R/process_sih.R
In rfsaldanha/microdatasus: Download and preprocess DataSUS files
Defines functions
process_sih
Documented in
process_sih
#' Process SIH variables from DataSUS
#'
#' \code{process_SIH} processes SIH variables retrieved by \code{fetch_datasus()}.
#'
#' This function processes SIH variables retrieved by \code{fetch_datasus()}, informing labels for categoric variables including NA values.
#'
#' Currently, only "SIH-RD" is supported.
#'
#' @param data \code{data.frame} created by \code{fetch_datasus()}.
#' @param information_system string. The abbreviation of the health information system. See \emph{Details}.
#' @param municipality_data optional logical. \code{TRUE} by default, creates new variables in the dataset informing the full name and other details about the municipality of residence.
#'
#' @examples \dontrun{
#' df <- fetch_datasus(year_start = 2010, month_start = 1,
#' year_end = 2010, month_end = 1,
#' uf = "RJ",
#' information_system = "SIH-RD")
#' df_a <- process_sih(df)
#' df_b <- process_sih(df, municipality_data = FALSE)
#' }
#' @export
process_sih <- function(data, information_system = "SIH-RD", municipality_data = TRUE) {
# Check information system
available_information_system <- "SIH-RD"
if(!(information_system %in% available_information_system)) stop("Health informaton system unknown.")
# Variables names
variables_names <- names(data)
# Use dtplyr
data <- dtplyr::lazy_dt(data)
if(information_system == "SIH-RD"){
# ANO_CMPT
if("ANO_CMPT" %in% variables_names){
data <- data %>%
dplyr::mutate(ANO_CMPT = as.numeric(.data$ANO_CMPT))
}
# MES_CMPT
if("MES_CMPT" %in% variables_names){
data <- data %>%
dplyr::mutate(MES_CMPT = as.numeric(.data$MES_CMPT))
}
# ESPEC
if("ESPEC" %in% variables_names){
data <- data %>%
dplyr::mutate(ESPEC = as.character(.data$ESPEC)) %>%
dplyr::mutate(PA_REGCT = dplyr::case_match(
.data$ESPEC,
"1" ~ "Cir\u00fargico",
"2" ~ "Obst\u00e9tricos",
"3" ~ "Cl\u00ednicos",
"4" ~ "Cr\u00f4nicos",
"5" ~ "Psiquiatria",
"6" ~ "Pneumologia sanit\u00e1ria (tsiologia)",
"7" ~ "Pedi\u00e1tricos",
"8" ~ "Reabilita\u00e7\u00e3o",
"9" ~ "Leito Dia / Cir\u00fargicos",
"10" ~ "Leito Dia / Aids",
"11" ~ "Leito Dia / Fibrose C\u00edstica",
"12" ~ "Leito Dia / Intercorr\u00eancia P\u00f3s-Transplante",
"13" ~ "Leito Dia / Geriatria",
"14" ~ "Leito Dia / Sa\u00fade Mental",
"51" ~ "UTI II Adulto COVID 19",
"52" ~ "UTI II Pedi\u00e1trica COVID 19",
"64" ~ "Unidade Intermedi\u00e1ria",
"65" ~ "Unidade Intermedi\u00e1ria Neonatal",
"74" ~ "UTI I",
"75" ~ "UTI Adulto II",
"76" ~ "UTI Adulto III",
"77" ~ "UTI Infantil I",
"78" ~ "UTI Infantil II",
"79" ~ "UTI Infantil III",
"80" ~ "UTI Neonatal I",
"81" ~ "UTI Neonatal II",
"82" ~ "UTI Neonatal III",
"83" ~ "UTI Queimados",
"84" ~ "Acolhimento Noturno",
"85" ~ "UTI Coronariana-UCO tipo II",
"86" ~ "UTI Coronariana-UCO tipo III",
"87" ~ "Sa\u00fade Mental (Cl\u00ednico)",
"88" ~ "Queimado Adulto (Cl\u00ednico)",
"89" ~ "Queimado Pedi\u00e1trico (Cl\u00ednico)",
"90" ~ "Queimado Adulto (Cir\u00fargico)",
"91" ~ "Queimado Pedi\u00e1trico (Cir\u00fargico)",
"92" ~ "UCI Unidade de Cuidados Intermediarios Neonatal Convencional",
"93" ~ "UCI Unidade de Cuidados Intermediarios Neonatal Canguru",
"94" ~ "UCI Unidade de Cuidados Intermediarios Pediatrico",
"95" ~ "UCI Unidade de Cuidados Intermediarios Adulto",
"96" ~ "Suporte Ventilat\u00f3rio Pulmonar COVID-19",
.default = .data$ESPEC
)) %>%
dplyr::mutate(ESPEC = as.factor(.data$ESPEC))
}
# IDENT
if("IDENT" %in% variables_names){
data <- data %>%
dplyr::mutate(IDENT = as.character(.data$IDENT)) %>%
dplyr::mutate(IDENT = dplyr::case_match(
.data$IDENT,
"1" ~ "Principal",
"3" ~ "Continua\u00e7\u00e3o",
"5" ~ "Longa perman\u00eancia",
.default = .data$IDENT
)) %>%
dplyr::mutate(IDENT = as.factor(.data$IDENT))
}
# MUNIC_RES
if("MUNIC_RES" %in% variables_names & municipality_data == TRUE){
colnames(tabMun)[1] <- "MUNIC_RES"
data <- data %>%
dplyr::mutate(MUNIC_RES = as.numeric(.data$MUNIC_RES)) %>%
dplyr::left_join(tabMun, by = "MUNIC_RES")
}
# NASC
if("NASC" %in% variables_names){
data <- data %>%
dplyr::mutate(NASC = as.Date(.data$NASC, format = "%Y%m%d"))
}
# SEXO
if("SEXO" %in% variables_names){
data <- data %>%
dplyr::mutate(SEXO = as.character(.data$SEXO)) %>%
dplyr::mutate(SEXO = dplyr::case_match(
.data$SEXO,
"1" ~ "Masculino",
"2" ~ "Feminino",
"3" ~ "Feminino",
"0" ~ NA,
"9" ~ NA,
.default = .data$SEXO
)) %>%
dplyr::mutate(SEXO = as.factor(.data$SEXO))
}
# UTI_MES_IN
if("UTI_MES_IN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_IN = as.numeric(.data$UTI_MES_IN))
}
# UTI_MES_AN
if("UTI_MES_AN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_AN = as.numeric(.data$UTI_MES_AN))
}
# UTI_MES_AL
if("UTI_MES_AL" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_AL = as.numeric(.data$UTI_MES_AL))
}
# UTI_MES_TO
if("UTI_MES_TO" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_TO = as.numeric(.data$UTI_MES_TO))
}
# MARCA_UTI
if("MARCA_UTI" %in% variables_names){
data <- data %>%
dplyr::mutate(MARCA_UTI = as.character(.data$MARCA_UTI)) %>%
dplyr::mutate(MARCA_UTI = dplyr::case_match(
.data$MARCA_UTI,
"0" ~ "N\u00e3o utilizou UTI",
"51" ~ "UTI adulto - tipo II COVID 19",
"52" ~ "UTI pedi\u00e1trica - tipo II COVID 19",
"74" ~ "UTI adulto - tipo I",
"75" ~ "UTI adulto - tipo II",
"76" ~ "UTI adulto - tipo III",
"77" ~ "UTI infantil - tipo I",
"78" ~ "UTI infantil - tipo II",
"79" ~ "UTI infantil - tipo III",
"80" ~ "UTI neonatal - tipo I",
"81" ~ "UTI neonatal - tipo II",
"82" ~ "UTI neonatal - tipo III",
"83" ~ "UTI de queimados",
"85" ~ "UTI coronariana tipo II - UCO tipo II",
"86" ~ "UTI coronariana tipo III - UCO tipo III",
"99" ~ "UTI Doador",
"1" ~ "Utilizou mais de um tipo de UTI",
.default = .data$MARCA_UTI
)) %>%
dplyr::mutate(MARCA_UTI = as.factor(.data$MARCA_UTI))
}
# UTI_INT_IN
if("UTI_INT_IN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_IN = as.numeric(.data$UTI_INT_IN))
}
# UTI_INT_AN
if("UTI_INT_AN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_AN = as.numeric(.data$UTI_INT_AN))
}
# UTI_INT_AL
if("UTI_INT_AL" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_AL = as.numeric(.data$UTI_INT_AL))
}
# UTI_INT_TO
if("UTI_INT_TO" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_TO = as.numeric(.data$UTI_INT_TO))
}
# DIAR_ACOM
if("DIAR_ACOM" %in% variables_names){
data <- data %>%
dplyr::mutate(DIAR_ACOM = as.numeric(.data$DIAR_ACOM))
}
# QT_DIARIAS
if("QT_DIARIAS" %in% variables_names){
data <- data %>%
dplyr::mutate(QT_DIARIAS = as.numeric(.data$QT_DIARIAS))
}
# VAL_SH
if("VAL_SH" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SH = as.numeric(.data$VAL_SH))
}
# VAL_SP
if("VAL_SP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SP = as.numeric(.data$VAL_SP))
}
# VAL_SADT
if("VAL_SADT" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SADT = as.numeric(.data$VAL_SADT))
}
# VAL_RN
if("VAL_RN" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_RN = as.numeric(.data$VAL_RN))
}
# VAL_ACOMP
if("VAL_ACOMP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_ACOMP = as.numeric(.data$VAL_ACOMP))
}
# VAL_ORTP
if("VAL_ORTP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_ORTP = as.numeric(.data$VAL_ORTP))
}
# VAL_SANGUE
if("VAL_SANGUE" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SANGUE = as.numeric(.data$VAL_SANGUE))
}
# VAL_SADTSR
if("VAL_SADTSR" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SADTSR = as.numeric(.data$VAL_SADTSR))
}
# VAL_TRANSP
if("VAL_TRANSP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_TRANSP = as.numeric(.data$VAL_TRANSP))
}
# VAL_OBSANG
if("VAL_OBSANG" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_OBSANG = as.numeric(.data$VAL_OBSANG))
}
# VAL_PED1AC
if("VAL_PED1AC" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_PED1AC = as.numeric(.data$VAL_PED1AC))
}
# VAL_TOT
if("VAL_TOT" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_TOT = as.numeric(.data$VAL_TOT))
}
# VAL_UTI
if("VAL_UTI" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_UTI = as.numeric(.data$VAL_UTI))
}
# US_TOT
if("US_TOT" %in% variables_names){
data <- data %>%
dplyr::mutate(US_TOT = as.numeric(.data$US_TOT))
}
# DT_INTER
if("DT_INTER" %in% variables_names){
data <- data %>%
dplyr::mutate(DT_INTER = as.Date(.data$DT_INTER, format = "%Y%m%d"))
}
# DT_SAIDA
if("DT_SAIDA" %in% variables_names){
data <- data %>%
dplyr::mutate(DT_SAIDA = as.Date(.data$DT_SAIDA, format = "%Y%m%d"))
}
# COBRANCA (motivo de saÃda/permanência, portaria SAS 719)
if("COBRANCA" %in% variables_names){
data <- data %>%
dplyr::mutate(COBRANCA = as.character(.data$COBRANCA)) %>%
dplyr::mutate(COBRANCA = dplyr::case_match(
.data$COBRANCA,
"11" ~ "Alta curado",
"12" ~ "Alta melhorado",
"14" ~ "Alta a pedido",
"15" ~ "Alta com previs\u00e3o de retorno p/acomp do paciente",
"16" ~ "Alta por evas\u00e3o",
"18" ~ "Alta por outros motivos",
"19" ~ "Alta de paciente agudo em psiquiatria",
"21" ~ "Perman\u00eancia por caracter\u00edsticas pr\u00f3prias da doen\u00e7a",
"22" ~ "Perman\u00eancia por intercorr\u00eancia",
"23" ~ "Perman\u00eancia por impossibilidade s\u00f3cio-familiar",
"24" ~ "Perman\u00eancia proc doa\u00e7\u00e3o \u00f3rg, tec, c\u00e9l-doador vivo",
"25" ~ "Perman\u00eancia proc doa\u00e7\u00e3o \u00f3rg, tec, c\u00e9l-doador morto",
"26" ~ "Perman\u00eancia por mudan\u00e7a de procedimento",
"27" ~ "Perman\u00eancia por reopera\u00e7\u00e3o",
"28" ~ "Perman\u00eancia por outros motivos",
"29" ~ "Transfer\u00eancia para interna\u00e7\u00e3o domiciliar",
"32" ~ "Transfer\u00eancia para interna\u00e7\u00e3o domiciliar",
"31" ~ "Transfer\u00eancia para outro estabelecimento",
"41" ~ "\u00d3bito com DO fornecida pelo m\u00e9dico assistente",
"42" ~ "\u00d3bito com DO fornecida pelo IML",
"43" ~ "\u00d3bito com DO fornecida pelo SVO",
"51" ~ "Encerramento administrativo",
"61" ~ "Alta da m\u00e3e/pu\u00e9rpera e do rec\u00e9m-nascido",
"17" ~ "Alta da m\u00e3e/pu\u00e9rpera e do rec\u00e9m-nascido",
"62" ~ "Alta da m\u00e3e/pu\u00e9rpera e perman\u00eancia rec\u00e9m-nascido",
"13" ~ "Alta da m\u00e3e/pu\u00e9rpera e perman\u00eancia rec\u00e9m-nascido",
"63" ~ "Alta da m\u00e3e/pu\u00e9rpera e \u00f3bito do rec\u00e9m-nascido",
"64" ~ "Alta da m\u00e3e/pu\u00e9rpera com \u00f3bito fetal",
"65" ~ "\u00d3bito da gestante e do concepto",
"66" ~ "\u00d3bito da m\u00e3e/pu\u00e9rpera e alta do rec\u00e9m-nascido",
"67" ~ "\u00d3bito da m\u00e3e/pu\u00e9rpera e perman\u00eancia rec\u00e9m-nascido",
.default = .data$COBRANCA
)) %>%
dplyr::mutate(COBRANCA = as.factor(.data$COBRANCA))
}
# NATUREZA
if("NATUREZA" %in% variables_names){
data <- data %>%
dplyr::mutate(NATUREZA = as.character(.data$NATUREZA)) %>%
dplyr::mutate(NATUREZA = dplyr::case_match(
.data$NATUREZA,
"0" ~ NA,
"99" ~ NA,
"10" ~ "Pr\u00f3prio",
"20" ~ "Contratado",
"22" ~ "Contratado optante SIMPLES",
"30" ~ "Federal",
"31" ~ "Federal Verba Pr\u00f3pria",
"40" ~ "Estadual",
"41" ~ "Estadual Verba Pr\u00f3pria",
"50" ~ "Municipal",
"60" ~ "Filantr\u00f3pico",
"61" ~ "Filantr\u00f3pico isento tributos e contr.sociais",
"63" ~ "Filantr\u00f3pico isento IR e contr.s/lucro l\u00edquido",
"70" ~ "Universit\u00e1rio Ensino",
"80" ~ "Sindicato",
"90" ~ "Universit\u00e1rio Pesquisas",
"91" ~ "Univ. Pesquisas isento tributos e contr.sociais",
"93" ~ "Univ. Pesquisas isento IR e contr.s/lucro l\u00edquido",
"94" ~ "Universit\u00e1rio de ensino e pesquisa privado",
"92" ~ "Universit\u00e1rio de ensino e pesquisa privado",
.default = .data$NATUREZA
)) %>%
dplyr::mutate(NATUREZA = as.factor(.data$NATUREZA))
}
# NAT_JUR
if("NAT_JUR" %in% variables_names){
data <- data %>%
dplyr::mutate(NAT_JUR = as.character(.data$NAT_JUR)) %>%
dplyr::mutate(NAT_JUR = dplyr::case_match(
.data$NAT_JUR,
"1015" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Executivo Federal",
"1023" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Exec Estadual ou Distr Fed",
"1031" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Executivo Municipal",
