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Arboviroses_Informe.R
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Arboviroses_Informe.R
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rm(list =ls())
###############################################################################################################
###############################################################################################################
## ##
## Script elaborado em ambiente LINUX. Para replicação deve ser ajustado os caminhos dos diretórios ##
## ################################################################################################ ##
## ##
## Até o ponto em que é necessario autenticação de acesso à internet todos os passos abaixo são ##
## reprodutíveis em qualquer computador desde que os itens abaixo sejam executados corretamente. ##
## ############################################################################################# ##
## ##
## /home/gustavo/Área de trabalho/Análise_de_Dados/Arboviroses_Geral deve ser previamente rodado ##
## para que o script abaixo funcione. ##
## ############################################################################################ ##
## ##
###############################################################################################################
###############################################################################################################
########################### Localizações #########################
### Linhas Assunto Linhas Assunto
### 83 - 97 libraries ### 2459 - 2599 Canais Endêmicos
### 104 - 154 Bases de dados ###
### 156 - 2170 Tabelas período atual
### 2184 - 2444 Decodificação SINAN
### 2455 - 2504 Série histórica
##### Definindo diretório de trabalho, caso tenha que trabalhar em Windows, acertar o diretório ####
setwd("/home/gustavo/Área de Trabalho/Análise_de_Dados/")
###############################################################################################################
###############################################################################################################
## ##
## OS PASSOS ABAIXO (ATÉ LINHA 80) DEVEM SER SEGUIDOS PARA FUNCIONAMENTO!!! ##
## ################################################################################################ ##
## ##
## Lembrar que OBRIGATORIAMENTE deve ser baixados as bases DBF de 2009 até 2024 ##
## As bases DBF devem ser salvas no formato DENGON2009, DENGON2012... até DENGON2024 ##
## A base DENGON2024 deve ser baixada diariamente e salva no local correto para que ##
## o sistema esteja sempre atualizado!!! ##
## Os dados do LACEN devem ser baixados da GAL ##
## Estes arquivos devem ser alocados no diretório abaixo: ##
## /home/gustavo/Área de trabalho/Análise_de_Dados/Base_de_Dados/DBF ou /Base_de_Dados/LACEN ##
## ############################################################################################ ##
## ##
###############################################################################################################
###############################################################################################################
#############################################################################################################
###################################DEFININDO A SEMANA EPIDEMIOLÓGICA#########################################
#######################################E DATA DA BASE DBF####################################################
#############################################################################################################
##### Definir fonte para ser utilizada nos gráficos (ggplot ira buscar o objeto ####
##### Fonte para "labs(caption = Fonte...") ####
##### Importante para os gráficos terem a DATA em que a base DBF foi acessada ####
Fonte <- "Fonte: SINAN. BASE DBF acessada em 20/12/2023" ##### Fonte dos gráficos relacionados ao SINAN
