pib_municipios.r

#--- libraries

library(readxl)
library(sf)
library(tidyverse)

#--- data

pib <- read_excel("pib.xlsx") %>% 
  mutate(code = as.numeric(code))

br <- geobr::read_municipality("all", year = 2018)

#--- plot

br %>% 
  left_join(pib, by=c('code_muni'='code')) %>% 
  mutate(gdppc2002 = cut(gdppc2002, 
                         breaks=c(-Inf, 5, 10, 15, 20, 25, 30, 50, Inf),
                         labels=c("<5", "5-10", "10-15", "15-20", "20-25", "25-30", "30-50", ">50")),
         gdppc2017 = cut(gdppc2017, 
                         breaks=c(-Inf, 5, 10, 15, 20, 25, 30, 50, Inf),
                         labels=c("<5", "5-10", "10-15", "15-20", "20-25", "25-30", "30-50", ">50"))) %>% 
  st_as_sf() %>% 
  ggplot() +
  geom_sf(aes(fill=gdppc2002), col=NA) +
  scale_fill_viridis_d(na.value="grey80") +
  ggthemes::theme_map()