World’s Fairs

I’ve always wanted to play with making a map-based visualization
TidyTuesday
R
Maps
Author

Steven Wolf

Published

August 14, 2024

This week’s #TidyTuesday is all about World’s Fairs, and I have always wanted to figure out how to make a visualization with a map. So this is the week that I try that out. In addition to the data from Wikipedia in the given dataset, I have also downloaded S. Altan’s Kaggle Dataset: Countries by Continent.

library(tidyverse)
library(gt)
library(rworldmap)
library(paletteer)
library(RColorBrewer)

load('worldsFairs.Rdata')
source('helperFunctions.R')

Plan and data cleaning

My plan is to create visualizations that look at different world’s fair properties by continent and country. I will clean the data a bit, and then generate some tables.

Continental breakdown of World’s fair

fairData = left_join(worlds_fairs,countryContinent, by='country')

# Replace missing continents by hand
missing = c("Europe","Oceania","Europe","Europe","Asia")
fairData$continent[is.na(fairData$continent)] = missing

fairDataByContinent = fairData |>
  mutate(
    duration = calcFLMV(start_month, start_year, end_month, end_year) 
  ) |> group_by(continent) |> 
  summarize(
    N = n(),
    across(where(is.numeric), ~ mean(.x,na.rm=TRUE))
  ) |>
  select(-c('start_month','start_year','end_month','end_year'))
World's Fair by the numbers
Average properties by host continent
Continent Number Hosted Visitors (M) Avg. cost (M$) Land area (hectares) Number of attending countries Fair duration (mo)
Asia 11 26.0 1,999.7 169.4 103.5 5.1
Europe 43 12.7 547.8 77.1 42.7 5.2
North America 14 20.9 281.4 144.2 29.8 8.9
Oceania 2 9.9 320.5 32.5 34.5 7.0

#TidyTuesday • Sources: Wikipedia and S. Altan Kaggle dataset
Missing data was ignored from averages taken.

Country breakdown of world’s fair

fairDataByCountry = fairData |>
  mutate(
    duration = calcFLMV(start_month, start_year, end_month, end_year) 
  ) |> group_by(country) |> 
  summarize(
    N = n(),
    across(where(is.numeric), ~ mean(.x,na.rm=TRUE))
  ) |>
  select(-c('start_month','start_year','end_month','end_year'))
World's Fair by the numbers
Average properties by host country
Country Number Hosted Visitors (M) Fair cost (M$) Land area (hectares) Number of attending countries Fair duration (mo)
Australia 1 18.5 625.0 40.0 36.0 7.0
Austria-Hungary 1 7.2 95.0 233.0 35.0 6.0
Belgium 7 16.4 558.8 117.7 26.9 6.7
Bulgaria 3 1.0 — 5.8 40.5 1.7
Canada 2 36.2 371.0 217.5 58.5 6.5
Colony of Victoria 1 1.3 16.0 25.0 33.0 7.0
Finland 1 — — 0.1 25.0 1.0
France 9 21.6 556.0 67.9 34.5 5.4
Germany 1 18.1 3,400.0 160.0 155.0 5.0
Haiti 1 — — 24.0 15.0 7.0
Hungary 1 1.9 — 35.0 52.0 2.0
Israel 2 — — 29.8 13.0 2.0
Italy 8 7.7 26.0 68.3 44.1 5.5
Japan 4 27.5 30.0 175.7 69.8 7.0
Kazakhstan 1 4.1 — 25.0 115.0 4.0
People's Republic of China 1 73.1 4,200.0 523.0 192.0 6.0
Portugal 1 10.1 — 50.0 143.0 5.0
South Korea 2 11.3 1,769.0 57.5 122.0 4.0
Spain 4 13.9 21.0 101.2 68.8 7.2
Sweden 3 — — 0.5 18.3 2.3
United Arab Emirates 1 24.1 — 438.0 192.0 6.0
United Kingdom 2 6.0 83.5 12.5 32.0 7.0
United States 11 18.2 265.1 141.8 25.6 9.5
West Germany 2 2.1 — 51.6 24.5 4.0

#TidyTuesday • Sources: Wikipedia and S. Altan Kaggle dataset
Missing data was ignored from averages taken. Blank values imply no data exists for that country and quantity.