"1040" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Legislativo Federal",
"1058" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Legisl Estadual ou Dist Fed",
"1066" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Legislativo Municipal",
"1074" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Judici\u00e1rio Federal",
"1082" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Judici\u00e1rio Estadual",
"1104" ~ "Autarquia Federal",
"1112" ~ "Autarquia Estadual ou do Distrito Federal",
"1120" ~ "Autarquia Municipal",
"1139" ~ "Funda\u00e7\u00e3o Federal",
"1147" ~ "Funda\u00e7\u00e3o Estadual ou do Distrito Federal",
"1155" ~ "Funda\u00e7\u00e3o Municipal",
"1163" ~ "\u00d3rg\u00e3o P\u00fablico Aut\u00f4nomo Federal",
"1171" ~ "\u00d3rg\u00e3o P\u00fablico Aut\u00f4nomo Estadual ou Distr Federal",
"1180" ~ "\u00d3rg\u00e3o P\u00fablico Aut\u00f4nomo Estadual ou Distr Federal",
"1198" ~ "Comiss\u00e3o Polinacional",
"1201" ~ "Fundo P\u00fablico",
"1210" ~ "Associa\u00e7\u00e3o P\u00fablica",
"2011" ~ "Empresa P\u00fablica",
"2038" ~ "Sociedade de Economia Mista",
"2046" ~ "Sociedade An\u00f4nima Aberta",
"2054" ~ "Sociedade An\u00f4nima Fechada",
"2062" ~ "Sociedade Empres\u00e1ria Limitada",
"2070" ~ "Sociedade Empres\u00e1ria em Nome Coletivo",
"2089" ~ "Sociedade Empres\u00e1ria em Comandita Simples",
"2097" ~ "Sociedade Empres\u00e1ria em Comandita por A\u00e7\u00f5es",
"2127" ~ "Sociedade em Conta de Participa\u00e7\u00e3o",
"2135" ~ "Empres\u00e1rio (Individual)",
"2143" ~ "Cooperativa",
"2151" ~ "Cons\u00f3rcio de Sociedades",
"2160" ~ "Grupo de Sociedades",
"2178" ~ "Estabelecimento no Brasil de Sociedade Estrangeira",
"2194" ~ "Estab no Brasil Empr Binacional Argentina-Brasil",
"2216" ~ "Empresa Domiciliada no Exterior",
"2224" ~ "Clube/Fundo de Investimento",
"2232" ~ "Sociedade Simples Pura",
"2240" ~ "Sociedade Simples Limitada",
"2259" ~ "Sociedade Simples em Nome Coletivo",
"2267" ~ "Sociedade Simples em Comandita Simples",
"2275" ~ "Empresa Binacional",
"2283" ~ "Cons\u00f3rcio de Empregadores",
"2291" ~ "Cons\u00f3rcio Simples",
"2305" ~ "Empr Individ Responsab Limitada (Natur Empres\u00e1ria)",
"2313" ~ "Empr Individ Responsab Limitada (Natureza Simples)",
"3034" ~ "Servi\u00e7o Notarial e Registral (Cart\u00f3rio)",
"3069" ~ "Funda\u00e7\u00e3o Privada",
"3077" ~ "Servi\u00e7o Social Aut\u00f4nomo",
"3085" ~ "Condom\u00ednio Edil\u00edcio",
"3107" ~ "Comiss\u00e3o de Concilia\u00e7\u00e3o Pr\u00e9via",
"3115" ~ "Entidade de Media\u00e7\u00e3o e Arbitragem",
"3123" ~ "Partido Pol\u00edtico",
"3131" ~ "Entidade Sindical",
"3204" ~ "Estab no Brasil de Funda\u00e7\u00e3o ou Associa\u00e7\u00e3o Estrang",
"3212" ~ "Funda\u00e7\u00e3o ou Associa\u00e7\u00e3o Domiciliada no Exterior",
"3220" ~ "Organiza\u00e7\u00e3o Religiosa",
"3239" ~ "Comunidade Ind\u00edgena",
"3247" ~ "Fundo Privado",
"3999" ~ "Associa\u00e7\u00e3o Privada",
"4014" ~ "Empresa Individual Imobili\u00e1ria",
"4022" ~ "Segurado Especial",
"4081" ~ "Contribuinte Individual",
"4090" ~ "Candidato a Cargo Pol\u00edtico Eletivo",
"4111" ~ "Leiloeiro",
"5010" ~ "Organiza\u00e7\u00e3o Internacional",
"5029" ~ "Representa\u00e7\u00e3o Diplom\u00e1tica Estrangeira",
"5037" ~ "Outras Institui\u00e7\u00f5es Extraterritoriais",
"0" ~ NA,
.default = .data$NAT_JUR
)) %>%
dplyr::mutate(NAT_JUR = as.factor(.data$NAT_JUR))
}
# GESTAO
if("GESTAO" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTAO = as.character(.data$GESTAO)) %>%
dplyr::mutate(GESTAO = dplyr::case_match(
.data$GESTAO,
"0" ~ "Estadual",
"2" ~ "Estadual plena",
"1" ~ "Municipal plena assist",
"3" ~ NA,
"9" ~ NA,
.default = .data$GESTAO
)) %>%
dplyr::mutate(GESTAO = as.factor(.data$GESTAO))
}
# RUBRICA
if("RUBRICA" %in% variables_names){
data <- data %>%
dplyr::mutate(RUBRICA = as.numeric(.data$RUBRICA))
}
# IND_VDRL
if("IND_VDRL" %in% variables_names){
data <- data %>%
dplyr::mutate(IND_VDRL = as.character(.data$IND_VDRL)) %>%
dplyr::mutate(IND_VDRL = dplyr::case_match(
.data$IND_VDRL,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$IND_VDRL
)) %>%
dplyr::mutate(IND_VDRL = as.factor(.data$IND_VDRL))
}
# MUNIC_MOV
if("MUNIC_MOV" %in% variables_names){
data$MUNIC_MOV <- as.numeric(data$MUNIC_MOV)
}
# COD_IDADE
if("COD_IDADE" %in% variables_names){
data <- data %>%
dplyr::mutate(COD_IDADE = as.character(.data$COD_IDADE)) %>%
dplyr::mutate(COD_IDADE = dplyr::case_match(
.data$COD_IDADE,
"0" ~ NA,
"2" ~ "Dias",
"3" ~ "Meses",
"4" ~ "Anos",
"5" ~ "Centena de anos (100 + idade)",
.default = .data$COD_IDADE
)) %>%
dplyr::mutate(COD_IDADE = as.factor(.data$COD_IDADE))
}
# IDADE
if("IDADE" %in% variables_names){
data <- data %>%
dplyr::mutate(IDADE = as.numeric(.data$IDADE))
}
# DIAS_PERM
if("DIAS_PERM" %in% variables_names){
data <- data %>%
dplyr::mutate(DIAS_PERM = as.numeric(.data$DIAS_PERM))
}
# MORTE
if("MORTE" %in% variables_names){
data <- data %>%
dplyr::mutate(MORTE = as.character(.data$MORTE)) %>%
dplyr::mutate(MORTE = dplyr::case_match(
.data$MORTE,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$MORTE
)) %>%
dplyr::mutate(MORTE = as.factor(.data$MORTE))
}
# NACIONAL
if("NACIONAL" %in% variables_names){
data <- data %>%
dplyr::mutate(NACIONAL = as.character(.data$NACIONAL)) %>%
dplyr::mutate(NACIONAL = dplyr::case_match(
.data$NACIONAL,
"170" ~ "Abissinia",
"171" ~ "Acores",
"172" ~ "Afar frances",
"241" ~ "Afeganistao",
"93" ~ "Albania",
"30" ~ "Alemanha",
"174" ~ "Alto volta",
"94" ~ "Andorra",
"175" ~ "Angola",
"334" ~ "Antartica francesa",
"337" ~ "Antartico argentino",
"333" ~ "Antartico britanico, territorio",
"336" ~ "Antartico chileno",
"338" ~ "Antartico noruegues",
"28" ~ "Antigua e. dep. barbuda",
"29" ~ "Antilhas holandesas",
"339" ~ "Apatrida",
"242" ~ "Arabia saudita",
"176" ~ "Argelia",
"21" ~ "Argentina",
"347" ~ "Armenia",
"289" ~ "Arquipelago de bismark",
"175" ~ "Angola",
"285" ~ "Arquipelago manahiki",
"286" ~ "Arquipelago midway",
"33" ~ "Aruba",
"175" ~ "Angola",
"198" ~ "Ascensao e tristao da cunha,is",
"287" ~ "Ashmore e cartier",
"288" ~ "Australia",
"95" ~ "Austria",
"138" ~ "Azerbaijao",
"243" ~ "Bahrein",
"342" ~ "Bangladesh",
"44" ~ "Barbados",
"139" ~ "Bashkista",
"177" ~ "Bechuanalandia",
"31" ~ "Belgica",
"46" ~ "Belize",
"178" ~ "Benin",
"83" ~ "Bermudas",
"246" ~ "Bhutan",
"244" ~ "Birmania",
"22" ~ "Bolivia",
"134" ~ "Bosnia herzegovina",
"179" ~ "Botsuana",
"10" ~ "Brasil",
"245" ~ "Brunei",
"96" ~ "Bulgaria",
"238" ~ "Burkina fasso",
"180" ~ "Burundi",
"141" ~ "Buryat",
"343" ~ "Cabo verde",
"181" ~ "Camaroes",
"34" ~ "Canada",
"142" ~ "Carelia",
"247" ~ "Catar",
"143" ~ "Cazaquistao",
"248" ~ "Ceilao",
"182" ~ "Ceuta e melilla",
"183" ~ "Chade",
"144" ~ "Chechen ingusth",
"23" ~ "Chile",
"42" ~ "China",
"249" ~ "China (taiwan)",
"97" ~ "Chipre",
"145" ~ "Chuvash",
"275" ~ "Cingapura",
"26" ~ "Colombia",
"40" ~ "Comunidade das bahamas",
"54" ~ "Comunidade dominicana",
"185" ~ "Congo",
"43" ~ "Coreia",
"186" ~ "Costa do marfim",
"51" ~ "Costa rica",
"250" ~ "Coveite",
"130" ~ "Croacia",
"52" ~ "Cuba",
"53" ~ "Curacao",
"146" ~ "Dagesta",
"187" ~ "Daome",
"340" ~ "Dependencia de ross",
"98" ~ "Dinamarca",
"188" ~ "Djibuti",
"99" ~ "Eire",
"251" ~ "Emirados arabes unidos",
"27" ~ "Equador",
"100" ~ "Escocia",
"136" ~ "Eslovaquia",
"132" ~ "Eslovenia",
"35" ~ "Espanha",
"129" ~ "Estado da cidade do vaticano",
"57" ~ "Estados assoc. das antilhas",
"36" ~ "Estados unidos da america (eua)",
"147" ~ "Estonia",
"190" ~ "Etiopia",
"252" ~ "Filipinas",
"102" ~ "Finlandia",
"37" ~ "Franca",
"192" ~ "Gambia",
"193" ~ "Gana",
"194" ~ "Gaza",
"148" ~ "Georgia",
"103" ~ "Gibraltar",
"149" ~ "Gorno altai",
"32" ~ "Gra-bretanha",
"59" ~ "Granada",
"104" ~ "Grecia",
"84" ~ "Groenlandia",
"292" ~ "Guam",
"61" ~ "Guatemala",
"87" ~ "Guiana francesa",
"195" ~ "Guine",
"344" ~ "Guine bissau",
"196" ~ "Guine equatorial",
"105" ~ "Holanda",
"64" ~ "Honduras",
"63" ~ "Honduras britanicas",
"253" ~ "Hong-kong",
"106" ~ "Hungria",
"254" ~ "Iemen",
"345" ~ "Iemen do sul",
"197" ~ "Ifni",
"300" ~ "Ilha johnston e sand",
"69" ~ "Ilha milhos",
"293" ~ "Ilhas baker",
"107" ~ "Ilhas baleares",
"199" ~ "Ilhas canarias",
"294" ~ "Ilhas cantao e enderburg",
"295" ~ "Ilhas carolinas",
"297" ~ "Ilhas christmas",
"184" ~ "Ilhas comores",
"290" ~ "Ilhas cook",
"108" ~ "Ilhas cosmoledo (lomores)",
"117" ~ "Ilhas de man",
"109" ~ "Ilhas do canal",
"296" ~ "Ilhas do pacifico",
"58" ~ "Ilhas falklands",
"101" ~ "Ilhas faroes",
"298" ~ "Ilhas gilbert",
"60" ~ "Ilhas guadalupe",
"299" ~ "Ilhas howland e jarvis",
"301" ~ "Ilhas kingman reef",
"305" ~ "Ilhas macdonal e heard",
"302" ~ "Ilhas macquaire",
"67" ~ "Ilhas malvinas",
"303" ~ "Ilhas marianas",
"304" ~ "Ilhas marshall",
"306" ~ "Ilhas niue",
"307" ~ "Ilhas norfolk",
"315" ~ "Ilhas nova caledonia",
"318" ~ "Ilhas novas hebridas",
"308" ~ "Ilhas palau",
"320" ~ "Ilhas pascoa",
"321" ~ "Ilhas pitcairin",
"309" ~ "Ilhas salomao",
"326" ~ "Ilhas santa cruz",
"65" ~ "Ilhas serranas",
"310" ~ "Ilhas tokelau",
"80" ~ "Ilhas turca",
"47" ~ "Ilhas turks e caicos",
"82" ~ "Ilhas virgens americanas",
"81" ~ "Ilhas virgens britanicas",
"311" ~ "Ilhas wake",
"332" ~ "Ilhas wallis e futuna",
"255" ~ "India",
"256" ~ "Indonesia",
"110" ~ "Inglaterra",
"257" ~ "Ira",
"258" ~ "Iraque",
"112" ~ "Irlanda",
"111" ~ "Irlanda do norte",
"113" ~ "Islandia",
"259" ~ "Israel",
"39" ~ "Italia",
"114" ~ "Iugoslavia",
"66" ~ "Jamaica",
"41" ~ "Japao",
"260" ~ "Jordania",
"150" ~ "Kabardino balkar",
"312" ~ "Kalimatan",
"151" ~ "Kalmir",
"346" ~ "Kara kalpak",
"152" ~ "Karachaevocherkess",
"153" ~ "Khakass",
"261" ~ "Kmer/camboja",
"154" ~ "Komi",
"262" ~ "Kuwait",
"263" ~ "Laos",
"200" ~ "Lesoto",
"155" ~ "Letonia",
"264" ~ "Libano",
"201" ~ "Liberia",
"202" ~ "Libia",
"115" ~ "Liechtenstein",
"156" ~ "Lituania",
"116" ~ "Luxemburgo",
"265" ~ "Macau",
"205" ~ "Madagascar",
"203" ~ "Madeira",
"266" ~ "Malasia",
"204" ~ "Malawi",
"267" ~ "Maldivas,is",
"206" ~ "Mali",
"157" ~ "Mari",
"207" ~ "Marrocos",
"68" ~ "Martinica",
"268" ~ "Mascate",
"208" ~ "Mauricio",
"209" ~ "Mauritania",
"85" ~ "Mexico",
"284" ~ "Mianma",
"210" ~ "Mocambique",
"158" ~ "Moldavia",
"118" ~ "Monaco",
"269" ~ "Mongolia",
"70" ~ "Monte serrat",
"137" ~ "Montenegro",
"240" ~ "Namibia",
"314" ~ "Nauru",
"270" ~ "Nepal",
"211" ~ "Nguane",
"71" ~ "Nicaragua",
"213" ~ "Nigeria",
"119" ~ "Noruega",
"316" ~ "Nova guine",
"317" ~ "Nova zelandia",
"271" ~ "Oman",
"159" ~ "Ossetia setentrional",
"121" ~ "Pais de gales",
"122" ~ "Paises baixos",
"272" ~ "Palestina",
"72" ~ "Panama",
"73" ~ "Panama(zona do canal)",
"214" ~ "Papua nova guine",
"273" ~ "Paquistao",
"24" ~ "Paraguai",
"89" ~ "Peru",
"322" ~ "Polinesia francesa",
"123" ~ "Polonia",
"74" ~ "Porto rico",
"45" ~ "Portugal",
"215" ~ "Pracas norte africanas",
"216" ~ "Protetor do sudoeste africano",
"217" ~ "Quenia",
"160" ~ "Quirguistao",
"75" ~ "Quitasueno",
"189" ~ "Republica arabe do egito",
"218" ~ "Republica centro africana",
"173" ~ "Republica da africa do sul",
"140" ~ "Republica da bielorrussia",
"133" ~ "Republica da macedonia",
"56" ~ "Republica de el salvador",
"291" ~ "Republica de fiji",
"120" ~ "Republica de malta",
"191" ~ "Republica do gabao",
"62" ~ "Republica do haiti",
"212" ~ "Republica do niger",
"55" ~ "Republica dominicana",
"88" ~ "Republica guiana",
"135" ~ "Republica tcheca",
"20" ~ "Reservado",
"48" ~ "Reservado",
"49" ~ "Reservado",
"50" ~ "Reservado",
"219" ~ "Reuniao",
"220" ~ "Rodesia (zimbabwe)",
"124" ~ "Romenia",
"76" ~ "Roncador",
"221" ~ "Ruanda",
"274" ~ "Ruiquiu,is",
"348" ~ "Russia",
"222" ~ "Saara espanhol",
"323" ~ "Sabah",
"324" ~ "Samoa americana",
"325" ~ "Samoa ocidental",
"125" ~ "San marino",
"223" ~ "Santa helena",
"77" ~ "Santa lucia",
"78" ~ "Sao cristovao",
"224" ~ "Sao tome e principe",
"79" ~ "Sao vicente",
"327" ~ "Sarawak",
"349" ~ "Senegal",
"276" ~ "Sequin",
"226" ~ "Serra leoa",
"131" ~ "Servia",
"225" ~ "Seychelles",
"277" ~ "Siria",
"227" ~ "Somalia, republica",
"278" ~ "Sri-lanka",
"86" ~ "St. pierre et miquelon",
"228" ~ "Suazilandia",
