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Arboviroses_Geocoding.R
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rm(list =ls())
##### Definindo diretório de trabalho, caso tenha que trabalhar em Windows, acertar o diretório ####
setwd("/home/gustavo/Área de Trabalho/Análise_de_Dados/")
############################################################################################
#### Definindo o objeto RS para servir de apoio para ##################################
#### buscar dados de todas as RS. Usar 1, 2, 3..., 21, 22 ############################
############################################################################################
RS <- 22 ##### Deve-se colocar AQUI a Regional
#### libraries a serem utilizadas ###
library(foreign)
library (dplyr)
library(stringr)
library(tidyr)
library(stringi)
library(tidygeocoder)
library(usethis)
#### Importando as bases de dados para formulação do Informe Epidemiológico ####
#### As Bases IBGE são planilhas contendo os nomes do municípios e o ####
#### código do IBGE (entre outros dados). ####
#### São as bases para que os for loops funcionem. ####
BASE_IBGE<-read.table(file="Base_de_Dados/Auxiliares/Planilha_Base_IBGE.csv",
header=TRUE,
sep=",")
BASE_IBGE_BRASIL <- read.csv (file = "Base_de_Dados/Auxiliares/Planilha_Base_IBGE_BRASIL.csv",
header = TRUE,
sep = ",")
###### Criando objeto ID_REG. Será utilizado para selecionar
###### RS no DBF do SINAN ONLINE.
ID_REG <- as.data.frame(BASE_IBGE[which(BASE_IBGE$RS == RS), 6])
ID_REG <- as.numeric(ID_REG[1,1])
#### Estabelecendo o número de municípios em cada RS
nrow <- NROW(BASE_IBGE[which(BASE_IBGE$RS == RS), 1])
####### Lista de Notificações com Coordenadas ######
SINAN_Coordenadas_001 <- read.csv(file = "Tabulacoes_R/Arboviroses/RS22_23_24_SINAN_DECODIFICADO.csv",
header = TRUE,
sep = ",")
SINAN_Coordenadas_002 <- read.csv(file = "Base_de_Dados/Auxiliares/SINAN_Coordenadas_DENGUE.csv",
header = TRUE,
sep = ",")
#### Base DBF do SINAN. Deve-se baixá-las, renomeá-las e salvá-las no diretório correto ######
DENGON2023 <- read.dbf(file = "Base_de_Dados/DBF/DENGON2023.dbf",
as.is = FALSE) %>% select(ID_REGIONA, ID_RG_RESI, SEM_PRI, NU_NOTIFIC, NM_PACIENT,
NM_LOGRADO, NU_NUMERO, NM_BAIRRO, NU_CEP, ID_MN_RESI, SG_UF)
DENGON2024 <- read.dbf(file = "Base_de_Dados/DBF/DENGON2024.dbf",
as.is = FALSE) %>% select(ID_REGIONA, ID_RG_RESI, SEM_PRI, NU_NOTIFIC, NM_PACIENT,
NM_LOGRADO, NU_NUMERO, NM_BAIRRO, NU_CEP, ID_MN_RESI, SG_UF)
###################################################################################################
### ###
### Transformando coluna de semana epidemiológica de fator para numérica ###
### (passando por texto, se for direto, ela transforma ###200905 em 06. ###
### Seria possível realizar busca de SE passando direto de fator para numérica ###
### utilizando as.integer (DENGON2009 ###%>% filter(ID_MUNICIP == 410165, ###
### SEM_PRI == 6) -1, para buscar SE 05? ###
### ###
### Será usado para buscar semanas epidemiológicas ###
###################################################################################################