Fonte_1 <- "Fonte: Lacen. Acesso em 19/12/2023" ##### Fonte dos gráficos relacionados ao LACEN
Fonte_2 <- "Fonte: Planilhas de Controle Municipais. Acesso em 20/12/2023" ##### Fonte dos gráficos relacionados às Planilhas Municipais
#### Objeto SE irá ser utilizado como auxiliar definidor de ponto ####
#### a partir do qual os histogramas de casos Notificados/Confirmados/Prováveis ####
#### nas últimas 10 semanas irá buscar os dados. ####
SE <- as.data.frame("1") ### Colocar a Semana Epidemiológica atual
SE <- as.numeric(SE)
############################################################################################
#### 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
#############################################################################################################
#############################################################################################################
###########################################################################################################
########### Definindo Períodos Epidemícos para serem excluídos do #####################################
########### cálculo para os canais endêmicos Regionais #####################################
########### COMENTAR AS LINHAS DOS PERÍODOS EPIDÊMICOS #####################################
###########################################################################################################
Periodos_Epidêmicos_RS <- c("2009/10",
"2010/11",
"2011/12",
"2012/13",
"2013/14",
"2014/15",
# "2015/16",
"2016/17",
"2017/18",
"2018/19",
# "2019/20",
"2020/21",
# "2021/22",
# "2022/23",
"2023/24"
)
Periodos_Epidêmicos_SEDE <- c("2009/10",
"2010/11",
"2011/12",
"2012/13",
"2013/14",
"2014/15",
"2015/16",
"2016/17",
"2017/18",
"2018/19",
# "2019/20",
"2020/21",
"2021/22",
"2022/23",
"2023/24"
)
#### libraries a serem utilizadas ###
library(patchwork)
library(foreign)
library (dplyr)
library (googlesheets4)
library (ggplot2)
###Não sei usar o httpuv!!!###
library (httpuv)
library(stringr)
library(lubridate)
#library(xlsx)
library(geobr)
library(ggspatial)
library(ggplot2)
library(tidyr)
#### 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])
#### 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, NU_NOTIFIC, ID_GEO1,ID_GEO2, ID_AGRAVO, ID_REGIONA, DT_NOTIFIC,
NU_ANO, SEM_NOT, DT_SIN_PRI, SEM_PRI, SG_UF_NOT, ID_MUNICIP, NM_PACIENT, DT_NASC,
NU_IDADE_N, CS_SEXO, CS_GESTANT, CS_ESCOL_N, NM_MAE_PAC, ID_MN_RESI, SG_UF, ID_RG_RESI,
NM_LOGRADO, NU_NUMERO, NM_BAIRRO, NU_CEP, CS_ZONA, DT_DIGITA, DT_INVEST, FEBRE, MIALGIA,
CEFALEIA, EXANTEMA, VOMITO, NAUSEA, DOR_COSTAS, CONJUNTVIT, ARTRITE, ARTRALGIA, PETEQUIA_N,
LEUCOPENIA, LACO, DOR_RETRO, DIABETES, HEMATOLOG, HEPATOPAT, RENAL, HIPERTENSA, ACIDO_PEPT,
AUTO_IMUNE, DT_SORO, RESUL_SORO, DT_PCR, RESUL_PCR_, SOROTIPO, CLASSI_FIN, CRITERIO,
TPAUTOCTO, COUFINF, COMUNINF, CO_BAINF, EVOLUCAO, HOSPITALIZ, DT_INTERNA, DT_OBITO,
DT_ENCERRA, DT_ALRM, ALRM_LETAR, ALRM_HEPAT, ALRM_LIQ, ALRM_HIPOT, ALRM_PLAQ, ALRM_VOM,
ALRM_SANG, ALRM_HEMAT, ALRM_ABDOM, DT_GRAV, GRAV_PULSO, GRAV_CONV, GRAV_ENCH, GRAV_INSUF,
GRAV_TAQUI, GRAV_EXTRE, GRAV_HIPOT, GRAV_HEMAT, GRAV_MELEN, GRAV_METRO, GRAV_SANG, GRAV_AST,
GRAV_MIOC, GRAV_CONSC, GRAV_ORGAO, MANI_HEMOR, EPISTAXE, GENGIVO, METRO, DS_OBS)
DENGON2024 <- read.dbf(file = "Base_de_Dados/DBF/DENGON2024.dbf",
as.is = FALSE) %>% select(ID_REGIONA, NU_NOTIFIC, ID_GEO1,ID_GEO2, ID_AGRAVO, ID_REGIONA, DT_NOTIFIC, NU_ANO,
SEM_NOT, DT_SIN_PRI, SEM_PRI, SG_UF_NOT, ID_MUNICIP, NM_PACIENT, DT_NASC, NU_IDADE_N,
CS_SEXO, CS_GESTANT, CS_ESCOL_N, NM_MAE_PAC, ID_MN_RESI, SG_UF, ID_RG_RESI, NM_LOGRADO,
NU_NUMERO, NM_BAIRRO, NU_CEP, CS_ZONA, DT_DIGITA, DT_INVEST, FEBRE, MIALGIA, CEFALEIA,
EXANTEMA, VOMITO, NAUSEA, DOR_COSTAS, CONJUNTVIT, ARTRITE, ARTRALGIA, PETEQUIA_N,
LEUCOPENIA, LACO, DOR_RETRO, DIABETES, HEMATOLOG, HEPATOPAT, RENAL, HIPERTENSA, ACIDO_PEPT,
AUTO_IMUNE, DT_SORO, RESUL_SORO, DT_PCR, RESUL_PCR_, SOROTIPO, CLASSI_FIN, CRITERIO,