"229" ~ "Sudao",
"126" ~ "Suecia",
"38" ~ "Suica",
"90" ~ "Suriname",
"127" ~ "Svalbard e jan mayer,is",
"161" ~ "Tadjiquistao",
"279" ~ "Tailandia",
"230" ~ "Tanganica",
"350" ~ "Tanzania",
"162" ~ "Tartaria",
"128" ~ "Tchecoslovaquia",
"335" ~ "Terr. antartico da australia",
"341" ~ "Terras austrais",
"231" ~ "Territ. britanico do oceano indico",
"328" ~ "Territorio de cocos",
"319" ~ "Territorio de papua",
"329" ~ "Timor",
"233" ~ "Togo",
"330" ~ "Tonga",
"232" ~ "Transkei",
"280" ~ "Tregua, estado",
"91" ~ "Trinidad e tobago",
"234" ~ "Tunisia",
"163" ~ "Turcomenistao",
"281" ~ "Turquia",
"331" ~ "Tuvalu",
"164" ~ "Tuvin",
"165" ~ "Ucrania",
"166" ~ "Udmurt",
"235" ~ "Uganda",
"167" ~ "Uniao sovietica",
"25" ~ "Uruguai",
"168" ~ "Uzbequistao",
"92" ~ "Venezuela",
"282" ~ "Vietna do norte",
"283" ~ "Vietna do sul",
"169" ~ "Yakut",
"236" ~ "Zaire",
"237" ~ "Zambia",
"239" ~ "Zimbabwe",
.default = .data$NACIONAL
)) %>%
dplyr::mutate(NACIONAL = as.factor(.data$NACIONAL))
}
# NUM_PROC
if("NUM_PROC" %in% variables_names){
data <- data %>%
dplyr::mutate(NUM_PROC = as.numeric(.data$NUM_PROC))
}
# CAR_INT
if("CAR_INT" %in% variables_names){
data <- data %>%
dplyr::mutate(CAR_INT = as.character(.data$CAR_INT)) %>%
dplyr::mutate(CAR_INT = dplyr::case_match(
.data$CAR_INT,
"1" ~ "Eletivo",
"2" ~ "Urg\u00eancia",
"3" ~ "Acidente no local trabalho ou a serv da empresa",
"4" ~ "Acidente no trajeto para o trabalho",
"5" ~ "Outros tipo de acidente de tr\u00e2nsito",
"6" ~ "Out tp les\u00f5es e envenen por agent qu\u00edm f\u00edsicos",
.default = .data$CAR_INT
)) %>%
dplyr::mutate(CAR_INT = as.factor(.data$CAR_INT))
}
# TOT_PT_SP
if("TOT_PT_SP" %in% variables_names){
data <- data %>%
dplyr::mutate(TOT_PT_SP = as.numeric(.data$TOT_PT_SP))
}
# CPF_AUT
if("CPF_AUT" %in% variables_names){
data <- data %>%
dplyr::mutate(CPF_AUT = as.numeric(.data$CPF_AUT))
}
# HOMONIMO
if("HOMONIMO" %in% variables_names){
data <- data %>%
dplyr::mutate(HOMONIMO = as.character(.data$HOMONIMO)) %>%
dplyr::mutate(HOMONIMO = dplyr::case_match(
.data$HOMONIMO,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$HOMONIMO
)) %>%
dplyr::mutate(HOMONIMO = as.factor(.data$HOMONIMO))
}
# NUM_FILHOS
if("NUM_FILHOS" %in% variables_names){
data <- data %>%
dplyr::mutate(NUM_FILHOS = as.numeric(.data$NUM_FILHOS))
}
# INSTRU
if("INSTRU" %in% variables_names){
data <- data %>%
dplyr::mutate(INSTRU = as.character(.data$INSTRU)) %>%
dplyr::mutate(INSTRU = dplyr::case_match(
.data$INSTRU,
"1" ~ "Analfabeto",
"2" ~ "1\u00ba grau",
"3" ~ "2\u00ba grau",
"4" ~ "3\u00ba grau",
"0" ~ NA,
"9" ~ NA,
.default = .data$INSTRU
)) %>%
dplyr::mutate(INSTRU = as.factor(.data$INSTRU))
}
# CONTRACEP1
if("CONTRACEP1" %in% variables_names){
data <- data %>%
dplyr::mutate(CONTRACEP1 = as.character(.data$CONTRACEP1)) %>%
dplyr::mutate(CONTRACEP1 = dplyr::case_match(
.data$CONTRACEP1,
"1" ~ "LAM",
"2" ~ "Ogino Kaus",
"3" ~ "Temperatura basal",
"4" ~ "Billings",
"5" ~ "Cinto t\u00e9rmico",
"6" ~ "DIU",
"7" ~ "Diafragma",
"8" ~ "Preservativo",
"9" ~ "Espermicida",
"10" ~ "Horm\u00f4nio oral",
"11" ~ "Horm\u00f4nio injet\u00e1vel",
"12" ~ "Coito interrompido",
"0" ~ NA,
"99" ~ NA,
.default = .data$CONTRACEP1
)) %>%
dplyr::mutate(CONTRACEP1 = as.factor(.data$CONTRACEP1))
}
# CONTRACEP2
if("CONTRACEP2" %in% variables_names){
data <- data %>%
dplyr::mutate(CONTRACEP2 = as.character(.data$CONTRACEP2)) %>%
dplyr::mutate(CONTRACEP2 = dplyr::case_match(
.data$CONTRACEP2,
"1" ~ "LAM",
"2" ~ "Ogino Kaus",
"3" ~ "Temperatura basal",
"4" ~ "Billings",
"5" ~ "Cinto t\u00e9rmico",
"6" ~ "DIU",
"7" ~ "Diafragma",
"8" ~ "Preservativo",
"9" ~ "Espermicida",
"10" ~ "Horm\u00f4nio oral",
"11" ~ "Horm\u00f4nio injet\u00e1vel",
"12" ~ "Coito interrompido",
"0" ~ NA,
"99" ~ NA,
.default = .data$CONTRACEP2
)) %>%
dplyr::mutate(CONTRACEP2 = as.factor(.data$CONTRACEP2))
}
# GESTRISCO
if("GESTRISCO" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTRISCO = as.character(.data$GESTRISCO)) %>%
dplyr::mutate(GESTRISCO = dplyr::case_match(
.data$GESTRISCO,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$GESTRISCO
)) %>%
dplyr::mutate(GESTRISCO = as.factor(.data$GESTRISCO))
}
# SEQ_AIH5
if("SEQ_AIH5" %in% variables_names){
data <- data %>%
dplyr::mutate(SEQ_AIH5 = as.character(.data$SEQ_AIH5)) %>%
dplyr::mutate(SEQ_AIH5 = dplyr::case_match(
.data$SEQ_AIH5,
"0" ~ "Sequencial zerado",
"1" ~ "Seq 1",
"2" ~ "Seq 2",
"3" ~ "Seq 3",
"4" ~ NA,
"999" ~ NA,
.default = .data$SEQ_AIH5
)) %>%
dplyr::mutate(SEQ_AIH5 = as.factor(.data$SEQ_AIH5))
}
# CBOR
if ("CBOR" %in% variables_names) {
colnames(tabCBO)[1] <- "CBOR"
data$CBOR <- factor(dplyr::left_join(data, tabCBO, by = "CBOR")$nome)
}
# VINCPREV
if("VINCPREV" %in% variables_names){
data <- data %>%
dplyr::mutate(VINCPREV = as.character(.data$VINCPREV)) %>%
dplyr::mutate(VINCPREV = dplyr::case_match(
.data$VINCPREV,
"1" ~ "Aut\u00f4nomo",
"2" ~ "Desempregado",
"3" ~ "Aposentado",
"4" ~ "N\u00e3o segurado",
"5" ~ "Empregado",
"6" ~ "Empregador",
"0" ~ NA,
"9" ~ NA,
.default = .data$VINCPREV
)) %>%
dplyr::mutate(VINCPREV = as.factor(.data$VINCPREV))
}
# GESTOR_COD
if("GESTOR_COD" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_COD = as.character(.data$GESTOR_COD)) %>%
dplyr::mutate(GESTOR_COD = dplyr::case_match(
.data$GESTOR_COD,
"1" ~ "TEMPO DE PERMANENCIA",
"2" ~ "IDADE MENOR",
"3" ~ "IDADE MAIOR",
"4" ~ "TEMPO DE PERMANENCIA E IDADE",
"5" ~ "QUANTIDADE MAXIMA",
"7" ~ "PERM.MENOR",
"8" ~ "ID.MENOR",
"9" ~ "ID.MENOR E PERM.MENOR",
"10" ~ "ID.MAIOR",
"11" ~ "ID.MAIOR E PERM.MENOR",
"14" ~ "QTD",
"15" ~ "QTD E PERM.MENOR",
"16" ~ "QTD E ID.MENOR",
"17" ~ "QTD E ID.MENOR E PERM.MENOR",
"18" ~ "QTD E ID.MAIOR",
"19" ~ "QTD E ID.MAIOR E PERM.MENOR",
"38" ~ "CBO",
"39" ~ "CBO E PERM.MENOR",
"40" ~ "CBO E ID.MENOR",
"41" ~ "CBO E ID.MENOR E PERM.MENOR",
"42" ~ "CBO E ID.MAIOR",
"43" ~ "CBO E ID.MAIOR E PERM.MENOR",
"46" ~ "CBO E QTD",
"47" ~ "CBO E QTD E PERM.MENOR",
"48" ~ "CBO E QTD E ID.MENOR",
"49" ~ "CBO E QTD E ID.MENOR E PERM.MENOR",
"50" ~ "CBO E QTD E ID.MAIOR",
"51" ~ "CBO E QTD E ID.MAIOR E PERM.MENOR",
"70" ~ "TELEFONE",
"71" ~ "TELEFONE E PERM.MENOR",
"72" ~ "TELEFONE E ID.MENOR",
"73" ~ "TELEFONE E ID.MENOR E PERM.MENOR",
"74" ~ "TELEFONE E ID.MAIOR",
"75" ~ "TELEFONE E ID.MAIOR E PERM.MENOR",
"78" ~ "TELEFONE E QTD",
"79" ~ "TELEFONE E QTD E PERM.MENOR",
"80" ~ "TELEFONE E QTD E ID.MENOR",
"81" ~ "TELEFONE E QTD E ID.MENOR E PERM.MENOR",
"82" ~ "TELEFONE E QTD E ID.MAIOR",
"83" ~ "TELEFONE E QTD E ID.MAIOR E PERM.MENOR",
"102" ~ "TELEFONE E CBO",
"103" ~ "TELEFONE E CBO E PERM.MENOR",
"104" ~ "TELEFONE E CBO E ID.MENOR",
"105" ~ "TELEFONE E CBO E ID.MENOR E PERM.MENOR",
"106" ~ "TELEFONE E CBO E ID.MAIOR",
"107" ~ "TELEFONE E CBO E ID.MAIOR E PERM.MENOR",
"110" ~ "TELEFONE E CBO E QTD",
"111" ~ "TELEFONE E CBO E QTD E PERM.MENOR",
"112" ~ "TELEFONE E CBO E QTD E ID.MENOR",
"113" ~ "TELEFONE E CBO E QTD E ID.MENOR E PERM.MENOR",
"114" ~ "TELEFONE E CBO E QTD E ID.MAIOR",
"115" ~ "TELEFONE E CBO E QTD E ID.MAIOR E PERM.MENOR",
"134" ~ "CNS",
"136" ~ "CNS E ID. MENOR",
"137" ~ "CNS E ID. MENOR E PERM. MENOR",
"138" ~ "CNS E ID. MAIOR",
"139" ~ "CNS E ID. MAIOR E PERM. MENOR",
"142" ~ "CNS E QTD",
"143" ~ "CNS E QTD E PERM. MENOR",
"144" ~ "CNS E QTD E ID. MENOR",
"145" ~ "CNS E QTD E ID. MENOR E PERM. MENOR",
"146" ~ "CNS E QTD E ID. MAIOR",
"147" ~ "CNS E QTD E ID. MAIOR E PERM. MENOR",
"166" ~ "CNS E CBO",
"167" ~ "CNS E CBO E PERM. MENOR",
"168" ~ "CNS E CBO E ID. MENOR",
"169" ~ "CNS E CBO E ID. MENOR E PERM. MENOR",
"170" ~ "CNS E CBO E ID. MAIOR",
"171" ~ "CNS E CBO E ID. MAIOR E PERM. MENOR",
"174" ~ "CNS E CBO E QTD",
"175" ~ "CNS E CBO E QTD E PERM. MENOR",
"176" ~ "CNS E CBO E QTD E ID. MENOR",
"177" ~ "CNS E CBO E QTD E ID. MENOR E PERM. MENOR",
"178" ~ "CNS E CBO E QTD E ID. MAIOR",
"179" ~ "CNS E CBO E QTD E ID. MAIOR E PERM. MENOR",
"198" ~ "CNS E TELEFONE",
"199" ~ "CNS E TELEFONE E PERM. MENOR",
"200" ~ "CNS E TELEFONE E ID. MENOR",
"201" ~ "CNS E TELEFONE E ID. MENOR E PERM. MENOR",
"202" ~ "CNS E TELEFONE E ID. MAIOR",
"203" ~ "CNS E TELEFONE E ID. MAIOR E PERM. MENOR",
"206" ~ "CNS E TELEFONE E QTD",
"207" ~ "CNS E TELEFONE E QTD E PERM. MENOR",
"208" ~ "CNS E TELEFONE E QTD E ID. MENOR",
"209" ~ "CNS E TELEFONE E QTD E ID. MENOR E PERM. MENOR",
"210" ~ "CNS E TELEFONE E QTD E ID. MAIOR",
"211" ~ "CNS E TELEFONE E QTD E ID. MAIOR E PERM. MENOR",
"230" ~ "CNS E TELEFONE E CBO",
"231" ~ "CNS E TELEFONE E CBO E PERM. MENOR",
"232" ~ "CNS E TELEFONE E CBO E ID. MENOR",
"233" ~ "CNS E TELEFONE E CBO E ID. MENOR E PERM. MENOR",
"234" ~ "CNS E TELEFONE E CBO E ID. MAIOR",
"235" ~ "CNS E TELEFONE E CBO E ID. MAIOR E PERM. MENOR",
"238" ~ "CNS E TELEFONE E CBO E QTD",
"239" ~ "CNS E TELEFONE E CBO E QTD E PERM. MENOR",
"240" ~ "CNS E TELEFONE E CBO E QTD E ID. MENOR",
"241" ~ "CNS E TELEFONE E CBO E QTD E ID. MENOR E PERM. MENOR",
"242" ~ "CNS E TELEFONE E CBO E QTD E ID. MAIOR",
"243" ~ "CNS E TELEFONE E CBO E QTD E ID. MAIOR E PERM. MENOR",
.default = .data$GESTOR_COD
)) %>%
dplyr::mutate(GESTOR_COD = as.factor(.data$GESTOR_COD))
}
# GESTOR_TP
if("GESTOR_TP" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_TP = as.numeric(.data$GESTOR_TP))
}
# GESTOR_CPF
if("GESTOR_CPF" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_CPF = as.numeric(.data$GESTOR_CPF))
}
# GESTOR_DT
if("GESTOR_DT" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_DT = as.numeric(.data$GESTOR_DT))
}
# INFEHOSP
if("INFEHOSP" %in% variables_names){
data <- data %>%
dplyr::mutate(INFEHOSP = as.character(.data$INFEHOSP)) %>%
dplyr::mutate(INFEHOSP = dplyr::case_match(
.data$INFEHOSP,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$INFEHOSP
)) %>%
dplyr::mutate(INFEHOSP = as.factor(.data$INFEHOSP))
}
# COMPLEX
if("COMPLEX" %in% variables_names){
data <- data %>%
dplyr::mutate(COMPLEX = as.character(.data$COMPLEX)) %>%
dplyr::mutate(COMPLEX = dplyr::case_match(
.data$COMPLEX,
"1" ~ "Aten\u00e7\u00e3o B\u00e1sica",
"2" ~ "M\u00e9dia complexidade",
"3" ~ "Alta complexidade",
"0" ~ NA,
"99" ~ NA,
.default = .data$COMPLEX
)) %>%
dplyr::mutate(COMPLEX = as.factor(.data$COMPLEX))
}
# FINANC
if("FINANC" %in% variables_names){
data <- data %>%
dplyr::mutate(FINANC = as.character(.data$FINANC)) %>%
dplyr::mutate(FINANC = dplyr::case_match(
.data$FINANC,
"1" ~ "Aten\u00e7\u00e3o B\u00e1sica (PAB)",
"2" ~ "Assist\u00eancia Farmac\u00eautica",
"4" ~ "Fundo de A\u00e7\u00f5es Estrat\u00e9gicas e Compensa\u00e7\u00f5es FAEC",
"5" ~ "Incentivo - MAC",
"6" ~ "M\u00e9dia e Alta Complexidade (MAC)",
"7" ~ "Vigil\u00e2ncia em Sa\u00fade",
"0" ~ NA,
"99" ~ NA,
.default = .data$FINANC
)) %>%
dplyr::mutate(FINANC = as.factor(.data$FINANC))
}
# FAEC_TP
if("FAEC_TP" %in% variables_names){
data <- data %>%
dplyr::mutate(FAEC_TP = as.character(.data$FAEC_TP)) %>%
dplyr::mutate(FAEC_TP = dplyr::case_match(
.data$FAEC_TP,
"10000" ~ "Aten\u00e7\u00e3o B\u00e1sica (PAB)",
"20000" ~ "Assist\u00eancia Farmac\u00eautica",
"40001" ~ "Coleta de material",
"40002" ~ "Diagn\u00f3stico em laborat\u00f3rio cl\u00ednico",
"40003" ~ "Coleta/exame an\u00e1tomo-patol\u00f3gico colo uterino",
"40004" ~ "Diagn\u00f3stico em neurologia",
"40005" ~ "Diagn\u00f3stico em otorrinolaringologia/fonoaudiologia",
"40006" ~ "Diagn\u00f3stico em psicologia/psiquiatria",
"40007" ~ "Consultas m\u00e9dicas/outros profissionais de n\u00edvel superior",
"40008" ~ "Aten\u00e7\u00e3o domiciliar",
"40009" ~ "Atendimento/acompanhamento em reabilita\u00e7\u00e3o f\u00edsica, mental, visual, auditiva e m\u00faltiplas defic",
"40010" ~ "Atendimento/acompanhamento psicossocial",
"40011" ~ "Atendimento/acompanhamento em sa\u00fade do idoso",
"40012" ~ "Atendimento/acompanhamento de queimados",
"40013" ~ "Atendimento/acompanhamento de diagn\u00f3stico de doen\u00e7as endocrinas/metab\u00f3licas e nutricionais",
"40014" ~ "Tratamento de doen\u00e7as do sistema nervoso central e perif\u00e9rico",
"40015" ~ "Tratamento de doen\u00e7as do aparelho da vis\u00e3o",
"40016" ~ "Tratamento em oncologia",
