mapas

# Importar datos

mapa <- read.csv2("map_2022.csv")

library(ggplot2)

planeta <- ggplot(mapa, aes(x=long, y=lat, group = as.factor(group)))
planeta <- planeta + geom_polygon(aes(fill= sub.region))
planeta <- planeta + scale_fill_viridis_d("viridis")
# planeta <- planeta + scale_fill_brewer(type ="gual", n=12)
planeta

#Asignaciòn decolores por equidad

datos <- read.csv2("Coefficient_of_human_inequality_2010-2019.csv")

datos2019 <- subset(datos, datos$variable == 2019)


library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#Combinar los dataframes

mapaInequida <- left_join(mapa,datos2019,by= c("ISO3"="ISO3"))


planeta <- ggplot(mapaInequida, aes(x=long, y=lat, group = as.factor(group)))
planeta <- planeta + geom_polygon(aes(fill= as.numeric(value)))
planeta <- planeta + scale_fill_viridis_c(option="viridis")
planeta