TPAUTOCTO, COUFINF, COMUNINF, CO_BAINF, EVOLUCAO, HOSPITALIZ, DT_INTERNA, DT_OBITO,
DT_ENCERRA, DT_ALRM, ALRM_LETAR, ALRM_HEPAT, ALRM_LIQ, ALRM_HIPOT, ALRM_PLAQ, ALRM_VOM,
ALRM_SANG, ALRM_HEMAT, ALRM_ABDOM, DT_GRAV, GRAV_PULSO, GRAV_CONV, GRAV_ENCH, GRAV_INSUF,
GRAV_TAQUI, GRAV_EXTRE, GRAV_HIPOT, GRAV_HEMAT, GRAV_MELEN, GRAV_METRO, GRAV_SANG, GRAV_AST,
GRAV_MIOC, GRAV_CONSC, GRAV_ORGAO, MANI_HEMOR, EPISTAXE, GENGIVO, METRO, DS_OBS)
CHIKON2023 <- read.dbf("Base_de_Dados/DBF/CHIKON2023.dbf",
as.is = FALSE) %>% select(ID_REGIONA, NU_NOTIFIC, ID_GEO1,ID_GEO2, ID_AGRAVO, ID_REGIONA, DT_NOTIFIC, NU_ANO, SEM_NOT,
DT_SIN_PRI, SEM_PRI, SG_UF_NOT, ID_MUNICIP, NM_PACIENT, DT_NASC, NU_IDADE_N, CS_SEXO,
CS_GESTANT, CS_ESCOL_N, NM_MAE_PAC, ID_MN_RESI, SG_UF, ID_RG_RESI, NM_LOGRADO, NU_NUMERO,
NM_BAIRRO, NU_CEP, CS_ZONA, DT_DIGITA, DT_INVEST, FEBRE, MIALGIA, CEFALEIA, EXANTEMA, VOMITO,
NAUSEA, DOR_COSTAS, CONJUNTVIT, ARTRITE, ARTRALGIA, PETEQUIA_N, LEUCOPENIA, LACO, DOR_RETRO,
DIABETES, HEMATOLOG, HEPATOPAT, RENAL, HIPERTENSA, ACIDO_PEPT, AUTO_IMUNE, DT_SORO, RESUL_SORO,
DT_PCR, RESUL_PCR_, SOROTIPO, CLASSI_FIN, CRITERIO, TPAUTOCTO, COUFINF, COMUNINF, CO_BAINF, EVOLUCAO,
HOSPITALIZ, DT_INTERNA, DT_OBITO, DT_ENCERRA, DT_ALRM, ALRM_LETAR, ALRM_HEPAT, ALRM_LIQ, ALRM_HIPOT,
ALRM_PLAQ, ALRM_VOM, ALRM_SANG, ALRM_HEMAT, ALRM_ABDOM, DT_GRAV, GRAV_PULSO, GRAV_CONV, GRAV_ENCH,
GRAV_INSUF, GRAV_TAQUI, GRAV_EXTRE, GRAV_HIPOT, GRAV_HEMAT, GRAV_MELEN, GRAV_METRO, GRAV_SANG,
GRAV_AST, GRAV_MIOC, GRAV_CONSC, GRAV_ORGAO, MANI_HEMOR, EPISTAXE, GENGIVO, METRO, DS_OBS)
CHIKON2024 <- read.dbf("Base_de_Dados/DBF/CHIKON2024.dbf",
as.is = FALSE) %>% select(ID_REGIONA, NU_NOTIFIC, ID_GEO1,ID_GEO2, ID_AGRAVO, ID_REGIONA, DT_NOTIFIC, NU_ANO, SEM_NOT,
DT_SIN_PRI, SEM_PRI, SG_UF_NOT, ID_MUNICIP, NM_PACIENT, DT_NASC, NU_IDADE_N, CS_SEXO, CS_GESTANT,
CS_ESCOL_N, NM_MAE_PAC, ID_MN_RESI, SG_UF, ID_RG_RESI, NM_LOGRADO, NU_NUMERO, NM_BAIRRO, NU_CEP,
CS_ZONA, DT_DIGITA, DT_INVEST, FEBRE, MIALGIA, CEFALEIA, EXANTEMA, VOMITO, NAUSEA, DOR_COSTAS,
CONJUNTVIT, ARTRITE, ARTRALGIA, PETEQUIA_N, LEUCOPENIA, LACO, DOR_RETRO, DIABETES, HEMATOLOG,
HEPATOPAT, RENAL, HIPERTENSA, ACIDO_PEPT, AUTO_IMUNE, DT_SORO, RESUL_SORO, DT_PCR, RESUL_PCR_,
SOROTIPO, CLASSI_FIN, CRITERIO, TPAUTOCTO, COUFINF, COMUNINF, CO_BAINF, EVOLUCAO, HOSPITALIZ,
DT_INTERNA, DT_OBITO, DT_ENCERRA, DT_ALRM, ALRM_LETAR, ALRM_HEPAT, ALRM_LIQ, ALRM_HIPOT, ALRM_PLAQ,
ALRM_VOM, ALRM_SANG, ALRM_HEMAT, ALRM_ABDOM, DT_GRAV, GRAV_PULSO, GRAV_CONV, GRAV_ENCH, GRAV_INSUF,
GRAV_TAQUI, GRAV_EXTRE, GRAV_HIPOT, GRAV_HEMAT, GRAV_MELEN, GRAV_METRO, GRAV_SANG, GRAV_AST,
GRAV_MIOC, GRAV_CONSC, GRAV_ORGAO, MANI_HEMOR, EPISTAXE, GENGIVO, METRO, DS_OBS)
RS_Serie_Historica_Base <- read.csv(file = paste0("Tabulacoes_R/Arboviroses/RS", RS, "_Serie_Historica_Base.csv"),
header = TRUE,
sep = ",")
RS_CE_Notificados_Base <- read.csv(file = paste0("Tabulacoes_R/Arboviroses/RS", RS, "_CE_Notificados_Base.csv"),
header = TRUE,
sep = ",")
RS_CE_Confirmados_Base <- read.csv(file = paste0("Tabulacoes_R/Arboviroses/RS", RS, "_CE_Confirmados_Base.csv"),
header = TRUE,
sep = ",")
RS_CE_Notificados_SEDE_Base <- read.csv(file = paste0("Tabulacoes_R/Arboviroses/RS", RS, "_CE_Notificados_Sede_Base.csv"),
header = TRUE,
sep = ",")
RS_CE_Confirmados_SEDE_Base <- read.csv(file = paste0("Tabulacoes_R/Arboviroses/RS", RS, "_CE_Confirmados_Sede_BASE.csv"),
header = TRUE,
sep = ",")
###################################################################################################
### ###
### 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)) ###############################
###
CHIKON2023$SEM_PRI <-as.numeric(as.character(CHIKON2023$SEM_PRI)) ###############################
###
CHIKON2024$SEM_PRI <-as.numeric(as.character(CHIKON2024$SEM_PRI)) ###############################
###################################################################################################
###################################################################################################