Putting this on a map

worldFairMap <- joinCountryData2Map(fairDataByCountry, joinCode = 'NAME',
                                    nameJoinColumn = "country")
21 codes from your data successfully matched countries in the map
3 codes from your data failed to match with a country code in the map
222 codes from the map weren't represented in your data
par(mar=c(0,0.1,2,0.1),xaxs="i",yaxs="i",cex.main=2)
attendMap = mapCountryData(worldFairMap, addLegend=FALSE,
                           nameColumnToPlot = 'attending_countries',
                           catMethod = "pretty",
                           colourPalette = brewer.pal(10,"Purples"),
                           oceanCol='lightblue',missingCountryCol = 'white',
                           mapTitle="Country attendance")
You asked for 7 categories, 10 were used due to pretty() classification
Warning in brewer.pal(10, "Purples"): n too large, allowed maximum for palette Purples is 9
Returning the palette you asked for with that many colors
Warning in rwmGetColours(colourPalette, numColours): 9 colours specified and 10
required, using interpolation to calculate colours
do.call(addMapLegend, c(attendMap, legendWidth=0.5, legendMar=5))

costMap = mapCountryData(worldFairMap, nameColumnToPlot = 'cost',
               catMethod = "pretty", addLegend=FALSE,
               colourPalette = brewer.pal(10,"Greens"),
               oceanCol='lightblue',missingCountryCol = 'white',
               mapTitle="Cost in Millions")
You asked for 7 categories, 9 were used due to pretty() classification
Warning in brewer.pal(10, "Greens"): n too large, allowed maximum for palette Greens is 9
Returning the palette you asked for with that many colors
do.call(addMapLegend, c(costMap, legendWidth=0.5, legendMar=5))

areaMap = mapCountryData(worldFairMap, nameColumnToPlot = 'area',
               catMethod = "pretty", addLegend=FALSE,
               colourPalette = brewer.pal(10,"PuBuGn"),
               oceanCol='lightblue',missingCountryCol = 'white',
               mapTitle="Size of fairgrounds")
You asked for 7 categories, 6 were used due to pretty() classification
Warning in brewer.pal(10, "PuBuGn"): n too large, allowed maximum for palette PuBuGn is 9
Returning the palette you asked for with that many colors
Warning in rwmGetColours(colourPalette, numColours): 9 colours specified and 6
required, using interpolation to calculate colours
do.call(addMapLegend, c(areaMap, legendWidth=0.5, legendMar=5))

visitMap = mapCountryData(worldFairMap, nameColumnToPlot = 'visitors',
               catMethod = "pretty", addLegend=FALSE,
               colourPalette = brewer.pal(10,"OrRd"),
               oceanCol='lightblue',missingCountryCol = 'white',
               mapTitle="Fair Visitors by the Million")
You asked for 7 categories, 8 were used due to pretty() classification
Warning in brewer.pal(10, "OrRd"): n too large, allowed maximum for palette OrRd is 9
Returning the palette you asked for with that many colors
Warning in rwmGetColours(colourPalette, numColours): 9 colours specified and 8
required, using interpolation to calculate colours
do.call(addMapLegend, c(visitMap, legendWidth=0.5, legendMar=5))

timeMap = mapCountryData(worldFairMap, nameColumnToPlot = 'duration',
               catMethod = "pretty", addLegend=FALSE,
               colourPalette = brewer.pal(10,"OrRd"),
               oceanCol='lightblue',missingCountryCol = 'white',
               mapTitle="Length of the World's Fair")
You asked for 7 categories, 9 were used due to pretty() classification
Warning in brewer.pal(10, "OrRd"): n too large, allowed maximum for palette OrRd is 9
Returning the palette you asked for with that many colors
do.call(addMapLegend, c(timeMap, legendWidth=0.5, legendMar=5))