###
DENGON2023$SEM_PRI <-as.numeric(as.character(DENGON2023$SEM_PRI)) ###############################
###
DENGON2024$SEM_PRI <-as.numeric(as.character(DENGON2024$SEM_PRI)) ###############################
###### Usando rbind para juntar as duas metades do período sazonal ######
AUX01 <- DENGON2023 %>%
filter(ID_REGIONA == ID_REG | ID_RG_RESI == ID_REG,
SEM_PRI >= 202331)
AUX02 <- DENGON2024 %>%
filter(ID_REGIONA == ID_REG | ID_RG_RESI == ID_REG,
SEM_PRI <=202430)
SINAN_DENGUE_RS <- rbind(AUX01,
AUX02)
SINAN_DENGUE_RS <- SINAN_DENGUE_RS[, c(4:11)]
#### For loop visando buscar o nome do município #####
AUX02 <- data.frame(COD = SINAN_DENGUE_RS[,7],
Municipio = NA)
for (i in SINAN_DENGUE_RS[,7]){
AUX02[which(AUX02$COD == i), 2] <- BASE_IBGE_BRASIL[which(BASE_IBGE_BRASIL$Código.Município.Completo == i),13]
}
SINAN_DENGUE_RS[, 7] <- AUX02[, 2]
AUX01 <- data.frame(UF = SINAN_DENGUE_RS[, 8],
Estado = NA)
AUX01 <- AUX01 %>% mutate(UF = case_when(UF == 41 ~ "Parana",
UF != 41 ~ "Não Procurar no API"))
SINAN_DENGUE_RS[, 8] <- as.data.frame(AUX01[,1])
AUX02 <- data.frame(UF = SINAN_DENGUE_RS[, 8],
Estado = NA)
AUX02 <- AUX02 %>% mutate(UF = case_when(UF == "Parana" ~ "Brasil",
UF != "Paraná" ~ "Não Procurar no API"))
SINAN_DENGUE_RS$Pais <- AUX02[,1]
SINAN_DENGUE_RS <-SINAN_DENGUE_RS %>% filter(NM_LOGRADO != is.na(SINAN_DENGUE_RS$NM_LOGRADO),
NU_NUMERO != is.na(SINAN_DENGUE_RS$NU_NUMERO),
NU_NUMERO != "SN",
NU_CEP != is.na(SINAN_DENGUE_RS$NU_CEP))
SINAN_Coord <- SINAN_Coordenadas_001 %>% filter(Latitude != is.na(SINAN_Coordenadas_001$Latitude))
SINAN_Coord <- anti_join(SINAN_Coordenadas_001, SINAN_Coord, by = c("SINAN" = "SINAN"))
SINAN_Coord$SINAN <- as.factor(SINAN_Coord$SINAN)
SINAN_Coord <- inner_join(SINAN_DENGUE_RS, SINAN_Coord, by = c("NU_NOTIFIC" = "SINAN"))
SINAN_Coord <- SINAN_Coord[, c(1:9)]
assign(paste0("RS", RS, "_23_24_SINAN_LOGRADOUROS"),
SINAN_Coord)
write.csv(SINAN_Coord,
"Base_de_Dados/Auxiliares/RS_23_24_SINAN_LOGRADOUROS.csv",
row.names = FALSE)
RS22_23_24_SINAN_LOGRADOUROS <- read.csv("Base_de_Dados/Auxiliares/RS_23_24_SINAN_LOGRADOUROS.csv",
header = TRUE,
sep = ",",
fileEncoding = "windows-1256")
RS22_23_24_SINAN_LOGRADOUROS[, 7] <- SINAN_Coord[, 7]
AUX01 <- as.data.frame(apply(RS22_23_24_SINAN_LOGRADOUROS[, 3:9], 1, paste, collapse =", "))
AUX01[,2] <- RS22_23_24_SINAN_LOGRADOUROS[,1]
colnames(AUX01)[1] <- "Endereço"
colnames(AUX01)[2] <- "SINAN"
AUX01 <- AUX01[, c(2, 1)]
teste <- geocode(AUX01,
address = Endereço,
method = "google")
Notificacoes <- read.csv(file = "Tabulacoes_R/Arboviroses/RS22_23_24_SINAN_PROVAVEIS_DECODIFICADO.csv",
header = TRUE,
sep = ",")
Notificacoes <- left_join(Notificacoes, teste, by = c("SINAN" = "SINAN"))
write.csv(teste,
"/home/gustavo/Área de Trabalho/teste.csv",
row.names=FALSE)
edit_r_environ()