Neste trabalho iremos elaborar um mapa por município no Estado do Rio de Janeiro na variável quantitativa “Densidade”, com base no banco de dados chamado “BasesMunicipios”.
library(readxl)
library(flextable)
library(dplyr)
library(ggplot2)
library(geobr)
library(readxl)
BasesMunicipios <- read_excel("C:/Users/eduar/Base_de_dados-master/BasesMunicipios.xlsx")
View(BasesMunicipios)
BasesMunicipios$`COD IBGE2` = as.numeric(BasesMunicipios$`COD IBGE2`)
class(BasesMunicipios$`COD IBGE2`)
## [1] "numeric"
mapa = geobr::read_municipality(code_muni = 33)
##
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 10 B
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 8.1 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 16 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 24 kB
Downloading: 32 kB
Downloading: 32 kB
Downloading: 32 kB
Downloading: 32 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 40 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 49 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 57 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 65 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 73 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 81 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 89 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 97 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 110 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 120 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 130 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 140 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 150 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 160 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 170 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 180 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 190 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 200 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 210 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 220 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 230 kB
Downloading: 240 kB
Downloading: 240 kB
Downloading: 240 kB
Downloading: 240 kB
BasesMunicipios = BasesMunicipios %>% rename(code_muni= `COD IBGE2`)
class(mapa$code_muni)
## [1] "numeric"
class(BasesMunicipios$code_muni)
## [1] "numeric"
dados_mais_mapa = mapa %>% left_join(BasesMunicipios)
mapa = join_by(code_muni)
dados_mais_mapa %>% ggplot() +
geom_sf(aes(fill=Densidade)) +
scale_fill_distiller(palette = "BrBG",
direction = 1,
name="Densidade")
O banco de dados utilizado como base para redigir este trabalho nos apresenta 92 registros de municípios do estado do Rio de Janeiro com 31 variáveis. Após relacionar os municípios do estado com a variável Densidade, através do mapa elaborado conclui-se que a maior densidade no estado do Rio de Janeiro se dá nos municípios da área Metropolitana. Enquanto a menor densidade é observada nos municípios da região Noroeste Fluminense e Serrana.