Presentation Slides

Inspiration

Gapminder Website

Data

  • Data downloaded straight from the Gapminder website
  • Compiled data from Gapminder World, World Bank, and some manually compiled
  • Life Expectancy, Child Mortality, Population, Babies per Woman

What We Were Working With

Research Question

Can we manipulate the data and visualizations such that a user could view these factors in a customizable manner where they can choose which countries and how to visualize this data?

High-Level Goal

We wanted to:
- show the data on the gapminder website, but in a different way
- create a shiny app that would allow users to pick the visualization that they best understood
- allow for between group and within group comparison
- add our own plots that could add to understanding the data and comparison

The Plots

Visualizing data geospatially

Visualizing data geospatially with fixed legend

Singular country visualizations static

Singular country visualizations dynamic

Comparing countries animation

Time Series/ARIMA Plot

year life_exp
2019 83.03873
2020 82.97761
2021 82.91662
2022 82.85578
2023 82.79509
2024 82.73453
2025 82.67411
2026 82.61383
2027 82.55370
2028 82.49370
2029 82.43385
2030 82.37413
2031 82.31455
2032 82.25512
2033 82.19582
2034 82.13665
2035 82.07763
2036 82.01874
2037 81.95999
2038 81.90138
2039 81.84290
2040 81.78456
2041 81.72636
2042 81.66829
2043 81.61036
2044 81.55256
2045 81.49489
2046 81.43736
2047 81.37997
2048 81.32270
2049 81.26558
2050 81.20858
2051 81.15172
2052 81.09499
2053 81.03839
2054 80.98192
2055 80.92559
2056 80.86938
2057 80.81331
2058 80.75736
2059 80.70155
2060 80.64587
2061 80.59032
2062 80.53489
2063 80.47960
2064 80.42443
2065 80.36940
2066 80.31449
2067 80.25971
2068 80.20505
2069 80.15053
2070 80.09613
2071 80.04185
2072 79.98771
2073 79.93369
2074 79.87979
2075 79.82603
2076 79.77238
2077 79.71886
2078 79.66547
2079 79.61220
2080 79.55905
2081 79.50603
2082 79.45313
2083 79.40036
2084 79.34770
2085 79.29518
2086 79.24277
2087 79.19048

Some Limitations

  • animations take a really long time to render in shiny, so users are limited by the speed of their computers to see that visualization
  • some countries do not have data and do not show up on the visualizations
  • some countries have data for certain years but not others

Conclusions

  • Created a shiny application that allows users to visualize population, fertility, life expectancy, and mortality in a customizable manner
  • Life expectancy and population has increased, fertility and mortality have decreased.
  • We were able to see drops in life expectency during disasters such as World War II and the Spanish flu pandemic.

Our Shiny App