican Republic dataset.

Hispaniola <- data %>% 
  filter (`Country Name` == "Haiti" | `Country Name` == "Dominican Republic")

Long to wide format

The data is in wide format. We need to make the data into long format.

Hispaniola_long <- Hispaniola %>% 
  select (-`Country Code`, -`Indicator Name`, -`Indicator Code`) %>% 
  pivot_longer(!`Country Name`, names_to = "Year", 
               values_to = "GDP Per Capita") 

Two rows (one for Haiti and one for Dominican Republic) has “X66” for their records. It looks like the data is missing for 2021 because 2021 has not ended yet. Let us remove those rowss

Hispaniola_long <- Hispaniola_long %>% 
  filter (Year != "X66")

Remember that Year is in ‘character’ format. We need to change into either ‘numberic’ or ‘date’ format

Hispaniola_long$Year <- as.numeric(Hispaniola_long$Year)

Plotting

plot <- ggplot (data = Hispaniola_long, aes (x = Year, y = `GDP Per Capita`, 
                       group = `Country Name`, 
                       color = `Country Name`)) +
  geom_line( size = 1.5)+
  theme_bw()

plot1 <- plot
  scale_x_continuous(labels = seq(1980, 2020,10 ))

## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

ggplotly(plot1)

Plotting

Hispaniola <- rbind(DominicanRepublic_2050, Haiti2050)

plot2 <- ggplot (data = Hispaniola, aes (x = Percent, y = ClassName, fill = Country))+
  geom_col(position = "dodge")+
  theme_bw()

ggplotly(plot2)