"40017" ~ "Nefrologia",
"40018" ~ "Tratamentos odontol\u00f3gicos",
"40019" ~ "Cirurgia do sistema nervoso central e perif\u00e9rico",
"40020" ~ "Cirurgias de ouvido, nariz e garganta",
"40021" ~ "Deformidade labio-palatal e cr\u00e2nio-facial",
"40022" ~ "Cirurgia do aparelho da vis\u00e3o",
"40023" ~ "Cirurgia do aparelho circulat\u00f3rio",
"40024" ~ "Cirurgia do aparelho digestivo, org\u00e3os anexos e parede abdominal(inclui pr\u00e9 e p\u00f3s operat\u00f3rio)",
"40025" ~ "Cirurgia do aparelho geniturin\u00e1rio",
"40026" ~ "Tratamento de queimados",
"40027" ~ "Cirurgia reparadora para lipodistrofia",
"40028" ~ "Outras cirurgias pl\u00e1sticas/reparadoras",
"40029" ~ "Cirurgia orofacial",
"40030" ~ "Sequenciais",
"40031" ~ "Cirurgias em nefrologia",
"40032" ~ "Transplantes de org\u00e3os, tecidos e c\u00e9lulas",
"40033" ~ "Medicamentos para transplante",
"40034" ~ "OPM auditivas",
"40035" ~ "OPM em odontologia",
"40036" ~ "OPM em queimados",
"40037" ~ "OPM em nefrologia",
"40038" ~ "OPM para transplantes",
"40039" ~ "Incentivos ao pr\u00e9-natal e nascimento",
"40040" ~ "Incentivo ao registro c\u00edvil de nascimento",
"40041" ~ "Central Nacional de Regula\u00e7\u00e3o de Alta Complexidade (CNRAC)",
"40042" ~ "Reguladores de Atividade hormonal - Inibidores de prolactina",
"40043" ~ "Pol\u00edtica Nacional de Cirurgias Eletivas",
"40044" ~ "Redesigna\u00e7\u00e3o e Acompanhamento",
"40045" ~ "Projeto Olhar Brasil",
"40046" ~ "Mamografia para Rastreamento",
"40047" ~ "Projeto Olhar Brasil - Consulta",
"40048" ~ "Projeto Olhar Brasil - \u00d3culos",
"40049" ~ "Implementar Cirg. CV Pedi\u00e1trica",
"40050" ~ "Cirurgias Eletivas - Componente I",
"40051" ~ "Cirurgias Eletivas - Componente II",
"40052" ~ "Cirurgias Eletivas - Componente III",
"40053" ~ "Pr\u00f3tese Mam\u00e1ria - Exames",
"40054" ~ "Pr\u00f3tese Mam\u00e1ria - Cirurgia",
"40055" ~ "Transplante - Histocompatibilidade",
"40056" ~ "Triagem Neonatal",
"40057" ~ "Controle de qualidade do exame citopatol\u00f3gico do colo de \u00fatero",
"40058" ~ "Exames do Leite Materno",
"40059" ~ "Aten\u00e7\u00e3o as Pessoas em Situa\u00e7\u00e3o de Viol\u00eancia Sexual",
"40060" ~ "Sangue e Hemoderivados",
"40061" ~ "Mamografia para rastreamento em faixa et\u00e1ria recomendada",
"40062" ~ "Doen\u00e7as Raras",
"40063" ~ "Cadeiras de Rodas",
"40064" ~ "Sistema de Frequencia Modulada Pessoal-FM",
"40065" ~ "Medicamentos em Urg\u00eancia",
"40066" ~ "Cirurgias Eletivas - Componente \u00danico",
"40067" ~ "Aten\u00e7\u00e3o Especializada em Sa\u00fade Auditiva",
"40068" ~ "Terapias Especializadas em Angiologia",
"21012" ~ "FAEC CNRAC (21012-c\u00f3d ant \u00e0 tab unif-v\u00e1l p/2008-01)",
"21014" ~ "FAEC Eletiv(21014-c\u00f3d ant \u00e0 tab unif-v\u00e1l p/2008-01)",
"50000" ~ "Incentivo - MAC",
"60000" ~ "M\u00e9dia e Alta Complexidade (MAC)",
"70000" ~ "Vigil\u00e2ncia em Sa\u00fade",
"80000" ~ "Gest\u00e3o do SUS",
.default = .data$FAEC_TP
)) %>%
dplyr::mutate(FAEC_TP = as.factor(.data$FAEC_TP))
}
# REGCT
if("REGCT" %in% variables_names){
data <- data %>%
dplyr::mutate(REGCT = as.character(.data$REGCT)) %>%
dplyr::mutate(REGCT = dplyr::case_match(
.data$REGCT,
"7100" ~ "TABELA DE NAO GERACAO DE CREDITO POR PRODUCAO NA INTERNACAO E/OU AMBULATORIO",
"7101" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA MEDIA COMPLEXIDADE AMBULATORIAL",
"7102" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA MEDIA COMPLEXIDADE HOSPITALAR",
"7103" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA ALTA COMPLEXIDADE AMBULATORIAL",
"7104" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA ALTA COMPLEXIDADE HOSPITALAR",
"7105" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO PARA OS PROCEDIMENTOS FINANCIADOS COM O FAEC",
"7106" ~ "ESTABELECIMENTO SEM GERA\u00c7\u00c3O DE CREDITO TOTAL - EXCLUINDO FAEC",
"7107" ~ "ESTABELECIMENTO SEM GERACAO DE CREDITO NAS ACOES ESPEC. DE ODONTOLOGIA(INCENTIVO CEO I,II E III)",
"7108" ~ "ESTABELECIMENTO SEM GERACAO DE CREDITO(INCENTIVO A SAUDE DO TRABALHADOR)",
"7109" ~ "ESTABELECIMENTO SEM GERACAO DE CREDITO TOTAL-MEC",
"7110" ~ "ESTABELECIMENTO DE SAUDE DA ESTRUTURA DO MINISTERIO DA SAUDE - SEM GERA\u00c7AO DE CREDITO TOTAL",
"7111" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO - NASF, EXCETO FAEC",
"7112" ~ "ESTABELECIMENTO SEM GERA\u00c7\u00c3O DE CREDITO TOTAL - INCLUINDO FAEC - EXCLUSIVO PARA REDE SARAH",
"7113" ~ "ESTABELECIMENTO SEM GERA\u00c7\u00c3O DE CREDITO TOTAL - INCLUINDO FAEC - OUTROS ESTABELECIMENTOS FEDERAIS",
"7114" ~ "ESTABELECIMENTO DE SA\u00daDE SEM GERA\u00c7\u00c3O DE CR\u00c9DITO TOTAL, INCLUSIVE FAEC - PRONTO ATENDIMENTO",
"7115" ~ "ESTABELECIMENTO DE SA\u00daDE SEM GERA\u00c7\u00c3O DE CR\u00c9DITO NA M\u00c9DIA COMPLEXIDADE - HU/MEC",
"7116" ~ "ESTABELECIMENTO DE SA\u00daDE SEM GERA\u00c7\u00c3O DE CR\u00c9DITO NA M\u00c9DIA COMPLEXIDADE - LRPD",
"7117" ~ "Estabelecimento de Sa\u00fade sem gera\u00e7\u00e3o de cr\u00e9dito na m\u00e9dia complexidade (exceto OPM) - CER",
"0" ~ "Sem regra contratual",
.default = .data$REGCT
)) %>%
dplyr::mutate(REGCT = as.factor(.data$REGCT))
}
# RACA_COR
if("RACA_COR" %in% variables_names){
data <- data %>%
dplyr::mutate(RACA_COR = as.character(.data$RACA_COR)) %>%
dplyr::mutate(RACA_COR = dplyr::case_match(
.data$RACA_COR,
"1" ~ "Branca",
"2" ~ "Preta",
"3" ~ "Parda",
"4" ~ "Amarela",
"5" ~ "Ind\u00edgena",
"0" ~ NA,
"99" ~ NA,
.default = .data$RACA_COR
)) %>%
dplyr::mutate(RACA_COR = as.factor(.data$RACA_COR))
}
# ETNIA
if("ETNIA" %in% variables_names){
data <- data %>%
dplyr::mutate(ETNIA = as.character(.data$ETNIA)) %>%
dplyr::mutate(ETNIA = dplyr::case_match(
.data$ETNIA,
"0001" ~ "ACONA (WAKONAS, NACONAS, JAKONA, ACORANES)",
"0002" ~ "AIKANA (AIKANA, MAS SAKA,TUBARAO)",
"0003" ~ "AJURU",
"0004" ~ "AKUNSU (AKUNT'SU)",
"0005" ~ "AMANAYE",
"0006" ~ "AMONDAWA",
"0007" ~ "ANAMBE",
"0008" ~ "APARAI (APALAI)",
"0009" ~ "APIAKA (APIACA)",
"0010" ~ "APINAYE (APINAJE/APINAIE/APINAGE)",
"0011" ~ "APURINA (APORINA, IPURINA, IPURINA, IPURINAN)",
"0012" ~ "ARANA (ARACUAI DO VALE DO JEQUITINHONHA)",
"0013" ~ "ARAPASO (ARAPACO)",
"0014" ~ "ARARA DE RONDONIA (KARO, URUCU, URUKU)",
"0015" ~ "ARARA DO ACRE (SHAWANAUA, AMAWAKA)",
"0016" ~ "ARARA DO ARIPUANA (ARARA DO BEIRADAO/ARI-PUANA)",
"0017" ~ "ARARA DO PARA (UKARAGMA, UKARAMMA)",
"0018" ~ "ARAWETE (ARAUETE)",
"0019" ~ "ARIKAPU (ARICAPU, ARIKAPO, MASUBI, MAXUBI)",
"0020" ~ "ARIKEM (ARIQUEN, ARIQUEME, ARIKEME)",
"0021" ~ "ARIKOSE (ARICOBE)",
"0022" ~ "ARUA",
"0023" ~ "ARUAK (ARAWAK)",
"0024" ~ "ASHANINKA (KAMPA)",
"0025" ~ "ASURINI DO TOCANTINS (AKUAWA/AKWAWA)",
"0026" ~ "ASURINI DO XINGU (AWAETE)",
"0027" ~ "ATIKUM (ATICUM)",
"0028" ~ "AVA - CANOEIRO",
"0029" ~ "AWETI (AUETI/AUETO)",
"0030" ~ "BAKAIRI (KURA, BACAIRI)",
"0031" ~ "BANAWA YAFI (BANAWA, BANAWA-JAFI)",
"0032" ~ "BANIWA (BANIUA, BANIVA, WALIMANAI, WAKUENAI)",
"0033" ~ "BARA (WAIPINOMAKA)",
"0034" ~ "BARASANA (HANERA)",
"0035" ~ "BARE",
"0036" ~ "BORORO (BOE)",
"0037" ~ "BOTOCUDO (GEREN)",
"0038" ~ "CANOE",
"0039" ~ "CASSUPA",
"0040" ~ "CHAMACOCO",
"0041" ~ "CHIQUITANO (XIQUITANO)",
"0042" ~ "CIKIYANA (SIKIANA)",
"0043" ~ "CINTA LARGA (MATETAMAE)",
"0044" ~ "COLUMBIARA (CORUMBIARA)",
"0045" ~ "DENI",
"0046" ~ "DESANA (DESANA, DESANO, DESSANO, WIRA, UMUKOMASA)",
"0047" ~ "DIAHUI (JAHOI, JAHUI, DIARROI)",
"0048" ~ "ENAWENE-NAWE (SALUMA)",
"0049" ~ "FULNI-O",
"0050" ~ "GALIBI (GALIBI DO OIAPOQUE, KARINHA)",
"0051" ~ "GALIBI MARWORNO (GALIBI DO UACA, ARUA)",
"0052" ~ "GAVIAO DE RONDONIA (DIGUT)",
"0053" ~ "GAVIAO KRIKATEJE",
"0054" ~ "GAVIAO PARKATEJE (PARKATEJE)",
"0055" ~ "GAVIAO PUKOBIE (PUKOBIE, PYKOPJE, GAVIAO DO MARANHAO)",
"0056" ~ "GUAJA (AWA, AVA)",
"0057" ~ "GUAJAJARA (TENETEHARA)",
"0058" ~ "GUARANI KAIOWA (PAI TAVYTERA)",
"0059" ~ "GUARANI M'BYA",
"0060" ~ "GUARANI NANDEVA (AVAKATUETE, CHIRIPA,NHANDEWA, AVA GUARANI)",
"0061" ~ "GUATO",
"0062" ~ "HIMARIMA (HIMERIMA)",
"0063" ~ "INGARIKO (INGARICO, AKAWAIO, KAPON)",
"0064" ~ "IRANXE (IRANTXE)",
"0065" ~ "ISSE",
"0066" ~ "JABOTI (JABUTI, KIPIU, YABYTI)",
"0067" ~ "JAMAMADI (YAMAMADI, DJEOROMITXI)",
"0068" ~ "JARAWARA",
"0069" ~ "JIRIPANCO (JERIPANCO, GERIPANCO)",
"0070" ~ "JUMA (YUMA)",
"0071" ~ "JURUNA",
"0072" ~ "JURUTI (YURITI)",
"0073" ~ "KAAPOR (URUBU-KAAPOR, KA'APOR, KAAPORTE)",
"0074" ~ "KADIWEU (CADUVEO, CADIUEU)",
"0075" ~ "KAIABI (CAIABI, KAYABI)",
"0076" ~ "KAIMBE (CAIMBE)",
"0077" ~ "KAINGANG (CAINGANGUE)",
"0078" ~ "KAIXANA (CAIXANA)",
"0079" ~ "KALABASSA (CALABASSA, CALABACAS)",
"0080" ~ "KALANCO",
"0081" ~ "KALAPALO (CALAPALO)",
"0082" ~ "KAMAYURA (CAMAIURA, KAMAIURA)",
"0083" ~ "KAMBA (CAMBA)",
"0084" ~ "KAMBEBA (CAMBEBA, OMAGUA)",
"0085" ~ "KAMBIWA (CAMBIUA)",
"0086" ~ "KAMBIWA PIPIPA (PIPIPA)",
"0087" ~ "KAMPE",
"0088" ~ "KANAMANTI (KANAMATI, CANAMANTI)",
"0089" ~ "KANAMARI (CANAMARI, KANAMARY, TUKUNA)",
"0090" ~ "KANELA APANIEKRA (CANELA)",
"0091" ~ "KANELA RANKOKAMEKRA (CANELA)",
"0092" ~ "KANINDE",
"0093" ~ "KANOE (CANOE)",
"0094" ~ "KANTARURE (CANTARURE)",
"0095" ~ "KAPINAWA (CAPINAUA)",
"0096" ~ "KARAJA (CARAJA)",
"0097" ~ "KARAJA/JAVAE (JAVAE)",
"0098" ~ "KARAJA/XAMBIOA (KARAJA DO NORTE)",
"0099" ~ "KARAPANA (CARAPANA, MUTEAMASA, UKOPINOPONA)",
"0100" ~ "KARAPOTO (CARAPOTO)",
"0101" ~ "KARIPUNA (CARIPUNA)",
"0102" ~ "KARIPUNA DO AMAPA (CARIPUNA)",
"0103" ~ "KARIRI (CARIRI)",
"0104" ~ "KARIRI-XOCO (CARIRI-CHOCO)",
"0105" ~ "KARITIANA (CARITIANA)",
"0106" ~ "KATAWIXI (KATAUIXI,KATAWIN, KATAWISI, CATAUICHI)",
"0107" ~ "KATUENA (CATUENA, KATWENA)",
"0108" ~ "KATUKINA (PEDA DJAPA)",
"0109" ~ "KATUKINA DO ACRE",
"0110" ~ "KAXARARI (CAXARARI)",
"0111" ~ "KAXINAWA (HUNI-KUIN, CASHINAUA, CAXINAUA)",
"0112" ~ "KAXIXO",
"0113" ~ "KAXUYANA (CAXUIANA)",
"0114" ~ "KAYAPO (CAIAPO)",
"0115" ~ "KAYAPO KARARAO (KARARAO)",
"0116" ~ "KAYAPO TXUKAHAMAE (TXUKAHAMAE)",
"0117" ~ "KAYAPO XICRIM (XIKRIN)",
"0118" ~ "KAYUISANA (CAIXANA, CAUIXANA, KAIXANA)",
"0119" ~ "KINIKINAWA (GUAN, KOINUKOEN, KINIKINAO)",
"0120" ~ "KIRIRI",
"0121" ~ "KOCAMA (COCAMA, KOKAMA)",
"0122" ~ "KOKUIREGATEJE",
"0123" ~ "KORUBO",
"0124" ~ "KRAHO (CRAO, KRAO)",
"0125" ~ "KREJE (KRENYE)",
"0126" ~ "KRENAK (BORUN, CRENAQUE)",
"0127" ~ "KRIKATI (KRINKATI)",
"0128" ~ "KUBEO (CUBEO, COBEWA, KUBEWA, PAMIWA, CUBEU)",
"0129" ~ "KUIKURO (KUIKURU, CUICURO)",
"0130" ~ "KUJUBIM (KUYUBI, CUJUBIM)",
"0131" ~ "KULINA PANO (CULINA)",
"0132" ~ "KULINA/MADIHA (CULINA, MADIJA, MADIHA)",
"0133" ~ "KURIPAKO (CURIPACO, CURRIPACO, CORIPACO, WAKUENAI)",
"0134" ~ "KURUAIA (CURUAIA)",
"0135" ~ "KWAZA (COAIA, KOAIA)",
"0136" ~ "MACHINERI (MANCHINERI, MANXINERI)",
"0137" ~ "MACURAP (MAKURAP)",
"0138" ~ "MAKU DOW (DOW)",
"0139" ~ "MAKU HUPDA (HUPDA)",
"0140" ~ "MAKU NADEB (NADEB)",
"0141" ~ "MAKU YUHUPDE (YUHUPDE)",
"0142" ~ "MAKUNA (MACUNA, YEBA-MASA)",
"0143" ~ "MAKUXI (MACUXI, MACHUSI, PEMON)",
"0144" ~ "MARIMAM (MARIMA)",
"0145" ~ "MARUBO",
"0146" ~ "MATIPU",
"0147" ~ "MATIS",
"0148" ~ "MATSE (MAYORUNA)",
"0149" ~ "MAXAKALI (MAXACALI)",
"0150" ~ "MAYA (MAYA)",
"0151" ~ "MAYTAPU",
"0152" ~ "MEHINAKO (MEINAKU, MEINACU)",
"0153" ~ "MEKEN (MEQUEM, MEKHEM, MICHENS)",
"0154" ~ "MENKY (MYKY, MUNKU, MENKI, MYNKY)",
"0155" ~ "MIRANHA (MIRANHA, MIRANA)",
"0156" ~ "MIRITI TAPUIA (MIRITI-TAPUYA, BUIA-TAPUYA)",
"0157" ~ "MUNDURUKU (MUNDURUCU)",
"0158" ~ "MURA",
"0159" ~ "NAHUKWA (NAFUQUA)",
"0160" ~ "NAMBIKWARA DO CAMPO (HALOTESU, KITHAULU, WAKALITESU, SAWENTES, MANDUKA)",
"0161" ~ "NAMBIKWARA DO NORTE (NEGAROTE ,MAMAINDE, LATUNDE, SABANE E MANDUKA, TAWANDE)",
"0162" ~ "NAMBIKWARA DO SUL (WASUSU ,HAHAINTESU, ALANTESU, WAIKISU, ALAKETESU, WASUSU, SARARE)",
"0163" ~ "NARAVUTE (NARUVOTO)",
"0164" ~ "NAWA (NAUA)",
"0165" ~ "NUKINI (NUQUINI, NUKUINI)",
"0166" ~ "OFAIE (OFAYE-XAVANTE)",
"0167" ~ "ORO WIN",
"0168" ~ "PAIAKU (JENIPAPO-KANINDE)",
"0169" ~ "PAKAA NOVA (WARI, PACAAS NOVOS)",
"0170" ~ "PALIKUR (AUKWAYENE, AUKUYENE, PALIKU'ENE)",
"0171" ~ "PANARA (KRENHAKARORE , KRENAKORE, KRENA-KARORE)",
"0172" ~ "PANKARARE (PANCARARE)",
"0173" ~ "PANKARARU (PANCARARU)",
"0174" ~ "PANKARARU KALANKO (KALANKO)",
