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Informe_Epidemiológico.R
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Informe_Epidemiológico.R
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###############################################################################################################
###############################################################################################################
## ##
## 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/Tabulacao_Geral deve ser previamente rodado ##
## para que o script abaixo funcione. ##
## ############################################################################################ ##
## ##
###############################################################################################################
###############################################################################################################
##### Definindo diretório de trabalho, caso tenha que trabalhar em Windows, acertar o diretório ####
setwd("/home/gustavo/Área de Trabalho/Análise_de_Dados/Base_de_Dados/Tabulacoes_R/Arboviroses/")
###############################################################################################################
###############################################################################################################
## ##
## OS PASSOS ABAIXO (ATÉ LINHA 50) DEVEM SER SEGUIDOS PARA FUNCIONAMENTO!!! ##
## ################################################################################################ ##
## ##
## Lembrar que OBRIGATORIAMENTE deve ser baixados as bases DBF de 2009 até 2023 ##
## As bases DBF devem ser salvas no formato DENGON2009, DENGON2012... até DENGON2023 ##
## A base DENGON2023 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 21/07/2023"
###### Definir Fonte dos gráficos do LACEN #############
Fonte_1 <- "Fonte: Lacen. Acesso em 21/07/2023"
#### 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("29")
SE <- as.numeric(SE)
#############################################################################################################
#############################################################################################################
#############################################################################################################
#############################################################################################################
library(patchwork)
library(foreign)
library (dplyr)
library (googlesheets4)
library (ggplot2)
###Não sei usar o httpuv!!!###
library (httpuv)
library(stringr)
library(lubridate)
####Importando as tabelas da Tabulação Primária para construção das tabelas base do Informe Epidemiológico ###
BASE_IBGE<-read.table(file="/home/gustavo/Área de Trabalho/Análise_de_Dados/Base_de_Dados/Planilha_Base_IBGE.csv",
header=TRUE,
sep=",")
BASE_IBGE_BRASIL <- read.csv (file = "/home/gustavo/Área de Trabalho/Análise_de_Dados/Base_de_Dados/Planilha_Base_IBGE_BRASIL.csv",
header = TRUE,
sep = ",")
DENGON2022 <- read.dbf(file = "/home/gustavo/Área de Trabalho/Análise_de_Dados/Base_de_Dados/DBF/DENGON2022.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)
DENGON2023 <- read.dbf(file = "/home/gustavo/Área de Trabalho/Análise_de_Dados/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)
CHIKON2022 <- read.dbf("/home/gustavo/Área de Trabalho/Análise_de_Dados/Base_de_Dados/DBF/CHIKON2022.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("/home/gustavo/Área de Trabalho/Análise_de_Dados/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)
RS22_Serie_Historica_Base <- read.csv(file = "RS22_Serie_Historica_Base.csv",
header = TRUE,
sep = ",")
RS22_CE_Notificados_Base <- read.csv(file = "RS22_CE_Notificados_Base.csv",
header = TRUE,
sep = ",")
RS22_CE_Confirmados_Base <- read.csv(file = "RS22_CE_Confirmados_Base.csv",
header = TRUE,
sep = ",")
RS22_CE_Notificados_IVAIPORÃ_Base <- read.csv(file = "RS22_CE_Notificados_IVAIPORÃ_Base.csv",
header = TRUE,
sep = ",")
RS22_CE_Confirmados_IVAIPORÃ_Base <- read.csv(file = "RS22_CE_Confirmados_IVAIPORÃ_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
##
DENGON2022$SEM_PRI <-as.numeric(as.character(DENGON2022$SEM_PRI))
DENGON2023$SEM_PRI <-as.numeric(as.character(DENGON2023$SEM_PRI))
CHIKON2022$SEM_PRI <-as.numeric(as.character(CHIKON2022$SEM_PRI))
CHIKON2023$SEM_PRI <-as.numeric(as.character(CHIKON2023$SEM_PRI))
######################################################################################################################
#####################################################################################################################
######################################## 2022/23 ###################################################
#####################################################################################################################
#####################################################################################################################
######### 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###
RS22_22_23_AUX01 <- DENGON2022 %>%
filter(ID_REGIONA == 1376 | ID_RG_RESI == 1376,
SEM_PRI >= 202231)
RS22_22_23_AUX02 <- DENGON2023 %>%
filter(ID_REGIONA == 1376 | ID_RG_RESI == 1376,
SEM_PRI <=202330)
RS22_22_23_SINAN <- rbind(RS22_22_23_AUX01, RS22_22_23_AUX02)
rm(RS22_22_23_AUX01, RS22_22_23_AUX02)
RS22_22_23_AUX01 <- CHIKON2022 %>%
filter(SEM_PRI >= 202231)
RS22_22_23_AUX02 <- CHIKON2023 %>%
filter(SEM_PRI <=202330)
RS22_22_23_SINAN_CHIK <- rbind(RS22_22_23_AUX01, RS22_22_23_AUX02)
rm(RS22_22_23_AUX01, RS22_22_23_AUX02)
###Removendo tabela DENGON2022 e DENGON2023 já utilizada###
rm (DENGON2022, DENGON2023, CHIKON2022, CHIKON2023)
###Construindo um for loop para realizar a tabela de notificados por semana epidemiológica###
RS22_22_23_SE_Notificados <- matrix(data = NA,
nrow = 16,
ncol = 54)
RS22_22_23_SE_Notificados <- as.data.frame(RS22_22_23_SE_Notificados)
colnames(RS22_22_23_SE_Notificados)[1] <- "Município"
RS22_22_23_SE_Notificados[,1] <- BASE_IBGE[which(BASE_IBGE$RS == 22), 2]
colnames (RS22_22_23_SE_Notificados)[2:24] <- c(31:53)
colnames (RS22_22_23_SE_Notificados)[25:54] <- c(1:30)
for (i in BASE_IBGE[(which(BASE_IBGE$RS == 22)), 2]){
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 2] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202231)%>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 3] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202232) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 4] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202233) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i),5] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202234) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 6] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202235) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 7] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202236) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 8] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202237) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 9] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202238) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 10] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202239) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 11] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202240) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 12] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202241) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 13] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202242) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 14] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202243) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 15] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202244) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 16] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202245) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 17] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202246) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 18] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202247) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 19] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202248) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 20] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202249) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 21] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202250) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 22] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202251) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 23] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202252) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 24] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202253) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 25] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202301) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 26] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202302) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 27] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202303) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 28] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202304) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 29] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202305) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 30] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202306) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 31] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202307) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 32] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202308) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 33] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202309) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 34] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202310) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 35] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202311) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 36] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202312) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 37] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202313) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 38] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202314) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 39] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202315) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 40] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202316) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 41] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202317) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 42] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202318) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 43] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202319) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 44] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202320) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 45] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202321) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 46] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202322) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 47] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202323) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 48] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202324) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 49] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202325) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 50] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202326) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 51] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202327) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 52] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202328) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 53] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202329) %>%
count()
)
RS22_22_23_SE_Notificados[which(RS22_22_23_SE_Notificados == i), 54] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
SEM_PRI ==202330) %>%
count()
)
}
RS22_22_23_SE_Notificados[,1] <- BASE_IBGE[which(BASE_IBGE$RS == 22), 3]
RS22_22_23_SE_Notificados[17,2:54] <- apply(RS22_22_23_SE_Notificados[,2:54], 2, sum)
RS22_22_23_SE_Notificados[17,1] <- "Total"
###Construindo um for loop para realizar a tabela de Confirmados por semana epidemiológica###
RS22_22_23_SE_Confirmados <- matrix(data = NA,
nrow = 16,
ncol = 54)
RS22_22_23_SE_Confirmados <- as.data.frame(RS22_22_23_SE_Confirmados)
colnames(RS22_22_23_SE_Confirmados)[1] <- "Município"
RS22_22_23_SE_Confirmados[,1] <- BASE_IBGE[which(BASE_IBGE$RS == 22), 2]
colnames (RS22_22_23_SE_Confirmados)[2:24] <- c(31:53)
colnames (RS22_22_23_SE_Confirmados)[25:54] <- c(1:30)
for (i in BASE_IBGE[(which(BASE_IBGE$RS == 22)), 2]){
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 2] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202231)%>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 3] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202232) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 4] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202233) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i),5] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202234) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 6] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202235) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 7] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202236) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 8] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202237) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 9] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202238) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 10] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202239) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 11] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202240) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 12] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202241) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 13] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202242) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 14] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202243) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 15] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202244) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 16] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202245) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 17] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202246) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 18] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202247) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 19] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202248) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 20] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202249) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 21] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202250) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 22] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202251) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 23] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202252) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 24] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202253) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 25] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202301) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 26] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202302) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 27] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202303) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 28] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202304) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 29] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202305) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 30] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202306) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 31] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202307) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 32] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202308) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 33] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202309) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 34] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202310) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 35] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202311) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 36] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202312) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 37] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202313) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 38] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202314) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 39] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202315) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 40] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202316) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 41] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202317) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 42] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|
CLASSI_FIN == 12,
SEM_PRI ==202318) %>%
count()
)
RS22_22_23_SE_Confirmados[which(RS22_22_23_SE_Confirmados == i), 43] <- as.integer(RS22_22_23_SINAN %>%
filter(ID_MN_RESI == i,
CLASSI_FIN == 10
|
CLASSI_FIN == 11
|