Data by country
- world map and color the countries by life expectancy using the 2007 gapminder data
# view the first 12 region names in country.map
data(country.map, package = "choroplethrMaps")
head(unique(country.map$region), 12)
## [1] "afghanistan" "angola" "azerbaijan" "moldova" "madagascar"
## [6] "mexico" "macedonia" "mali" "myanmar" "montenegro"
## [11] "mongolia" "mozambique"
# prepare dataset
data(gapminder, package = "gapminder")
plotdata <- gapminder %>%
filter(year == 2007) %>%
rename(region = country,
value = lifeExp) %>%
mutate(region = tolower(region)) %>%
mutate(region = recode(region,
"united states" = "united states of america",
"congo, dem. rep." = "democratic republic of the congo",
"congo, rep." = "republic of congo",
"korea, dem. rep." = "south korea",
"korea. rep." = "north korea",
"tanzania" = "united republic of tanzania",
"serbia" = "republic of serbia",
"slovak republic" = "slovakia",
"yemen, rep." = "yemen"))
head(plotdata)
## # A tibble: 6 × 6
## region continent year value pop gdpPercap
## <chr> <fct> <int> <dbl> <int> <dbl>
## 1 afghanistan Asia 2007 43.8 31889923 975.
## 2 albania Europe 2007 76.4 3600523 5937.
## 3 algeria Africa 2007 72.3 33333216 6223.
## 4 angola Africa 2007 42.7 12420476 4797.
## 5 argentina Americas 2007 75.3 40301927 12779.
## 6 australia Oceania 2007 81.2 20434176 34435.
library(choroplethr)
country_choropleth(plotdata)
- choroplethr functions return ggplot2 graphs. Let’s make it a bit more attractive by modifying the code with additional ggplot2 functions.
country_choropleth(plotdata,
num_colors=9) +
scale_fill_brewer(palette="YlOrRd") +
labs(title = "Life expectancy by country",
subtitle = "Gapminder 2007 data",
caption = "source: https://www.gapminder.org",
fill = "Years")