#####################################################################################################################
######################################## 2023/24 ###################################################
#####################################################################################################################
#####################################################################################################################
######### A partir deste ponto do script, o código terá como função, além de criar a ################################
######### a base do período sazonal atual, servirá para, via ctrl -c / ctrl -v, ser ################################
######### incluído no script contido na Tabulação GERAL para servir de base histórica################################
######### do Programa Regional de Controle das Arboviroses da 22ª RS. Falta trabalhar################################
######### com a BASE DBF Chikungunya e Zika. ########################################################################
#####################################################################################################################
################ O código a ser copiado para a Tabulação Geral irá até o ponto determinado com FIM ##################
#####################################################################################################################
#### Tabela de notificações SINAN ###
################### Usando rbind para juntar o segundo semestre de um ano ########################################
################### ao primeiro semestro do ano seguinte. ########################################
################### Criando assim o 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)
assign(paste0("RS", RS, "_23_24_SINAN"),
SINAN_DENGUE_RS)
############### Fazendo o mesmo com as bases DBF de chikungunya ################################################
AUX01 <- CHIKON2023 %>%
filter(SEM_PRI >= 202331,
ID_REGIONA == ID_REG | ID_RG_RESI == ID_REG)
AUX02 <- CHIKON2024 %>%
filter(SEM_PRI <= 202430,
ID_REGIONA == ID_REG | ID_RG_RESI == ID_REG)
SINAN_CHIK_RS <- rbind(AUX01,
AUX02)
### Removendo tabela DENGON2023 e DENGON2024 já utilizada ###
#################################################################################################################
### Construindo um for loop para realizar a tabela de notificados por semana epidemiológica ###
#################################################################################################################
AUX <- matrix(data = NA,
nrow = nrow,
ncol = 54)
AUX <- as.data.frame(AUX)
colnames(AUX)[1] <- "Município"
AUX[,1] <- BASE_IBGE[which(BASE_IBGE$RS == RS), 2]
colnames (AUX)[2:24] <- c(31:53)
colnames (AUX)[25:54] <- c(1:30)
for (i in BASE_IBGE[(which(BASE_IBGE$RS == RS)), 2]){
AUX[which(AUX == i), 2] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202331)%>%
count()
)
AUX[which(AUX == i), 3] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202332) %>%
count()
)
AUX[which(AUX == i), 4] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202333) %>%
count()
)
AUX[which(AUX == i),5] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202334) %>%
count()
)
AUX[which(AUX == i), 6] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202335) %>%
count()
)
AUX[which(AUX == i), 7] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202336) %>%
count()
)
AUX[which(AUX == i), 8] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202337) %>%
count()
)
AUX[which(AUX == i), 9] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202338) %>%
count()
)
AUX[which(AUX == i), 10] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202339) %>%
count()
)
AUX[which(AUX == i), 11] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202340) %>%
count()
)
AUX[which(AUX == i), 12] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202341) %>%
count()
)
