Advanced Tidyverse Plots

library(tidyverse)
library(patchwork)
library(RColorBrewer)

worldHappiness = read.csv('worldHappiness.csv')
head(worldHappiness)

##   region      long      lat group order Ladder.score Social.support
## 1  Aruba -69.89912 12.45200     1     1           NA             NA
## 2  Aruba -69.89571 12.42300     1     2           NA             NA
## 3  Aruba -69.94219 12.43853     1     3           NA             NA
## 4  Aruba -70.00415 12.50049     1     4           NA             NA
## 5  Aruba -70.06612 12.54697     1     5           NA             NA
## 6  Aruba -70.05088 12.59707     1     6           NA             NA
##   Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1                      NA                           NA         NA
## 2                      NA                           NA         NA
## 3                      NA                           NA         NA
## 4                      NA                           NA         NA
## 5                      NA                           NA         NA
## 6                      NA                           NA         NA
##   Perceptions.of.corruption Ladder.score.in.Dystopia
## 1                        NA                       NA
## 2                        NA                       NA
## 3                        NA                       NA
## 4                        NA                       NA
## 5                        NA                       NA
## 6                        NA                       NA

1. Reproduce a world map with countries colored by corruption

# Let's create a map with world_happiness
ggplot(data =worldHappiness %>% arrange(order),
       aes(x=long,y=lat,
           group=group,
           fill=Perceptions.of.corruption))+
    geom_polygon(color='black',alpha=.6,linewidth=.1)+
    coord_cartesian(ylim=c(-55,90))+
    theme_bw()+
    scale_fill_gradientn("Perception of\nCorruption",colors = rev(brewer.pal(9,name="RdYlBu")),
                         na.value='black')+
    labs(x="Longitude", y = "Latitude",
         title = "Perceptions of Corruption by Country",
         subtitle = "(Data provided by World happiness Report)",
         caption = "Note: Antarctica has been removed, but rest assured\n that penguins are generally pretty happy.")

?geom_polygon
?scale_fill_gradientn
?brewer.pal

2. Make your own plot with new column data and colors

ggplot(data = worldHappiness %>%
         arrange(order),
       aes(x = long,
           y = lat,
           group = group,
           fill = Freedom.to.make.life.choices)) + # Life choice freedom values
  geom_polygon(color = 'black',
               alpha = 0.7,
               linewidth = 0.15) +
  coord_cartesian(ylim = c(-55, 90)) +
  theme_minimal() +
  scale_fill_gradientn('Freedom to Make\nLife Choices',
                       colors = brewer.pal(9,
                                           name = 'RdYlGn'), # Red, yellow, green value spectrum
                       na.value = 'white') + # Black --> white NA countries
  labs(x = 'Longitude',
       y = 'Latitude',
       title = 'Freedom to Make Life Choices by Country',
       subtitle = '(Data provided by World Happiness Report)')