"0175" ~ "PANKARARU KARUAZU (KARUAZU)",
"0176" ~ "PANKARU (PANCARU)",
"0177" ~ "PARAKANA (PARACANA, APITEREWA, AWAETE)",
"0178" ~ "PARECI (PARESI, HALITI)",
"0179" ~ "PARINTINTIN",
"0180" ~ "PATAMONA (KAPON)",
"0181" ~ "PATAXO",
"0182" ~ "PATAXO HA-HA-HAE",
"0183" ~ "PAUMARI (PALMARI)",
"0184" ~ "PAUMELENHO",
"0185" ~ "PIRAHA (MURA PIRAHA)",
"0186" ~ "PIRATUAPUIA (PIRATAPUYA, PIRATAPUYO, PIRA-TAPUYA, WAIKANA)",
"0187" ~ "PITAGUARI",
"0188" ~ "POTIGUARA",
"0189" ~ "POYANAWA (POIANAUA)",
"0190" ~ "RIKBAKTSA (CANOEIROS, ERIGPAKTSA)",
"0191" ~ "SAKURABIAT(MEKENS, SAKIRABIAP, SAKIRABIAR)",
"0192" ~ "SATERE-MAWE (SATERE-MAUE)",
"0193" ~ "SHANENAWA (KATUKINA)",
"0194" ~ "SIRIANO (SIRIA-MASA)",
"0195" ~ "SURIANA",
"0196" ~ "SURUI DE RONDONIA (PAITER)",
"0197" ~ "SURUI DO PARA (AIKEWARA)",
"0198" ~ "SUYA (SUIA/KISEDJE)",
"0199" ~ "TAPAYUNA (BEICO-DE-PAU)",
"0200" ~ "TAPEBA",
"0201" ~ "TAPIRAPE (TAPI'IRAPE)",
"0202" ~ "TAPUIA (TAPUIA-XAVANTE, TAPUIO)",
"0203" ~ "TARIANO (TARIANA, TALIASERI)",
"0204" ~ "TAUREPANG (TAULIPANG, PEMON, AREKUNA, PAGEYN)",
"0205" ~ "TEMBE",
"0206" ~ "TENHARIM",
"0207" ~ "TERENA",
"0208" ~ "TICUNA (TIKUNA, TUKUNA, MAGUTA)",
"0209" ~ "TINGUI BOTO",
"0210" ~ "TIRIYO EWARHUYANA (TIRIYO, TRIO, TARONA, YAWI, PIANOKOTO)",
"0211" ~ "TIRIYO KAH'YANA (TIRIYO, TRIO, TARONA, YAWI, PIANOKOTO)",
"0212" ~ "TIRIYO TSIKUYANA (TIRIYO, TRIO, TARONA, YAWI, PIANOKOTO)",
"0213" ~ "TORA",
"0214" ~ "TREMEMBE",
"0215" ~ "TRUKA",
"0216" ~ "TRUMAI",
"0217" ~ "TSOHOM DJAPA (TSUNHUM-DJAPA)",
"0218" ~ "TUKANO (TUCANO, YE'PA-MASA, DASEA)",
"0219" ~ "TUMBALALA",
"0220" ~ "TUNAYANA",
"0221" ~ "TUPARI",
"0222" ~ "TUPINAMBA",
"0223" ~ "TUPINIQUIM",
"0224" ~ "TURIWARA",
"0225" ~ "TUXA",
"0226" ~ "TUYUKA (TUIUCA, DOKAPUARA, UTAPINOMAKAPHONA)",
"0227" ~ "TXIKAO (TXICAO, IKPENG)",
"0228" ~ "UMUTINA (OMOTINA, BARBADOS)",
"0229" ~ "URU-EU-WAU-WAU (URUEU-UAU-UAU, URUPAIN, URUPA)",
"0230" ~ "WAI WAI HIXKARYANA (HIXKARYANA)",
"0231" ~ "WAI WAI KARAFAWYANA (KARAFAWYANA, KARA-PAWYANA)",
"0232" ~ "WAI WAI XEREU (XEREU)",
"0233" ~ "WAI WAI KATUENA (KATUENA)",
"0234" ~ "WAI WAI MAWAYANA (MAWAYANA)",
"0235" ~ "WAIAPI (WAYAMPI, OYAMPI, WAYAPY, )",
"0236" ~ "WAIMIRI ATROARI (KINA)",
"0237" ~ "WANANO (UANANO, WANANA)",
"0238" ~ "WAPIXANA (UAPIXANA, VAPIDIANA, WAPISIANA, WAPISHANA)",
"0239" ~ "WAREKENA (UAREQUENA, WEREKENA)",
"0240" ~ "WASSU",
"0241" ~ "WAURA (UAURA, WAUJA)",
"0242" ~ "WAYANA (WAIANA, UAIANA)",
"0243" ~ "WITOTO (UITOTO, HUITOTO)",
"0244" ~ "XAKRIABA (XACRIABA)",
"0245" ~ "XAVANTE (A'UWE, AKWE, AWEN, AKWEN)",
"0246" ~ "XERENTE (AKWE, AWEN, AKWEN)",
"0247" ~ "XETA",
"0248" ~ "XIPAIA (SHIPAYA, XIPAYA)",
"0249" ~ "XOKLENG (SHOKLENG, XOCLENG)",
"0250" ~ "XOKO (XOCO, CHOCO)",
"0251" ~ "XUKURU (XUCURU)",
"0252" ~ "XUKURU KARIRI (XUCURU-KARIRI)",
"0253" ~ "YAIPIYANA",
"0254" ~ "YAMINAWA (JAMINAWA, IAMINAWA)",
"0255" ~ "YANOMAMI NINAM (IANOMAMI, IANOAMA, XIRIANA)",
"0256" ~ "YANOMAMI SANUMA (IANOMAMI, IANOAMA, XIRIANA)",
"0257" ~ "YANOMAMI YANOMAM (IANOMAMI, IANOAMA, XIRIANA)",
"0258" ~ "YAWALAPITI (IAUALAPITI)",
"0259" ~ "YAWANAWA (IAUANAUA)",
"0260" ~ "YEKUANA (MAIONGON, YE'KUANA, YEKWANA, MAYONGONG)",
"0261" ~ "YUDJA (JURUNA, YURUNA)",
"0262" ~ "ZO'E (POTURU)",
"0263" ~ "ZORO (PAGEYN)",
"0264" ~ "ZURUAHA (SOROWAHA, SURUWAHA)",
"X265" ~ "AHANENAWA",
"X266" ~ "AICABA",
"X267" ~ "AIKAN\\u00c3-KWAS\\u00c1",
"X268" ~ "AKUNTSU",
"X269" ~ "ALANTESU",
"X271" ~ "AMAW\\u00c1KA",
"X272" ~ "ANAC\\u00c9",
"X273" ~ "APURIN\\u00c3",
"X274" ~ "ARAN\\u00c3",
"X275" ~ "ARAPA\\u00c7O",
"X276" ~ "ARARA APOLIMA",
"X277" ~ "ARARA DO ARIPUANA",
"X278" ~ "ARIPUAN\\u00c1",
"X279" ~ "ASSURINI",
"X280" ~ "AWUAR\\u00c1",
"X281" ~ "BORBA",
"X282" ~ "CABIXI",
"X283" ~ "CAMARAR\\u00c9",
"X284" ~ "CAMASURI",
"X285" ~ "CARA PRETA",
"X286" ~ "CHARRUA",
"X287" ~ "CUJUBIM",
"X288" ~ "DAW",
"X289" ~ "GAVI\\u00c3O",
"X290" ~ "GUARANI",
"X291" ~ "HALANTESU",
"X292" ~ "HALOTESU",
"X293" ~ "HENGAT\\u00da",
"X294" ~ "HIXKARYANA",
"X295" ~ "HUPDE",
"X296" ~ "HUPDES",
"X297" ~ "IAUANAUA",
"X298" ~ "IAUARETE A\\u00c7U",
"X299" ~ "IKPENG",
"X300" ~ "INAMBU",
"X301" ~ "INHABARANA",
"X302" ~ "JAVAE",
"X303" ~ "JENIPAPO",
"X304" ~ "JENIPAPO-KANINDE",
"X305" ~ "JIAHOI",
"X306" ~ "KAIOWA",
"X307" ~ "KAMPA",
"X308" ~ "KANELA",
"X309" ~ "KARAFAWYANA",
"X310" ~ "KARARAO",
"X311" ~ "KARUBO",
"X312" ~ "KASSUP\\u00c1",
"X313" ~ "KATITH\\u00c3ULU",
"X314" ~ "KATOKIN",
"X315" ~ "KATUKINA PANO",
"X316" ~ "KATUKINA PEDA DJAPA",
"X317" ~ "KATUKINA SHANENAUWA",
"X318" ~ "KAXAGO",
"X319" ~ "KAYABI",
"X320" ~ "KIN\\u00c3 (WAIMIRI-ATROARI)",
"X321" ~ "KIRIRI-BARRA",
"X322" ~ "KITH\\u00c3ULU",
"X323" ~ "KOIAI\\u00c1",
"X324" ~ "KOIUPANK\\u00c1",
"X325" ~ "KONTANAWA",
"X326" ~ "KRAH\\u00d4 KANELA",
"X327" ~ "KULINA",
"X328" ~ "LATUND\\u00ca",
"X329" ~ "MAKU",
"X330" ~ "MAKUNAMB\\u00c9",
"X331" ~ "MAMAIND\\u00ca",
"X332" ~ "MAMURI",
"X333" ~ "MANACAPURU",
"X334" ~ "MANAIRISSU",
"X335" ~ "MANCHINERI",
"X336" ~ "MANDUCA",
"X337" ~ "MARIBONDO",
"X338" ~ "MASSAKA",
"X339" ~ "MAWAYANA",
"X340" ~ "MAW\\u00c9",
"X341" ~ "MAYORUNA",
"X342" ~ "MIQUELENO",
"X343" ~ "MOKURI\\u00d1",
"X344" ~ "MON ORO WARAM",
"X345" ~ "MUTUM",
"X346" ~ "MYKY",
"X347" ~ "NADEB",
"X348" ~ "NAMBIKWARA",
"X349" ~ "NEGAROT\\u00ca",
"X350" ~ "NHENGATU",
"X351" ~ "OFAIE XAVANTE",
"X352" ~ "ON\\u00c7A",
"X353" ~ "ORO AT",
"X354" ~ "ORO EO",
"X355" ~ "ORO JOWIN",
"X356" ~ "ORO MIYLIN",
"X357" ~ "ORO MON",
"X358" ~ "ORO N\\u00c1O",
"X359" ~ "ORO WAM",
"X360" ~ "ORO WARAM",
"X361" ~ "ORO WARAM XIJEIN",
"X362" ~ "PACA",
"X363" ~ "PANKAR\\u00c1",
"X364" ~ "PAPAGAIO",
"X365" ~ "PAYAY\\u00c1",
"X366" ~ "PIPIPAN",
"X367" ~ "PIRATA",
"X368" ~ "PUROBOR\\u00c1",
"X369" ~ "SABAN\\u00ca",
"X370" ~ "SANUMA",
"X371" ~ "SAWENTES\\u00da",
"X372" ~ "SILCY-TAPUYA",
"X373" ~ "SIUCI",
"X374" ~ "TABAJARA",
"X375" ~ "TAKUARA",
"X376" ~ "TATU",
"X377" ~ "TAWAND\\u00ca",
"X378" ~ "TEF\\u00c9",
"X379" ~ "TIMBIRA",
"X380" ~ "TOR\\u00c1 DO BAIXO GRANDE",
"X381" ~ "TSUNHUM-DJAP\\u00c1",
"X382" ~ "TUBAR\\u00c3O",
"X383" ~ "TUPAIU",
"X384" ~ "TUPI",
"X385" ~ "TUPINAMB\\u00c1 DE BELMONTE",
"X386" ~ "URUBU",
"X387" ~ "URUBU KAAPOR",
"X388" ~ "URUP\\u00c1",
"X389" ~ "WAI WAI",
"X390" ~ "WAIKISU",
"X391" ~ "WAKALITES\\u00da",
"X392" ~ "WASSUSU",
"X393" ~ "XEREU",
"X394" ~ "XI EIN",
"X395" ~ "XICRIN",
"X396" ~ "XIPAYA",
"X397" ~ "XIRIANA",
"X398" ~ "XIRUAI",
"X399" ~ "YEPAMASS\\u00c3",
"X400" ~ "TIRIY\\u00d3",
"X401" ~ "YANOMAMI",
"X402" ~ "ARARA",
"X403" ~ "SAKIRIABAR",
"X404" ~ "TATZ",
"X405" ~ "SEM INFORMACAO",
.default = .data$ETNIA
)) %>%
dplyr::mutate(ETNIA = as.factor(.data$ETNIA))
}
# VAL_SH_FED
if("VAL_SH_FED" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SH_FED = as.numeric(.data$VAL_SH_FED))
}
# VAL_SP_FED
if("VAL_SP_FED" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SP_FED = as.numeric(.data$VAL_SP_FED))
}
# VAL_SH_GES
if("VAL_SH_GES" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SH_GES = as.numeric(.data$VAL_SH_GES))
}
# VAL_SP_GES
if("VAL_SP_GES" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SP_GES = as.numeric(.data$VAL_SP_GES))
}
# VAL_UCI
if("VAL_UCI" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_UCI = as.numeric(.data$VAL_UCI))
}
# MARCA_UCI
if("MARCA_UCI" %in% variables_names){
data <- data %>%
dplyr::mutate(MARCA_UCI = as.character(.data$MARCA_UCI)) %>%
dplyr::mutate(MARCA_UCI = dplyr::case_match(
.data$MARCA_UCI,
"0" ~ "N\u00e3o utilizou UCI",
"1" ~ "Unidade de cuidados intermed neonatal convencional",
"2" ~ "Unidade de cuidados intermed neonatal canguru",
"3" ~ "Unidade intermedi\u00e1ria neonatal",
"88" ~ "Utilizou dois tipos de leitos UCI",
.default = .data$MARCA_UCI
)) %>%
dplyr::mutate(MARCA_UCI = as.factor(.data$MARCA_UCI))
}
# TPDISEC1
if("TPDISEC1" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC1 = as.character(.data$TPDISEC1)) %>%
dplyr::mutate(TPDISEC1 = dplyr::case_match(
.data$TPDISEC1,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC1
)) %>%
dplyr::mutate(TPDISEC1 = as.factor(.data$TPDISEC1))
}
# TPDISEC2
if("TPDISEC2" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC2 = as.character(.data$TPDISEC2)) %>%
dplyr::mutate(TPDISEC2 = dplyr::case_match(
.data$TPDISEC2,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC2
)) %>%
dplyr::mutate(TPDISEC2 = as.factor(.data$TPDISEC2))
}
# TPDISEC3
if("TPDISEC3" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC3 = as.character(.data$TPDISEC3)) %>%
dplyr::mutate(TPDISEC3 = dplyr::case_match(
.data$TPDISEC3,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC3
)) %>%
dplyr::mutate(TPDISEC3 = as.factor(.data$TPDISEC3))
}
# TPDISEC4
if("TPDISEC4" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC4 = as.character(.data$TPDISEC4)) %>%
dplyr::mutate(TPDISEC4 = dplyr::case_match(
.data$TPDISEC4,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC4
)) %>%
dplyr::mutate(TPDISEC4 = as.factor(.data$TPDISEC4))
data$TPDISEC4 <- factor(data$TPDISEC4)
}
# TPDISEC5
if("TPDISEC5" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC5 = as.character(.data$TPDISEC5)) %>%
dplyr::mutate(TPDISEC5 = dplyr::case_match(
.data$TPDISEC5,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC5
)) %>%
dplyr::mutate(TPDISEC5 = as.factor(.data$TPDISEC5))
}
# TPDISEC6
if("TPDISEC6" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC6 = as.character(.data$TPDISEC6)) %>%
dplyr::mutate(TPDISEC6 = dplyr::case_match(
.data$TPDISEC6,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC6
)) %>%
dplyr::mutate(TPDISEC6 = as.factor(.data$TPDISEC6))
}
# TPDISEC7
if("TPDISEC7" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC7 = as.character(.data$TPDISEC7)) %>%
dplyr::mutate(TPDISEC7 = dplyr::case_match(
.data$TPDISEC7,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC7
)) %>%
dplyr::mutate(TPDISEC7 = as.factor(.data$TPDISEC7))
}
# TPDISEC8
if("TPDISEC8" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC8 = as.character(.data$TPDISEC8)) %>%
dplyr::mutate(TPDISEC8 = dplyr::case_match(
.data$TPDISEC8,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC8
)) %>%
dplyr::mutate(TPDISEC8 = as.factor(.data$TPDISEC8))
}
# TPDISEC9
if("TPDISEC9" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC9 = as.character(.data$TPDISEC9)) %>%
dplyr::mutate(TPDISEC9 = dplyr::case_match(
.data$TPDISEC9,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC9
)) %>%
dplyr::mutate(TPDISEC9 = as.factor(.data$TPDISEC9))
}
}
# From data.table to tibble
data <- tibble::as_tibble(data)
# Purge levels
data <- droplevels(data.table::as.data.table(data))
# Unescape unicode characters
data <- suppressWarnings(tibble::as_tibble(lapply(X = data, FUN = stringi::stri_unescape_unicode)))
# Return
return(data)
}
rfsaldanha/microdatasus documentation built on April 13, 2024, 2:25 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions process_sih
Documented in process_sih
#' Process SIH variables from DataSUS
#'
#' \code{process_SIH} processes SIH variables retrieved by \code{fetch_datasus()}.
#'
#' This function processes SIH variables retrieved by \code{fetch_datasus()}, informing labels for categoric variables including NA values.
#'
#' Currently, only "SIH-RD" is supported.
#'
#' @param data \code{data.frame} created by \code{fetch_datasus()}.
#' @param information_system string. The abbreviation of the health information system. See \emph{Details}.
#' @param municipality_data optional logical. \code{TRUE} by default, creates new variables in the dataset informing the full name and other details about the municipality of residence.
#'
#' @examples \dontrun{
#' df <- fetch_datasus(year_start = 2010, month_start = 1,
#' year_end = 2010, month_end = 1,
#' uf = "RJ",
#' information_system = "SIH-RD")
#' df_a <- process_sih(df)
#' df_b <- process_sih(df, municipality_data = FALSE)
#' }
#' @export
process_sih <- function(data, information_system = "SIH-RD", municipality_data = TRUE) {
# Check information system
available_information_system <- "SIH-RD"
if(!(information_system %in% available_information_system)) stop("Health informaton system unknown.")
