DSlabs Datasets Homework

Loading in the libraries

library(tidyverse)

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library(dslabs)

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library(plotly) # for interactivity 

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Attaching package: 'plotly'

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data("gapminder")

Filtering

gapminder1 <- gapminder |> filter(!is.na(life_expectancy),country %in% c("China", "Cambodia", "Mali", "Pakistan", "United States", "Japan", "Ethiopia","Rwanda" )) # creating a subset data set by filtering specific countries and removing NA values 

Plotting

p1 <- gapminder1 |>
  ggplot(aes(x = year, y = life_expectancy, color = country)) + 
  geom_line()+ # making a line chart for the filtered countries since 1960
  scale_color_brewer(palette = "Dark2")+ # choosing Dark2 color palette
  theme_minimal() + # for a minimalistic theme
  labs(x = "Year", 
       y = "Life Expectancy(in years)",
       title = "Life Expectancy of Different Countries Since 1960",
       color = "Country") # labels

p1 <- ggplotly(p1) # using plotly for interactivity to find out more information at a specific point 
p1

Summary

For my visualization, I used the “gapminder” data set from the DS labs package. I firstly wanted to create an Alluvial of the life expectancy of the countries across the years, but I ran into difficulties so I decided to plot a line chart instead. I started off by creating a subset data set by filtering for specific countries and removing NA values from the original data set. Then, I plotted the line chart since I had trouble making an alluvial. Finally, I incorporated interactivity to mouse over the lines and find out more information. An interesting and unfortunate insight I found is the plummeting of life expectancy for Cambodia and Rwanda in 1977 and 1994 respectively. After further research, I found out this was due to genocides in both countries. This highlights the detrimental and immense effect the tragedies had on the population.