Shiny app pitch: A reactive shiny app utilising Gapminder data (R dataset)

Richard Hardy
2020-02-01

Investigating changes in Gross Domestic Product and life expectancy globally

This app has two parts:

Part 1:

  • The app displays countries' GDP per capita and life expectancy in years from birth as recorded across 5-year intervals between 1952 and 2007.

  • Moving the slider (or clicking through using the right arrowkey), reveals the general improvements in health and wealth globally over the second half of the 20th century.

Part 2:

  • Select a country of interest and the app returns a line plot of the GDP per capita for that country over time, as well as the percentage change in GDP per capita from 1952 to 2007. Similarly for life expectancy.

Part 1: sample output for 1952

  • The world plot displays countries by continent, with bubble size an indication of their population. The example below is produced by setting the slider to 1952.

plot of chunk plot

  • A note on the dataset: continents include Oceania, containing Australia and New Zealand in this dataset. These two countries are omitted from the global view.

Part 2: Sample output: China

  • GDP per capita and life expectancy are shown over time when a country is selected from the dropdown menu. Selecting 'China' from the country box produces the following GDP plot for China.

plot of chunk china_plot

  • The app also returns from a reactive expression, the % change in GDP between 1952 and 2007, given as: 
[1] "% 1138.39"

  • Thank you for watching and I hope you enjoy exploring the Gapminder data with this app

  • Hans Rosling's TED talk is the inspiration for this project, and r ?gapminder will give the source code used to build the global plot. Enjoy!

reactive expression code in server.R : GDP change (%) calculation

shinyServer(function(input, output) {

   percentchange <- reactive({

       countryInput <- input$country
       (gapminder$gdpPercap[gapminder$country == 
        as.character(countryInput) & gapminder$year == 2007] -
        gapminder$gdpPercap[gapminder$country == 
        as.character(countryInput) & gapminder$year == 1952] ) / 
        gapminder$gdpPercap[gapminder$country == 
        as.character(countryInput) & gapminder$year == 1952] * 100

    })
})