# Variables names
variables_names <- names(data)
# Use dtplyr
data <- dtplyr::lazy_dt(data)
if(information_system == "SIH-RD"){
# ANO_CMPT
if("ANO_CMPT" %in% variables_names){
data <- data %>%
dplyr::mutate(ANO_CMPT = as.numeric(.data$ANO_CMPT))
}
# MES_CMPT
if("MES_CMPT" %in% variables_names){
data <- data %>%
dplyr::mutate(MES_CMPT = as.numeric(.data$MES_CMPT))
}
# ESPEC
if("ESPEC" %in% variables_names){
data <- data %>%
dplyr::mutate(ESPEC = as.character(.data$ESPEC)) %>%
dplyr::mutate(PA_REGCT = dplyr::case_match(
.data$ESPEC,
"1" ~ "Cir\u00fargico",
"2" ~ "Obst\u00e9tricos",
"3" ~ "Cl\u00ednicos",
"4" ~ "Cr\u00f4nicos",
"5" ~ "Psiquiatria",
"6" ~ "Pneumologia sanit\u00e1ria (tsiologia)",
"7" ~ "Pedi\u00e1tricos",
"8" ~ "Reabilita\u00e7\u00e3o",
"9" ~ "Leito Dia / Cir\u00fargicos",
"10" ~ "Leito Dia / Aids",
"11" ~ "Leito Dia / Fibrose C\u00edstica",
"12" ~ "Leito Dia / Intercorr\u00eancia P\u00f3s-Transplante",
"13" ~ "Leito Dia / Geriatria",
"14" ~ "Leito Dia / Sa\u00fade Mental",
"51" ~ "UTI II Adulto COVID 19",
"52" ~ "UTI II Pedi\u00e1trica COVID 19",
"64" ~ "Unidade Intermedi\u00e1ria",
"65" ~ "Unidade Intermedi\u00e1ria Neonatal",
"74" ~ "UTI I",
"75" ~ "UTI Adulto II",
"76" ~ "UTI Adulto III",
"77" ~ "UTI Infantil I",
"78" ~ "UTI Infantil II",
"79" ~ "UTI Infantil III",
"80" ~ "UTI Neonatal I",
"81" ~ "UTI Neonatal II",
"82" ~ "UTI Neonatal III",
"83" ~ "UTI Queimados",
"84" ~ "Acolhimento Noturno",
"85" ~ "UTI Coronariana-UCO tipo II",
"86" ~ "UTI Coronariana-UCO tipo III",
"87" ~ "Sa\u00fade Mental (Cl\u00ednico)",
"88" ~ "Queimado Adulto (Cl\u00ednico)",
"89" ~ "Queimado Pedi\u00e1trico (Cl\u00ednico)",
"90" ~ "Queimado Adulto (Cir\u00fargico)",
"91" ~ "Queimado Pedi\u00e1trico (Cir\u00fargico)",
"92" ~ "UCI Unidade de Cuidados Intermediarios Neonatal Convencional",
"93" ~ "UCI Unidade de Cuidados Intermediarios Neonatal Canguru",
"94" ~ "UCI Unidade de Cuidados Intermediarios Pediatrico",
"95" ~ "UCI Unidade de Cuidados Intermediarios Adulto",
"96" ~ "Suporte Ventilat\u00f3rio Pulmonar COVID-19",
.default = .data$ESPEC
)) %>%
dplyr::mutate(ESPEC = as.factor(.data$ESPEC))
}
# IDENT
if("IDENT" %in% variables_names){
data <- data %>%
dplyr::mutate(IDENT = as.character(.data$IDENT)) %>%
dplyr::mutate(IDENT = dplyr::case_match(
.data$IDENT,
"1" ~ "Principal",
"3" ~ "Continua\u00e7\u00e3o",
"5" ~ "Longa perman\u00eancia",
.default = .data$IDENT
)) %>%
dplyr::mutate(IDENT = as.factor(.data$IDENT))
}
# MUNIC_RES
if("MUNIC_RES" %in% variables_names & municipality_data == TRUE){
colnames(tabMun)[1] <- "MUNIC_RES"
data <- data %>%
dplyr::mutate(MUNIC_RES = as.numeric(.data$MUNIC_RES)) %>%
dplyr::left_join(tabMun, by = "MUNIC_RES")
}
# NASC
if("NASC" %in% variables_names){
data <- data %>%
dplyr::mutate(NASC = as.Date(.data$NASC, format = "%Y%m%d"))
}
# SEXO
if("SEXO" %in% variables_names){
data <- data %>%
dplyr::mutate(SEXO = as.character(.data$SEXO)) %>%
dplyr::mutate(SEXO = dplyr::case_match(
.data$SEXO,
"1" ~ "Masculino",
"2" ~ "Feminino",
"3" ~ "Feminino",
"0" ~ NA,
"9" ~ NA,
.default = .data$SEXO
)) %>%
dplyr::mutate(SEXO = as.factor(.data$SEXO))
}
# UTI_MES_IN
if("UTI_MES_IN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_IN = as.numeric(.data$UTI_MES_IN))
}
# UTI_MES_AN
if("UTI_MES_AN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_AN = as.numeric(.data$UTI_MES_AN))
}
# UTI_MES_AL
if("UTI_MES_AL" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_AL = as.numeric(.data$UTI_MES_AL))
}
# UTI_MES_TO
if("UTI_MES_TO" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_MES_TO = as.numeric(.data$UTI_MES_TO))
}
# MARCA_UTI
if("MARCA_UTI" %in% variables_names){
data <- data %>%
dplyr::mutate(MARCA_UTI = as.character(.data$MARCA_UTI)) %>%
dplyr::mutate(MARCA_UTI = dplyr::case_match(
.data$MARCA_UTI,
"0" ~ "N\u00e3o utilizou UTI",
"51" ~ "UTI adulto - tipo II COVID 19",
"52" ~ "UTI pedi\u00e1trica - tipo II COVID 19",
"74" ~ "UTI adulto - tipo I",
"75" ~ "UTI adulto - tipo II",
"76" ~ "UTI adulto - tipo III",
"77" ~ "UTI infantil - tipo I",
"78" ~ "UTI infantil - tipo II",
"79" ~ "UTI infantil - tipo III",
"80" ~ "UTI neonatal - tipo I",
"81" ~ "UTI neonatal - tipo II",
"82" ~ "UTI neonatal - tipo III",
"83" ~ "UTI de queimados",
"85" ~ "UTI coronariana tipo II - UCO tipo II",
"86" ~ "UTI coronariana tipo III - UCO tipo III",
"99" ~ "UTI Doador",
"1" ~ "Utilizou mais de um tipo de UTI",
.default = .data$MARCA_UTI
)) %>%
dplyr::mutate(MARCA_UTI = as.factor(.data$MARCA_UTI))
}
# UTI_INT_IN
if("UTI_INT_IN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_IN = as.numeric(.data$UTI_INT_IN))
}
# UTI_INT_AN
if("UTI_INT_AN" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_AN = as.numeric(.data$UTI_INT_AN))
}
# UTI_INT_AL
if("UTI_INT_AL" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_AL = as.numeric(.data$UTI_INT_AL))
}
# UTI_INT_TO
if("UTI_INT_TO" %in% variables_names){
data <- data %>%
dplyr::mutate(UTI_INT_TO = as.numeric(.data$UTI_INT_TO))
}
# DIAR_ACOM
if("DIAR_ACOM" %in% variables_names){
data <- data %>%
dplyr::mutate(DIAR_ACOM = as.numeric(.data$DIAR_ACOM))
}
# QT_DIARIAS
if("QT_DIARIAS" %in% variables_names){
data <- data %>%
dplyr::mutate(QT_DIARIAS = as.numeric(.data$QT_DIARIAS))
}
# VAL_SH
if("VAL_SH" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SH = as.numeric(.data$VAL_SH))
}
# VAL_SP
if("VAL_SP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SP = as.numeric(.data$VAL_SP))
}
# VAL_SADT
if("VAL_SADT" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SADT = as.numeric(.data$VAL_SADT))
}
# VAL_RN
if("VAL_RN" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_RN = as.numeric(.data$VAL_RN))
}
# VAL_ACOMP
if("VAL_ACOMP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_ACOMP = as.numeric(.data$VAL_ACOMP))
}
# VAL_ORTP
if("VAL_ORTP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_ORTP = as.numeric(.data$VAL_ORTP))
}
# VAL_SANGUE
if("VAL_SANGUE" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SANGUE = as.numeric(.data$VAL_SANGUE))
}
# VAL_SADTSR
if("VAL_SADTSR" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SADTSR = as.numeric(.data$VAL_SADTSR))
}
# VAL_TRANSP
if("VAL_TRANSP" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_TRANSP = as.numeric(.data$VAL_TRANSP))
}
# VAL_OBSANG
if("VAL_OBSANG" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_OBSANG = as.numeric(.data$VAL_OBSANG))
}
# VAL_PED1AC
if("VAL_PED1AC" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_PED1AC = as.numeric(.data$VAL_PED1AC))
}
# VAL_TOT
if("VAL_TOT" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_TOT = as.numeric(.data$VAL_TOT))
}
# VAL_UTI
if("VAL_UTI" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_UTI = as.numeric(.data$VAL_UTI))
}
# US_TOT
if("US_TOT" %in% variables_names){
data <- data %>%
dplyr::mutate(US_TOT = as.numeric(.data$US_TOT))
}
# DT_INTER
if("DT_INTER" %in% variables_names){
data <- data %>%
dplyr::mutate(DT_INTER = as.Date(.data$DT_INTER, format = "%Y%m%d"))
}
# DT_SAIDA
if("DT_SAIDA" %in% variables_names){
data <- data %>%
dplyr::mutate(DT_SAIDA = as.Date(.data$DT_SAIDA, format = "%Y%m%d"))
}
# COBRANCA (motivo de saÃda/permanência, portaria SAS 719)
if("COBRANCA" %in% variables_names){
data <- data %>%
dplyr::mutate(COBRANCA = as.character(.data$COBRANCA)) %>%
dplyr::mutate(COBRANCA = dplyr::case_match(
.data$COBRANCA,
"11" ~ "Alta curado",
"12" ~ "Alta melhorado",
"14" ~ "Alta a pedido",
"15" ~ "Alta com previs\u00e3o de retorno p/acomp do paciente",
"16" ~ "Alta por evas\u00e3o",
"18" ~ "Alta por outros motivos",
"19" ~ "Alta de paciente agudo em psiquiatria",
"21" ~ "Perman\u00eancia por caracter\u00edsticas pr\u00f3prias da doen\u00e7a",
"22" ~ "Perman\u00eancia por intercorr\u00eancia",
"23" ~ "Perman\u00eancia por impossibilidade s\u00f3cio-familiar",
"24" ~ "Perman\u00eancia proc doa\u00e7\u00e3o \u00f3rg, tec, c\u00e9l-doador vivo",
"25" ~ "Perman\u00eancia proc doa\u00e7\u00e3o \u00f3rg, tec, c\u00e9l-doador morto",
"26" ~ "Perman\u00eancia por mudan\u00e7a de procedimento",
"27" ~ "Perman\u00eancia por reopera\u00e7\u00e3o",
"28" ~ "Perman\u00eancia por outros motivos",
"29" ~ "Transfer\u00eancia para interna\u00e7\u00e3o domiciliar",
"32" ~ "Transfer\u00eancia para interna\u00e7\u00e3o domiciliar",
"31" ~ "Transfer\u00eancia para outro estabelecimento",
"41" ~ "\u00d3bito com DO fornecida pelo m\u00e9dico assistente",
"42" ~ "\u00d3bito com DO fornecida pelo IML",
"43" ~ "\u00d3bito com DO fornecida pelo SVO",
"51" ~ "Encerramento administrativo",
"61" ~ "Alta da m\u00e3e/pu\u00e9rpera e do rec\u00e9m-nascido",
"17" ~ "Alta da m\u00e3e/pu\u00e9rpera e do rec\u00e9m-nascido",
"62" ~ "Alta da m\u00e3e/pu\u00e9rpera e perman\u00eancia rec\u00e9m-nascido",
"13" ~ "Alta da m\u00e3e/pu\u00e9rpera e perman\u00eancia rec\u00e9m-nascido",
"63" ~ "Alta da m\u00e3e/pu\u00e9rpera e \u00f3bito do rec\u00e9m-nascido",
"64" ~ "Alta da m\u00e3e/pu\u00e9rpera com \u00f3bito fetal",
"65" ~ "\u00d3bito da gestante e do concepto",
"66" ~ "\u00d3bito da m\u00e3e/pu\u00e9rpera e alta do rec\u00e9m-nascido",
"67" ~ "\u00d3bito da m\u00e3e/pu\u00e9rpera e perman\u00eancia rec\u00e9m-nascido",
.default = .data$COBRANCA
)) %>%
dplyr::mutate(COBRANCA = as.factor(.data$COBRANCA))
}
# NATUREZA
if("NATUREZA" %in% variables_names){
data <- data %>%
dplyr::mutate(NATUREZA = as.character(.data$NATUREZA)) %>%
dplyr::mutate(NATUREZA = dplyr::case_match(
.data$NATUREZA,
"0" ~ NA,
"99" ~ NA,
"10" ~ "Pr\u00f3prio",
"20" ~ "Contratado",
"22" ~ "Contratado optante SIMPLES",
"30" ~ "Federal",
"31" ~ "Federal Verba Pr\u00f3pria",
"40" ~ "Estadual",
"41" ~ "Estadual Verba Pr\u00f3pria",
"50" ~ "Municipal",
"60" ~ "Filantr\u00f3pico",
"61" ~ "Filantr\u00f3pico isento tributos e contr.sociais",
"63" ~ "Filantr\u00f3pico isento IR e contr.s/lucro l\u00edquido",
"70" ~ "Universit\u00e1rio Ensino",
"80" ~ "Sindicato",
"90" ~ "Universit\u00e1rio Pesquisas",
"91" ~ "Univ. Pesquisas isento tributos e contr.sociais",
"93" ~ "Univ. Pesquisas isento IR e contr.s/lucro l\u00edquido",
"94" ~ "Universit\u00e1rio de ensino e pesquisa privado",
"92" ~ "Universit\u00e1rio de ensino e pesquisa privado",
.default = .data$NATUREZA
)) %>%
dplyr::mutate(NATUREZA = as.factor(.data$NATUREZA))
}
# NAT_JUR
if("NAT_JUR" %in% variables_names){
data <- data %>%
dplyr::mutate(NAT_JUR = as.character(.data$NAT_JUR)) %>%
dplyr::mutate(NAT_JUR = dplyr::case_match(
.data$NAT_JUR,
"1015" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Executivo Federal",
"1023" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Exec Estadual ou Distr Fed",
"1031" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Executivo Municipal",
"1040" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Legislativo Federal",
"1058" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Legisl Estadual ou Dist Fed",
"1066" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Legislativo Municipal",
"1074" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Judici\u00e1rio Federal",
"1082" ~ "\u00d3rg\u00e3o P\u00fablico do Poder Judici\u00e1rio Estadual",
"1104" ~ "Autarquia Federal",
"1112" ~ "Autarquia Estadual ou do Distrito Federal",
"1120" ~ "Autarquia Municipal",
"1139" ~ "Funda\u00e7\u00e3o Federal",
"1147" ~ "Funda\u00e7\u00e3o Estadual ou do Distrito Federal",
"1155" ~ "Funda\u00e7\u00e3o Municipal",
"1163" ~ "\u00d3rg\u00e3o P\u00fablico Aut\u00f4nomo Federal",
"1171" ~ "\u00d3rg\u00e3o P\u00fablico Aut\u00f4nomo Estadual ou Distr Federal",
"1180" ~ "\u00d3rg\u00e3o P\u00fablico Aut\u00f4nomo Estadual ou Distr Federal",
"1198" ~ "Comiss\u00e3o Polinacional",
"1201" ~ "Fundo P\u00fablico",
"1210" ~ "Associa\u00e7\u00e3o P\u00fablica",
"2011" ~ "Empresa P\u00fablica",
"2038" ~ "Sociedade de Economia Mista",
"2046" ~ "Sociedade An\u00f4nima Aberta",
"2054" ~ "Sociedade An\u00f4nima Fechada",
"2062" ~ "Sociedade Empres\u00e1ria Limitada",
"2070" ~ "Sociedade Empres\u00e1ria em Nome Coletivo",
"2089" ~ "Sociedade Empres\u00e1ria em Comandita Simples",
"2097" ~ "Sociedade Empres\u00e1ria em Comandita por A\u00e7\u00f5es",
"2127" ~ "Sociedade em Conta de Participa\u00e7\u00e3o",
"2135" ~ "Empres\u00e1rio (Individual)",
"2143" ~ "Cooperativa",
"2151" ~ "Cons\u00f3rcio de Sociedades",
"2160" ~ "Grupo de Sociedades",
"2178" ~ "Estabelecimento no Brasil de Sociedade Estrangeira",
"2194" ~ "Estab no Brasil Empr Binacional Argentina-Brasil",
"2216" ~ "Empresa Domiciliada no Exterior",
"2224" ~ "Clube/Fundo de Investimento",
"2232" ~ "Sociedade Simples Pura",
"2240" ~ "Sociedade Simples Limitada",
"2259" ~ "Sociedade Simples em Nome Coletivo",
"2267" ~ "Sociedade Simples em Comandita Simples",
"2275" ~ "Empresa Binacional",
"2283" ~ "Cons\u00f3rcio de Empregadores",
"2291" ~ "Cons\u00f3rcio Simples",
"2305" ~ "Empr Individ Responsab Limitada (Natur Empres\u00e1ria)",
"2313" ~ "Empr Individ Responsab Limitada (Natureza Simples)",
"3034" ~ "Servi\u00e7o Notarial e Registral (Cart\u00f3rio)",