AUX[which(AUX == i), 13] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202342) %>%
count()
)
AUX[which(AUX == i), 14] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202343) %>%
count()
)
AUX[which(AUX == i), 15] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202344) %>%
count()
)
AUX[which(AUX == i), 16] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202345) %>%
count()
)
AUX[which(AUX == i), 17] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202346) %>%
count()
)
AUX[which(AUX == i), 18] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202347) %>%
count()
)
AUX[which(AUX == i), 19] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202348) %>%
count()
)
AUX[which(AUX == i), 20] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202349) %>%
count()
)
AUX[which(AUX == i), 21] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202350) %>%
count()
)
AUX[which(AUX == i), 22] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202351) %>%
count()
)
AUX[which(AUX == i), 23] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202352) %>%
count()
)
AUX[which(AUX == i), 24] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202353) %>%
count()
)
AUX[which(AUX == i), 25] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202401) %>%
count()
)
AUX[which(AUX == i), 26] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202402) %>%
count()
)
AUX[which(AUX == i), 27] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202403) %>%
count()
)
AUX[which(AUX == i), 28] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202404) %>%
count()
)
AUX[which(AUX == i), 29] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202405) %>%
count()
)
AUX[which(AUX == i), 30] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202406) %>%
count()
)
AUX[which(AUX == i), 31] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202407) %>%
count()
)
AUX[which(AUX == i), 32] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202408) %>%
count()
)
AUX[which(AUX == i), 33] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202409) %>%
count()
)
AUX[which(AUX == i), 34] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202410) %>%
count()
)
AUX[which(AUX == i), 35] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202411) %>%
count()
)
AUX[which(AUX == i), 36] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202412) %>%
count()
)
AUX[which(AUX == i), 37] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202413) %>%
count()
)
AUX[which(AUX == i), 38] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202414) %>%
count()
)
AUX[which(AUX == i), 39] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202415) %>%
count()
)
AUX[which(AUX == i), 40] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202416) %>%
count()
)
AUX[which(AUX == i), 41] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202417) %>%
count()
)
AUX[which(AUX == i), 42] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202418) %>%
count()
)
AUX[which(AUX == i), 43] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202419) %>%
count()
)
AUX[which(AUX == i), 44] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202420) %>%
count()
)
AUX[which(AUX == i), 45] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202421) %>%
count()
)
AUX[which(AUX == i), 46] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202422) %>%
count()
)
AUX[which(AUX == i), 47] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202423) %>%
count()
)
AUX[which(AUX == i), 48] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202424) %>%
count()
)
AUX[which(AUX == i), 49] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202425) %>%
count()
)
AUX[which(AUX == i), 50] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202426) %>%
count()
)
AUX[which(AUX == i), 51] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202427) %>%
count()
)
AUX[which(AUX == i), 52] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202428) %>%
count()
)
