#extrair da base de dados
library(readxl)
## Warning: package 'readxl' was built under R version 4.2.3
BasesMunicipios <- read_excel("C:/Users/14086606798/Desktop/Base_de_dados-master/BasesMunicipios.xlsx")
View(BasesMunicipios)
#blibliotecas que serão usadas para o mapa.
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.2.3
library(geobr)
## Warning: package 'geobr' was built under R version 4.2.3
## Loading required namespace: sf
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.2.3
##
## 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
library(RColorBrewer)
library(sf)
## Warning: package 'sf' was built under R version 4.2.3
## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(plotly)
## Warning: package 'plotly' was built under R version 4.2.3
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
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## filter
## The following object is masked from 'package:graphics':
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## layout
#Passo 3 - criar o mapa do estado do Rio de janeiro.
#para ler os municipos do RJ
desenho_municipios = read_municipality(code_muni = "RJ", showProgress = FALSE)
## Using year 2010
class(BasesMunicipios)
## [1] "tbl_df" "tbl" "data.frame"
class(desenho_municipios)
## [1] "sf" "data.frame"
plot(desenho_municipios)
class(desenho_municipios$code_muni)
## [1] "numeric"
class(BasesMunicipios$`COD IBGE`)
## [1] "numeric"
BasesMunicipios$`COD IBGE2` = as.numeric(BasesMunicipios$`COD IBGE2`)
#aq parou
BasesMunicipios1 <-desenho_municipios %>% left_join(BasesMunicipios, by = c( "code_muni"="COD IBGE2"))
summary(BasesMunicipios1$Esperancadevida)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 71.93 73.26 73.74 73.96 74.83 76.27
mapa3 = ggplot() +
geom_sf(data=BasesMunicipios1, aes(fill=Esperancadevida)) +
scale_fill_distiller(palette = "Reds", direction = 1, name="esperança de vida ao nascer",
limits = c(71,77))
ggplotly(mapa3)
hipotese 1 : A região N-ENE do estado do Rio de Janeiro possui menos esperança de vida ao nascer do que a região SO-NO do estado do Rio de janeiro.
hipotese 2 : A região metropolitana possui mais esperança de vida ao nascer do que as demais regiões, entretanto existem intraregiões que possuem uma esperança de vida alta assim como a região metropolitana.
hipotese 3 : Os municipios industrializados e os que possuem a uma melhor qualidade de vida possuem uma maior esperança de vida ao nascer do que os demais municipios.