"3069" ~ "Funda\u00e7\u00e3o Privada",
"3077" ~ "Servi\u00e7o Social Aut\u00f4nomo",
"3085" ~ "Condom\u00ednio Edil\u00edcio",
"3107" ~ "Comiss\u00e3o de Concilia\u00e7\u00e3o Pr\u00e9via",
"3115" ~ "Entidade de Media\u00e7\u00e3o e Arbitragem",
"3123" ~ "Partido Pol\u00edtico",
"3131" ~ "Entidade Sindical",
"3204" ~ "Estab no Brasil de Funda\u00e7\u00e3o ou Associa\u00e7\u00e3o Estrang",
"3212" ~ "Funda\u00e7\u00e3o ou Associa\u00e7\u00e3o Domiciliada no Exterior",
"3220" ~ "Organiza\u00e7\u00e3o Religiosa",
"3239" ~ "Comunidade Ind\u00edgena",
"3247" ~ "Fundo Privado",
"3999" ~ "Associa\u00e7\u00e3o Privada",
"4014" ~ "Empresa Individual Imobili\u00e1ria",
"4022" ~ "Segurado Especial",
"4081" ~ "Contribuinte Individual",
"4090" ~ "Candidato a Cargo Pol\u00edtico Eletivo",
"4111" ~ "Leiloeiro",
"5010" ~ "Organiza\u00e7\u00e3o Internacional",
"5029" ~ "Representa\u00e7\u00e3o Diplom\u00e1tica Estrangeira",
"5037" ~ "Outras Institui\u00e7\u00f5es Extraterritoriais",
"0" ~ NA,
.default = .data$NAT_JUR
)) %>%
dplyr::mutate(NAT_JUR = as.factor(.data$NAT_JUR))
}
# GESTAO
if("GESTAO" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTAO = as.character(.data$GESTAO)) %>%
dplyr::mutate(GESTAO = dplyr::case_match(
.data$GESTAO,
"0" ~ "Estadual",
"2" ~ "Estadual plena",
"1" ~ "Municipal plena assist",
"3" ~ NA,
"9" ~ NA,
.default = .data$GESTAO
)) %>%
dplyr::mutate(GESTAO = as.factor(.data$GESTAO))
}
# RUBRICA
if("RUBRICA" %in% variables_names){
data <- data %>%
dplyr::mutate(RUBRICA = as.numeric(.data$RUBRICA))
}
# IND_VDRL
if("IND_VDRL" %in% variables_names){
data <- data %>%
dplyr::mutate(IND_VDRL = as.character(.data$IND_VDRL)) %>%
dplyr::mutate(IND_VDRL = dplyr::case_match(
.data$IND_VDRL,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$IND_VDRL
)) %>%
dplyr::mutate(IND_VDRL = as.factor(.data$IND_VDRL))
}
# MUNIC_MOV
if("MUNIC_MOV" %in% variables_names){
data$MUNIC_MOV <- as.numeric(data$MUNIC_MOV)
}
# COD_IDADE
if("COD_IDADE" %in% variables_names){
data <- data %>%
dplyr::mutate(COD_IDADE = as.character(.data$COD_IDADE)) %>%
dplyr::mutate(COD_IDADE = dplyr::case_match(
.data$COD_IDADE,
"0" ~ NA,
"2" ~ "Dias",
"3" ~ "Meses",
"4" ~ "Anos",
"5" ~ "Centena de anos (100 + idade)",
.default = .data$COD_IDADE
)) %>%
dplyr::mutate(COD_IDADE = as.factor(.data$COD_IDADE))
}
# IDADE
if("IDADE" %in% variables_names){
data <- data %>%
dplyr::mutate(IDADE = as.numeric(.data$IDADE))
}
# DIAS_PERM
if("DIAS_PERM" %in% variables_names){
data <- data %>%
dplyr::mutate(DIAS_PERM = as.numeric(.data$DIAS_PERM))
}
# MORTE
if("MORTE" %in% variables_names){
data <- data %>%
dplyr::mutate(MORTE = as.character(.data$MORTE)) %>%
dplyr::mutate(MORTE = dplyr::case_match(
.data$MORTE,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$MORTE
)) %>%
dplyr::mutate(MORTE = as.factor(.data$MORTE))
}
# NACIONAL
if("NACIONAL" %in% variables_names){
data <- data %>%
dplyr::mutate(NACIONAL = as.character(.data$NACIONAL)) %>%
dplyr::mutate(NACIONAL = dplyr::case_match(
.data$NACIONAL,
"170" ~ "Abissinia",
"171" ~ "Acores",
"172" ~ "Afar frances",
"241" ~ "Afeganistao",
"93" ~ "Albania",
"30" ~ "Alemanha",
"174" ~ "Alto volta",
"94" ~ "Andorra",
"175" ~ "Angola",
"334" ~ "Antartica francesa",
"337" ~ "Antartico argentino",
"333" ~ "Antartico britanico, territorio",
"336" ~ "Antartico chileno",
"338" ~ "Antartico noruegues",
"28" ~ "Antigua e. dep. barbuda",
"29" ~ "Antilhas holandesas",
"339" ~ "Apatrida",
"242" ~ "Arabia saudita",
"176" ~ "Argelia",
"21" ~ "Argentina",
"347" ~ "Armenia",
"289" ~ "Arquipelago de bismark",
"175" ~ "Angola",
"285" ~ "Arquipelago manahiki",
"286" ~ "Arquipelago midway",
"33" ~ "Aruba",
"175" ~ "Angola",
"198" ~ "Ascensao e tristao da cunha,is",
"287" ~ "Ashmore e cartier",
"288" ~ "Australia",
"95" ~ "Austria",
"138" ~ "Azerbaijao",
"243" ~ "Bahrein",
"342" ~ "Bangladesh",
"44" ~ "Barbados",
"139" ~ "Bashkista",
"177" ~ "Bechuanalandia",
"31" ~ "Belgica",
"46" ~ "Belize",
"178" ~ "Benin",
"83" ~ "Bermudas",
"246" ~ "Bhutan",
"244" ~ "Birmania",
"22" ~ "Bolivia",
"134" ~ "Bosnia herzegovina",
"179" ~ "Botsuana",
"10" ~ "Brasil",
"245" ~ "Brunei",
"96" ~ "Bulgaria",
"238" ~ "Burkina fasso",
"180" ~ "Burundi",
"141" ~ "Buryat",
"343" ~ "Cabo verde",
"181" ~ "Camaroes",
"34" ~ "Canada",
"142" ~ "Carelia",
"247" ~ "Catar",
"143" ~ "Cazaquistao",
"248" ~ "Ceilao",
"182" ~ "Ceuta e melilla",
"183" ~ "Chade",
"144" ~ "Chechen ingusth",
"23" ~ "Chile",
"42" ~ "China",
"249" ~ "China (taiwan)",
"97" ~ "Chipre",
"145" ~ "Chuvash",
"275" ~ "Cingapura",
"26" ~ "Colombia",
"40" ~ "Comunidade das bahamas",
"54" ~ "Comunidade dominicana",
"185" ~ "Congo",
"43" ~ "Coreia",
"186" ~ "Costa do marfim",
"51" ~ "Costa rica",
"250" ~ "Coveite",
"130" ~ "Croacia",
"52" ~ "Cuba",
"53" ~ "Curacao",
"146" ~ "Dagesta",
"187" ~ "Daome",
"340" ~ "Dependencia de ross",
"98" ~ "Dinamarca",
"188" ~ "Djibuti",
"99" ~ "Eire",
"251" ~ "Emirados arabes unidos",
"27" ~ "Equador",
"100" ~ "Escocia",
"136" ~ "Eslovaquia",
"132" ~ "Eslovenia",
"35" ~ "Espanha",
"129" ~ "Estado da cidade do vaticano",
"57" ~ "Estados assoc. das antilhas",
"36" ~ "Estados unidos da america (eua)",
"147" ~ "Estonia",
"190" ~ "Etiopia",
"252" ~ "Filipinas",
"102" ~ "Finlandia",
"37" ~ "Franca",
"192" ~ "Gambia",
"193" ~ "Gana",
"194" ~ "Gaza",
"148" ~ "Georgia",
"103" ~ "Gibraltar",
"149" ~ "Gorno altai",
"32" ~ "Gra-bretanha",
"59" ~ "Granada",
"104" ~ "Grecia",
"84" ~ "Groenlandia",
"292" ~ "Guam",
"61" ~ "Guatemala",
"87" ~ "Guiana francesa",
"195" ~ "Guine",
"344" ~ "Guine bissau",
"196" ~ "Guine equatorial",
"105" ~ "Holanda",
"64" ~ "Honduras",
"63" ~ "Honduras britanicas",
"253" ~ "Hong-kong",
"106" ~ "Hungria",
"254" ~ "Iemen",
"345" ~ "Iemen do sul",
"197" ~ "Ifni",
"300" ~ "Ilha johnston e sand",
"69" ~ "Ilha milhos",
"293" ~ "Ilhas baker",
"107" ~ "Ilhas baleares",
"199" ~ "Ilhas canarias",
"294" ~ "Ilhas cantao e enderburg",
"295" ~ "Ilhas carolinas",
"297" ~ "Ilhas christmas",
"184" ~ "Ilhas comores",
"290" ~ "Ilhas cook",
"108" ~ "Ilhas cosmoledo (lomores)",
"117" ~ "Ilhas de man",
"109" ~ "Ilhas do canal",
"296" ~ "Ilhas do pacifico",
"58" ~ "Ilhas falklands",
"101" ~ "Ilhas faroes",
"298" ~ "Ilhas gilbert",
"60" ~ "Ilhas guadalupe",
"299" ~ "Ilhas howland e jarvis",
"301" ~ "Ilhas kingman reef",
"305" ~ "Ilhas macdonal e heard",
"302" ~ "Ilhas macquaire",
"67" ~ "Ilhas malvinas",
"303" ~ "Ilhas marianas",
"304" ~ "Ilhas marshall",
"306" ~ "Ilhas niue",
"307" ~ "Ilhas norfolk",
"315" ~ "Ilhas nova caledonia",
"318" ~ "Ilhas novas hebridas",
"308" ~ "Ilhas palau",
"320" ~ "Ilhas pascoa",
"321" ~ "Ilhas pitcairin",
"309" ~ "Ilhas salomao",
"326" ~ "Ilhas santa cruz",
"65" ~ "Ilhas serranas",
"310" ~ "Ilhas tokelau",
"80" ~ "Ilhas turca",
"47" ~ "Ilhas turks e caicos",
"82" ~ "Ilhas virgens americanas",
"81" ~ "Ilhas virgens britanicas",
"311" ~ "Ilhas wake",
"332" ~ "Ilhas wallis e futuna",
"255" ~ "India",
"256" ~ "Indonesia",
"110" ~ "Inglaterra",
"257" ~ "Ira",
"258" ~ "Iraque",
"112" ~ "Irlanda",
"111" ~ "Irlanda do norte",
"113" ~ "Islandia",
"259" ~ "Israel",
"39" ~ "Italia",
"114" ~ "Iugoslavia",
"66" ~ "Jamaica",
"41" ~ "Japao",
"260" ~ "Jordania",
"150" ~ "Kabardino balkar",
"312" ~ "Kalimatan",
"151" ~ "Kalmir",
"346" ~ "Kara kalpak",
"152" ~ "Karachaevocherkess",
"153" ~ "Khakass",
"261" ~ "Kmer/camboja",
"154" ~ "Komi",
"262" ~ "Kuwait",
"263" ~ "Laos",
"200" ~ "Lesoto",
"155" ~ "Letonia",
"264" ~ "Libano",
"201" ~ "Liberia",
"202" ~ "Libia",
"115" ~ "Liechtenstein",
"156" ~ "Lituania",
"116" ~ "Luxemburgo",
"265" ~ "Macau",
"205" ~ "Madagascar",
"203" ~ "Madeira",
"266" ~ "Malasia",
"204" ~ "Malawi",
"267" ~ "Maldivas,is",
"206" ~ "Mali",
"157" ~ "Mari",
"207" ~ "Marrocos",
"68" ~ "Martinica",
"268" ~ "Mascate",
"208" ~ "Mauricio",
"209" ~ "Mauritania",
"85" ~ "Mexico",
"284" ~ "Mianma",
"210" ~ "Mocambique",
"158" ~ "Moldavia",
"118" ~ "Monaco",
"269" ~ "Mongolia",
"70" ~ "Monte serrat",
"137" ~ "Montenegro",
"240" ~ "Namibia",
"314" ~ "Nauru",
"270" ~ "Nepal",
"211" ~ "Nguane",
"71" ~ "Nicaragua",
"213" ~ "Nigeria",
"119" ~ "Noruega",
"316" ~ "Nova guine",
"317" ~ "Nova zelandia",
"271" ~ "Oman",
"159" ~ "Ossetia setentrional",
"121" ~ "Pais de gales",
"122" ~ "Paises baixos",
"272" ~ "Palestina",
"72" ~ "Panama",
"73" ~ "Panama(zona do canal)",
"214" ~ "Papua nova guine",
"273" ~ "Paquistao",
"24" ~ "Paraguai",
"89" ~ "Peru",
"322" ~ "Polinesia francesa",
"123" ~ "Polonia",
"74" ~ "Porto rico",
"45" ~ "Portugal",
"215" ~ "Pracas norte africanas",
"216" ~ "Protetor do sudoeste africano",
"217" ~ "Quenia",
"160" ~ "Quirguistao",
"75" ~ "Quitasueno",
"189" ~ "Republica arabe do egito",
"218" ~ "Republica centro africana",
"173" ~ "Republica da africa do sul",
"140" ~ "Republica da bielorrussia",
"133" ~ "Republica da macedonia",
"56" ~ "Republica de el salvador",
"291" ~ "Republica de fiji",
"120" ~ "Republica de malta",
"191" ~ "Republica do gabao",
"62" ~ "Republica do haiti",
"212" ~ "Republica do niger",
"55" ~ "Republica dominicana",
"88" ~ "Republica guiana",
"135" ~ "Republica tcheca",
"20" ~ "Reservado",
"48" ~ "Reservado",
"49" ~ "Reservado",
"50" ~ "Reservado",
"219" ~ "Reuniao",
"220" ~ "Rodesia (zimbabwe)",
"124" ~ "Romenia",
"76" ~ "Roncador",
"221" ~ "Ruanda",
"274" ~ "Ruiquiu,is",
"348" ~ "Russia",
"222" ~ "Saara espanhol",
"323" ~ "Sabah",
"324" ~ "Samoa americana",
"325" ~ "Samoa ocidental",
"125" ~ "San marino",
"223" ~ "Santa helena",
"77" ~ "Santa lucia",
"78" ~ "Sao cristovao",
"224" ~ "Sao tome e principe",
"79" ~ "Sao vicente",
"327" ~ "Sarawak",
"349" ~ "Senegal",
"276" ~ "Sequin",
"226" ~ "Serra leoa",
"131" ~ "Servia",
"225" ~ "Seychelles",
"277" ~ "Siria",
"227" ~ "Somalia, republica",
"278" ~ "Sri-lanka",
"86" ~ "St. pierre et miquelon",
"228" ~ "Suazilandia",
"229" ~ "Sudao",
"126" ~ "Suecia",
"38" ~ "Suica",
"90" ~ "Suriname",
"127" ~ "Svalbard e jan mayer,is",
"161" ~ "Tadjiquistao",
"279" ~ "Tailandia",
"230" ~ "Tanganica",
"350" ~ "Tanzania",
"162" ~ "Tartaria",
"128" ~ "Tchecoslovaquia",
"335" ~ "Terr. antartico da australia",
"341" ~ "Terras austrais",
"231" ~ "Territ. britanico do oceano indico",
"328" ~ "Territorio de cocos",
"319" ~ "Territorio de papua",
"329" ~ "Timor",
"233" ~ "Togo",
"330" ~ "Tonga",
"232" ~ "Transkei",
"280" ~ "Tregua, estado",
"91" ~ "Trinidad e tobago",
"234" ~ "Tunisia",
"163" ~ "Turcomenistao",
"281" ~ "Turquia",
"331" ~ "Tuvalu",
"164" ~ "Tuvin",
"165" ~ "Ucrania",
"166" ~ "Udmurt",
"235" ~ "Uganda",
"167" ~ "Uniao sovietica",
"25" ~ "Uruguai",
"168" ~ "Uzbequistao",
"92" ~ "Venezuela",
"282" ~ "Vietna do norte",
"283" ~ "Vietna do sul",
"169" ~ "Yakut",
"236" ~ "Zaire",
"237" ~ "Zambia",
"239" ~ "Zimbabwe",
.default = .data$NACIONAL
)) %>%
dplyr::mutate(NACIONAL = as.factor(.data$NACIONAL))
}
# NUM_PROC
if("NUM_PROC" %in% variables_names){
data <- data %>%
dplyr::mutate(NUM_PROC = as.numeric(.data$NUM_PROC))
}
# CAR_INT
if("CAR_INT" %in% variables_names){
data <- data %>%
dplyr::mutate(CAR_INT = as.character(.data$CAR_INT)) %>%
dplyr::mutate(CAR_INT = dplyr::case_match(
.data$CAR_INT,
"1" ~ "Eletivo",
"2" ~ "Urg\u00eancia",
"3" ~ "Acidente no local trabalho ou a serv da empresa",
"4" ~ "Acidente no trajeto para o trabalho",
"5" ~ "Outros tipo de acidente de tr\u00e2nsito",
"6" ~ "Out tp les\u00f5es e envenen por agent qu\u00edm f\u00edsicos",
.default = .data$CAR_INT
)) %>%
dplyr::mutate(CAR_INT = as.factor(.data$CAR_INT))
}
# TOT_PT_SP
if("TOT_PT_SP" %in% variables_names){
data <- data %>%
dplyr::mutate(TOT_PT_SP = as.numeric(.data$TOT_PT_SP))
}
# CPF_AUT
if("CPF_AUT" %in% variables_names){
data <- data %>%
dplyr::mutate(CPF_AUT = as.numeric(.data$CPF_AUT))
}
# HOMONIMO
if("HOMONIMO" %in% variables_names){
data <- data %>%
dplyr::mutate(HOMONIMO = as.character(.data$HOMONIMO)) %>%
dplyr::mutate(HOMONIMO = dplyr::case_match(
.data$HOMONIMO,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$HOMONIMO
)) %>%
dplyr::mutate(HOMONIMO = as.factor(.data$HOMONIMO))
}
# NUM_FILHOS
if("NUM_FILHOS" %in% variables_names){
data <- data %>%
dplyr::mutate(NUM_FILHOS = as.numeric(.data$NUM_FILHOS))
}
# INSTRU
if("INSTRU" %in% variables_names){
data <- data %>%
dplyr::mutate(INSTRU = as.character(.data$INSTRU)) %>%
dplyr::mutate(INSTRU = dplyr::case_match(
.data$INSTRU,
"1" ~ "Analfabeto",
"2" ~ "1\u00ba grau",
"3" ~ "2\u00ba grau",
"4" ~ "3\u00ba grau",
"0" ~ NA,
"9" ~ NA,
.default = .data$INSTRU
)) %>%
dplyr::mutate(INSTRU = as.factor(.data$INSTRU))
}
# CONTRACEP1
if("CONTRACEP1" %in% variables_names){
data <- data %>%
dplyr::mutate(CONTRACEP1 = as.character(.data$CONTRACEP1)) %>%
dplyr::mutate(CONTRACEP1 = dplyr::case_match(
.data$CONTRACEP1,
"1" ~ "LAM",
"2" ~ "Ogino Kaus",
"3" ~ "Temperatura basal",
"4" ~ "Billings",
"5" ~ "Cinto t\u00e9rmico",
"6" ~ "DIU",
"7" ~ "Diafragma",
"8" ~ "Preservativo",
"9" ~ "Espermicida",
"10" ~ "Horm\u00f4nio oral",
"11" ~ "Horm\u00f4nio injet\u00e1vel",
"12" ~ "Coito interrompido",