AUX[which(AUX == i), 53] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202429) %>%
count()
)
AUX[which(AUX == i), 54] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202430) %>%
count()
)
}
AUX[,1] <- BASE_IBGE[which(BASE_IBGE$RS == RS), 3]
AUX[(nrow(AUX)+ 1),2:54] <- apply(AUX[,2:54], 2, sum)
AUX[nrow(AUX), 1] <- "Total"
assign(paste0("RS", RS, "_23_24_SE_Notificados"), AUX)
assign("RS_23_24_SE_Notificados", AUX)
########################################################################################################
### Construindo um for loop para realizar a tabela de Confirmados por semana epidemiológica ###
########################################################################################################
AUX <- matrix(data = NA,
nrow = nrow,
ncol = 54)
AUX <- as.data.frame(AUX)
colnames(AUX)[1] <- "Município"
AUX[,1] <- BASE_IBGE[which(BASE_IBGE$RS == RS), 2]
colnames (AUX)[2:24] <- c(31:53)
colnames (AUX)[25:54] <- c(1:30)
for (i in BASE_IBGE[(which(BASE_IBGE$RS == RS)), 2]){
AUX[which(AUX == i), 2] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202331)%>%
count()
)
AUX[which(AUX == i), 3] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202332) %>%
count()
)
AUX[which(AUX == i), 4] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202333) %>%
count()
)
AUX[which(AUX == i),5] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202334) %>%
count()
)
AUX[which(AUX == i), 6] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202335) %>%
count()
)
AUX[which(AUX == i), 7] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202336) %>%
count()
)
AUX[which(AUX == i), 8] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202337) %>%
count()
)
AUX[which(AUX == i), 9] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202338) %>%
count()
)
AUX[which(AUX == i), 10] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202339) %>%
count()
)
AUX[which(AUX == i), 11] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202340) %>%
count()
)
AUX[which(AUX == i), 12] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202341) %>%
count()
)
AUX[which(AUX == i), 13] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202342) %>%
count()
)
AUX[which(AUX == i), 14] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202343) %>%
count()
)
AUX[which(AUX == i), 15] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202344) %>%
count()
)
AUX[which(AUX == i), 16] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202345) %>%
count()
)
AUX[which(AUX == i), 17] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202346) %>%
count()
)
AUX[which(AUX == i), 18] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202347) %>%
count()
)
AUX[which(AUX == i), 19] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202348) %>%
count()
)
AUX[which(AUX == i), 20] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202349) %>%
count()
)
AUX[which(AUX == i), 21] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202350) %>%
count()
)
AUX[which(AUX == i), 22] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202351) %>%
count()
)
AUX[which(AUX == i), 23] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202352) %>%
count()
)
AUX[which(AUX == i), 24] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202353) %>%
count()
)
AUX[which(AUX == i), 25] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202401) %>%
count()
)
AUX[which(AUX == i), 26] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202402) %>%
count()
)
AUX[which(AUX == i), 27] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202403) %>%
count()
)
AUX[which(AUX == i), 28] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202404) %>%
count()
)
AUX[which(AUX == i), 29] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202405) %>%
count()
)
AUX[which(AUX == i), 30] <- as.integer(SINAN_DENGUE_RS %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202406) %>%
count()
)
AUX[which(AUX == i), 31] <- as.integer(SINAN_DENGUE_RS %>%