World Tile Grid of Quintiles of GDP per capita, 1995-2015 (https://twitter.com/matschnetzer/status/1036879668606107648)

wtg_gdppc.r

library(worldtilegrid)
library(plyr)
library(tidyverse)
library(countrycode)
library(viridis)
library(msthemes)

# Get GDP per capita data from UNdata http://bit.ly/2N4hUlV
data <- read.csv("undata.csv",dec=".") %>% rename(Country=Country.or.Area) %>% select(-Value.Footnotes)

# Check compatibility for countrycode-transformation
data %>% filter(!Country %in% countrycode::codelist$country.name.en) %>% 
  select(Country)

# Harmonize country names manually
data <- data %>% mutate_at(vars(Country),funs(iconv(., "UTF-8", "WINDOWS-1252"))) %>% 
  mutate_at(vars(Country),funs(gsub(" and "," & ",.))) %>% 
  mutate_at(vars(Country), funs(revalue(., c("Côte d'Ivoire"="Côte d’Ivoire",
                                             "Cabo Verde" = "Cape Verde",
                                             "Congo" = "Congo - Brazzaville",
                                             "Dem. Rep. Congo" = "Congo - Kinshasa",
                                             "Czech Republic" = "Czechia",
                                             "Hong Kong SAR, China" = "Hong Kong SAR China",
                                             "Korea" = "South Korea",
                                             "Kyrgyz Republic" = "Kyrgyzstan",
                                             "Lao PDR" = "Laos",
                                             "Macao SAR, China" = "Macau SAR China",
                                             "Micronesia" = "Micronesia (Federated States of)",
                                             "Myanmar" = "Myanmar (Burma)",
                                             "São Tomé & Principe" = "São Tomé & Príncipe",
                                             "Slovak Republic" = "Slovakia",
                                             "St. Vincent & the Grenadines" = "St. Vincent & Grenadines",
                                             "The Bahamas" = "Bahamas",
                                             "The Gambia" = "Gambia"))))

# Keep only countries with data for both years or all countries (line 38)
data <- data %>% filter(Country %in% countrycode::codelist$country.name.en) %>%
  group_by(Country) %>% filter(n()>1) %>% ungroup() %>%
  group_by(Year) %>% mutate(quintile=as.factor(ntile(Value,5))) %>% 
  mutate_at(vars(quintile),funs(revalue(.,c("1"="Poorest Quintile",
                                            "2"="2nd Quintile",
                                            "3"="3rd Quintile",
                                            "4"="4th Quintile",
                                            "5"="Richest Quintile"))))
  
data$ctycode <- countrycode(data$Country,"country.name","iso3c")

png("wtg_gdppc.png", width = 9, height = 6, units = 'in', res = 300)
ggplot(data, aes(country = ctycode, fill = quintile)) +
  geom_wtg() +
  geom_text(aes(label = stat(alpha.2)), stat="wtg", size=2) + # re-compute the stat to label
  coord_equal() +
  facet_wrap(~Year) +
  scale_fill_manual(values=msl_palette,na.value=alpha("#c3c3c3",0.3), name="") +
  theme_ms(grid=F,dark=T,alttf=T) +
  theme(axis.text = element_blank(),legend.position = "bottom",plot.margin=margin(10,10,10,10)) +
  labs(title="Which countries climbed the income ladder?",subtitle="Quintiles of GDP per capita (PPP)",caption="Data: UN, World Bank. Figure: @matschnetzer")
dev.off()