"0" ~ NA,
"99" ~ NA,
.default = .data$CONTRACEP1
)) %>%
dplyr::mutate(CONTRACEP1 = as.factor(.data$CONTRACEP1))
}
# CONTRACEP2
if("CONTRACEP2" %in% variables_names){
data <- data %>%
dplyr::mutate(CONTRACEP2 = as.character(.data$CONTRACEP2)) %>%
dplyr::mutate(CONTRACEP2 = dplyr::case_match(
.data$CONTRACEP2,
"1" ~ "LAM",
"2" ~ "Ogino Kaus",
"3" ~ "Temperatura basal",
"4" ~ "Billings",
"5" ~ "Cinto t\u00e9rmico",
"6" ~ "DIU",
"7" ~ "Diafragma",
"8" ~ "Preservativo",
"9" ~ "Espermicida",
"10" ~ "Horm\u00f4nio oral",
"11" ~ "Horm\u00f4nio injet\u00e1vel",
"12" ~ "Coito interrompido",
"0" ~ NA,
"99" ~ NA,
.default = .data$CONTRACEP2
)) %>%
dplyr::mutate(CONTRACEP2 = as.factor(.data$CONTRACEP2))
}
# GESTRISCO
if("GESTRISCO" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTRISCO = as.character(.data$GESTRISCO)) %>%
dplyr::mutate(GESTRISCO = dplyr::case_match(
.data$GESTRISCO,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$GESTRISCO
)) %>%
dplyr::mutate(GESTRISCO = as.factor(.data$GESTRISCO))
}
# SEQ_AIH5
if("SEQ_AIH5" %in% variables_names){
data <- data %>%
dplyr::mutate(SEQ_AIH5 = as.character(.data$SEQ_AIH5)) %>%
dplyr::mutate(SEQ_AIH5 = dplyr::case_match(
.data$SEQ_AIH5,
"0" ~ "Sequencial zerado",
"1" ~ "Seq 1",
"2" ~ "Seq 2",
"3" ~ "Seq 3",
"4" ~ NA,
"999" ~ NA,
.default = .data$SEQ_AIH5
)) %>%
dplyr::mutate(SEQ_AIH5 = as.factor(.data$SEQ_AIH5))
}
# CBOR
if ("CBOR" %in% variables_names) {
colnames(tabCBO)[1] <- "CBOR"
data$CBOR <- factor(dplyr::left_join(data, tabCBO, by = "CBOR")$nome)
}
# VINCPREV
if("VINCPREV" %in% variables_names){
data <- data %>%
dplyr::mutate(VINCPREV = as.character(.data$VINCPREV)) %>%
dplyr::mutate(VINCPREV = dplyr::case_match(
.data$VINCPREV,
"1" ~ "Aut\u00f4nomo",
"2" ~ "Desempregado",
"3" ~ "Aposentado",
"4" ~ "N\u00e3o segurado",
"5" ~ "Empregado",
"6" ~ "Empregador",
"0" ~ NA,
"9" ~ NA,
.default = .data$VINCPREV
)) %>%
dplyr::mutate(VINCPREV = as.factor(.data$VINCPREV))
}
# GESTOR_COD
if("GESTOR_COD" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_COD = as.character(.data$GESTOR_COD)) %>%
dplyr::mutate(GESTOR_COD = dplyr::case_match(
.data$GESTOR_COD,
"1" ~ "TEMPO DE PERMANENCIA",
"2" ~ "IDADE MENOR",
"3" ~ "IDADE MAIOR",
"4" ~ "TEMPO DE PERMANENCIA E IDADE",
"5" ~ "QUANTIDADE MAXIMA",
"7" ~ "PERM.MENOR",
"8" ~ "ID.MENOR",
"9" ~ "ID.MENOR E PERM.MENOR",
"10" ~ "ID.MAIOR",
"11" ~ "ID.MAIOR E PERM.MENOR",
"14" ~ "QTD",
"15" ~ "QTD E PERM.MENOR",
"16" ~ "QTD E ID.MENOR",
"17" ~ "QTD E ID.MENOR E PERM.MENOR",
"18" ~ "QTD E ID.MAIOR",
"19" ~ "QTD E ID.MAIOR E PERM.MENOR",
"38" ~ "CBO",
"39" ~ "CBO E PERM.MENOR",
"40" ~ "CBO E ID.MENOR",
"41" ~ "CBO E ID.MENOR E PERM.MENOR",
"42" ~ "CBO E ID.MAIOR",
"43" ~ "CBO E ID.MAIOR E PERM.MENOR",
"46" ~ "CBO E QTD",
"47" ~ "CBO E QTD E PERM.MENOR",
"48" ~ "CBO E QTD E ID.MENOR",
"49" ~ "CBO E QTD E ID.MENOR E PERM.MENOR",
"50" ~ "CBO E QTD E ID.MAIOR",
"51" ~ "CBO E QTD E ID.MAIOR E PERM.MENOR",
"70" ~ "TELEFONE",
"71" ~ "TELEFONE E PERM.MENOR",
"72" ~ "TELEFONE E ID.MENOR",
"73" ~ "TELEFONE E ID.MENOR E PERM.MENOR",
"74" ~ "TELEFONE E ID.MAIOR",
"75" ~ "TELEFONE E ID.MAIOR E PERM.MENOR",
"78" ~ "TELEFONE E QTD",
"79" ~ "TELEFONE E QTD E PERM.MENOR",
"80" ~ "TELEFONE E QTD E ID.MENOR",
"81" ~ "TELEFONE E QTD E ID.MENOR E PERM.MENOR",
"82" ~ "TELEFONE E QTD E ID.MAIOR",
"83" ~ "TELEFONE E QTD E ID.MAIOR E PERM.MENOR",
"102" ~ "TELEFONE E CBO",
"103" ~ "TELEFONE E CBO E PERM.MENOR",
"104" ~ "TELEFONE E CBO E ID.MENOR",
"105" ~ "TELEFONE E CBO E ID.MENOR E PERM.MENOR",
"106" ~ "TELEFONE E CBO E ID.MAIOR",
"107" ~ "TELEFONE E CBO E ID.MAIOR E PERM.MENOR",
"110" ~ "TELEFONE E CBO E QTD",
"111" ~ "TELEFONE E CBO E QTD E PERM.MENOR",
"112" ~ "TELEFONE E CBO E QTD E ID.MENOR",
"113" ~ "TELEFONE E CBO E QTD E ID.MENOR E PERM.MENOR",
"114" ~ "TELEFONE E CBO E QTD E ID.MAIOR",
"115" ~ "TELEFONE E CBO E QTD E ID.MAIOR E PERM.MENOR",
"134" ~ "CNS",
"136" ~ "CNS E ID. MENOR",
"137" ~ "CNS E ID. MENOR E PERM. MENOR",
"138" ~ "CNS E ID. MAIOR",
"139" ~ "CNS E ID. MAIOR E PERM. MENOR",
"142" ~ "CNS E QTD",
"143" ~ "CNS E QTD E PERM. MENOR",
"144" ~ "CNS E QTD E ID. MENOR",
"145" ~ "CNS E QTD E ID. MENOR E PERM. MENOR",
"146" ~ "CNS E QTD E ID. MAIOR",
"147" ~ "CNS E QTD E ID. MAIOR E PERM. MENOR",
"166" ~ "CNS E CBO",
"167" ~ "CNS E CBO E PERM. MENOR",
"168" ~ "CNS E CBO E ID. MENOR",
"169" ~ "CNS E CBO E ID. MENOR E PERM. MENOR",
"170" ~ "CNS E CBO E ID. MAIOR",
"171" ~ "CNS E CBO E ID. MAIOR E PERM. MENOR",
"174" ~ "CNS E CBO E QTD",
"175" ~ "CNS E CBO E QTD E PERM. MENOR",
"176" ~ "CNS E CBO E QTD E ID. MENOR",
"177" ~ "CNS E CBO E QTD E ID. MENOR E PERM. MENOR",
"178" ~ "CNS E CBO E QTD E ID. MAIOR",
"179" ~ "CNS E CBO E QTD E ID. MAIOR E PERM. MENOR",
"198" ~ "CNS E TELEFONE",
"199" ~ "CNS E TELEFONE E PERM. MENOR",
"200" ~ "CNS E TELEFONE E ID. MENOR",
"201" ~ "CNS E TELEFONE E ID. MENOR E PERM. MENOR",
"202" ~ "CNS E TELEFONE E ID. MAIOR",
"203" ~ "CNS E TELEFONE E ID. MAIOR E PERM. MENOR",
"206" ~ "CNS E TELEFONE E QTD",
"207" ~ "CNS E TELEFONE E QTD E PERM. MENOR",
"208" ~ "CNS E TELEFONE E QTD E ID. MENOR",
"209" ~ "CNS E TELEFONE E QTD E ID. MENOR E PERM. MENOR",
"210" ~ "CNS E TELEFONE E QTD E ID. MAIOR",
"211" ~ "CNS E TELEFONE E QTD E ID. MAIOR E PERM. MENOR",
"230" ~ "CNS E TELEFONE E CBO",
"231" ~ "CNS E TELEFONE E CBO E PERM. MENOR",
"232" ~ "CNS E TELEFONE E CBO E ID. MENOR",
"233" ~ "CNS E TELEFONE E CBO E ID. MENOR E PERM. MENOR",
"234" ~ "CNS E TELEFONE E CBO E ID. MAIOR",
"235" ~ "CNS E TELEFONE E CBO E ID. MAIOR E PERM. MENOR",
"238" ~ "CNS E TELEFONE E CBO E QTD",
"239" ~ "CNS E TELEFONE E CBO E QTD E PERM. MENOR",
"240" ~ "CNS E TELEFONE E CBO E QTD E ID. MENOR",
"241" ~ "CNS E TELEFONE E CBO E QTD E ID. MENOR E PERM. MENOR",
"242" ~ "CNS E TELEFONE E CBO E QTD E ID. MAIOR",
"243" ~ "CNS E TELEFONE E CBO E QTD E ID. MAIOR E PERM. MENOR",
.default = .data$GESTOR_COD
)) %>%
dplyr::mutate(GESTOR_COD = as.factor(.data$GESTOR_COD))
}
# GESTOR_TP
if("GESTOR_TP" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_TP = as.numeric(.data$GESTOR_TP))
}
# GESTOR_CPF
if("GESTOR_CPF" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_CPF = as.numeric(.data$GESTOR_CPF))
}
# GESTOR_DT
if("GESTOR_DT" %in% variables_names){
data <- data %>%
dplyr::mutate(GESTOR_DT = as.numeric(.data$GESTOR_DT))
}
# INFEHOSP
if("INFEHOSP" %in% variables_names){
data <- data %>%
dplyr::mutate(INFEHOSP = as.character(.data$INFEHOSP)) %>%
dplyr::mutate(INFEHOSP = dplyr::case_match(
.data$INFEHOSP,
"0" ~ "N\u00e3o",
"1" ~ "Sim",
.default = .data$INFEHOSP
)) %>%
dplyr::mutate(INFEHOSP = as.factor(.data$INFEHOSP))
}
# COMPLEX
if("COMPLEX" %in% variables_names){
data <- data %>%
dplyr::mutate(COMPLEX = as.character(.data$COMPLEX)) %>%
dplyr::mutate(COMPLEX = dplyr::case_match(
.data$COMPLEX,
"1" ~ "Aten\u00e7\u00e3o B\u00e1sica",
"2" ~ "M\u00e9dia complexidade",
"3" ~ "Alta complexidade",
"0" ~ NA,
"99" ~ NA,
.default = .data$COMPLEX
)) %>%
dplyr::mutate(COMPLEX = as.factor(.data$COMPLEX))
}
# FINANC
if("FINANC" %in% variables_names){
data <- data %>%
dplyr::mutate(FINANC = as.character(.data$FINANC)) %>%
dplyr::mutate(FINANC = dplyr::case_match(
.data$FINANC,
"1" ~ "Aten\u00e7\u00e3o B\u00e1sica (PAB)",
"2" ~ "Assist\u00eancia Farmac\u00eautica",
"4" ~ "Fundo de A\u00e7\u00f5es Estrat\u00e9gicas e Compensa\u00e7\u00f5es FAEC",
"5" ~ "Incentivo - MAC",
"6" ~ "M\u00e9dia e Alta Complexidade (MAC)",
"7" ~ "Vigil\u00e2ncia em Sa\u00fade",
"0" ~ NA,
"99" ~ NA,
.default = .data$FINANC
)) %>%
dplyr::mutate(FINANC = as.factor(.data$FINANC))
}
# FAEC_TP
if("FAEC_TP" %in% variables_names){
data <- data %>%
dplyr::mutate(FAEC_TP = as.character(.data$FAEC_TP)) %>%
dplyr::mutate(FAEC_TP = dplyr::case_match(
.data$FAEC_TP,
"10000" ~ "Aten\u00e7\u00e3o B\u00e1sica (PAB)",
"20000" ~ "Assist\u00eancia Farmac\u00eautica",
"40001" ~ "Coleta de material",
"40002" ~ "Diagn\u00f3stico em laborat\u00f3rio cl\u00ednico",
"40003" ~ "Coleta/exame an\u00e1tomo-patol\u00f3gico colo uterino",
"40004" ~ "Diagn\u00f3stico em neurologia",
"40005" ~ "Diagn\u00f3stico em otorrinolaringologia/fonoaudiologia",
"40006" ~ "Diagn\u00f3stico em psicologia/psiquiatria",
"40007" ~ "Consultas m\u00e9dicas/outros profissionais de n\u00edvel superior",
"40008" ~ "Aten\u00e7\u00e3o domiciliar",
"40009" ~ "Atendimento/acompanhamento em reabilita\u00e7\u00e3o f\u00edsica, mental, visual, auditiva e m\u00faltiplas defic",
"40010" ~ "Atendimento/acompanhamento psicossocial",
"40011" ~ "Atendimento/acompanhamento em sa\u00fade do idoso",
"40012" ~ "Atendimento/acompanhamento de queimados",
"40013" ~ "Atendimento/acompanhamento de diagn\u00f3stico de doen\u00e7as endocrinas/metab\u00f3licas e nutricionais",
"40014" ~ "Tratamento de doen\u00e7as do sistema nervoso central e perif\u00e9rico",
"40015" ~ "Tratamento de doen\u00e7as do aparelho da vis\u00e3o",
"40016" ~ "Tratamento em oncologia",
"40017" ~ "Nefrologia",
"40018" ~ "Tratamentos odontol\u00f3gicos",
"40019" ~ "Cirurgia do sistema nervoso central e perif\u00e9rico",
"40020" ~ "Cirurgias de ouvido, nariz e garganta",
"40021" ~ "Deformidade labio-palatal e cr\u00e2nio-facial",
"40022" ~ "Cirurgia do aparelho da vis\u00e3o",
"40023" ~ "Cirurgia do aparelho circulat\u00f3rio",
"40024" ~ "Cirurgia do aparelho digestivo, org\u00e3os anexos e parede abdominal(inclui pr\u00e9 e p\u00f3s operat\u00f3rio)",
"40025" ~ "Cirurgia do aparelho geniturin\u00e1rio",
"40026" ~ "Tratamento de queimados",
"40027" ~ "Cirurgia reparadora para lipodistrofia",
"40028" ~ "Outras cirurgias pl\u00e1sticas/reparadoras",
"40029" ~ "Cirurgia orofacial",
"40030" ~ "Sequenciais",
"40031" ~ "Cirurgias em nefrologia",
"40032" ~ "Transplantes de org\u00e3os, tecidos e c\u00e9lulas",
"40033" ~ "Medicamentos para transplante",
"40034" ~ "OPM auditivas",
"40035" ~ "OPM em odontologia",
"40036" ~ "OPM em queimados",
"40037" ~ "OPM em nefrologia",
"40038" ~ "OPM para transplantes",
"40039" ~ "Incentivos ao pr\u00e9-natal e nascimento",
"40040" ~ "Incentivo ao registro c\u00edvil de nascimento",
"40041" ~ "Central Nacional de Regula\u00e7\u00e3o de Alta Complexidade (CNRAC)",
"40042" ~ "Reguladores de Atividade hormonal - Inibidores de prolactina",
"40043" ~ "Pol\u00edtica Nacional de Cirurgias Eletivas",
"40044" ~ "Redesigna\u00e7\u00e3o e Acompanhamento",
"40045" ~ "Projeto Olhar Brasil",
"40046" ~ "Mamografia para Rastreamento",
"40047" ~ "Projeto Olhar Brasil - Consulta",
"40048" ~ "Projeto Olhar Brasil - \u00d3culos",
"40049" ~ "Implementar Cirg. CV Pedi\u00e1trica",
"40050" ~ "Cirurgias Eletivas - Componente I",
"40051" ~ "Cirurgias Eletivas - Componente II",
"40052" ~ "Cirurgias Eletivas - Componente III",
"40053" ~ "Pr\u00f3tese Mam\u00e1ria - Exames",
"40054" ~ "Pr\u00f3tese Mam\u00e1ria - Cirurgia",
"40055" ~ "Transplante - Histocompatibilidade",
"40056" ~ "Triagem Neonatal",
"40057" ~ "Controle de qualidade do exame citopatol\u00f3gico do colo de \u00fatero",
"40058" ~ "Exames do Leite Materno",
"40059" ~ "Aten\u00e7\u00e3o as Pessoas em Situa\u00e7\u00e3o de Viol\u00eancia Sexual",
"40060" ~ "Sangue e Hemoderivados",
"40061" ~ "Mamografia para rastreamento em faixa et\u00e1ria recomendada",
"40062" ~ "Doen\u00e7as Raras",
"40063" ~ "Cadeiras de Rodas",
"40064" ~ "Sistema de Frequencia Modulada Pessoal-FM",
"40065" ~ "Medicamentos em Urg\u00eancia",
"40066" ~ "Cirurgias Eletivas - Componente \u00danico",
"40067" ~ "Aten\u00e7\u00e3o Especializada em Sa\u00fade Auditiva",
"40068" ~ "Terapias Especializadas em Angiologia",
"21012" ~ "FAEC CNRAC (21012-c\u00f3d ant \u00e0 tab unif-v\u00e1l p/2008-01)",
"21014" ~ "FAEC Eletiv(21014-c\u00f3d ant \u00e0 tab unif-v\u00e1l p/2008-01)",
"50000" ~ "Incentivo - MAC",
"60000" ~ "M\u00e9dia e Alta Complexidade (MAC)",
"70000" ~ "Vigil\u00e2ncia em Sa\u00fade",
"80000" ~ "Gest\u00e3o do SUS",
.default = .data$FAEC_TP
)) %>%
dplyr::mutate(FAEC_TP = as.factor(.data$FAEC_TP))
}
# REGCT
if("REGCT" %in% variables_names){
data <- data %>%
dplyr::mutate(REGCT = as.character(.data$REGCT)) %>%
dplyr::mutate(REGCT = dplyr::case_match(
.data$REGCT,
"7100" ~ "TABELA DE NAO GERACAO DE CREDITO POR PRODUCAO NA INTERNACAO E/OU AMBULATORIO",
"7101" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA MEDIA COMPLEXIDADE AMBULATORIAL",
"7102" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA MEDIA COMPLEXIDADE HOSPITALAR",
"7103" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA ALTA COMPLEXIDADE AMBULATORIAL",
"7104" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO NA ALTA COMPLEXIDADE HOSPITALAR",
"7105" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO PARA OS PROCEDIMENTOS FINANCIADOS COM O FAEC",
"7106" ~ "ESTABELECIMENTO SEM GERA\u00c7\u00c3O DE CREDITO TOTAL - EXCLUINDO FAEC",
"7107" ~ "ESTABELECIMENTO SEM GERACAO DE CREDITO NAS ACOES ESPEC. DE ODONTOLOGIA(INCENTIVO CEO I,II E III)",
"7108" ~ "ESTABELECIMENTO SEM GERACAO DE CREDITO(INCENTIVO A SAUDE DO TRABALHADOR)",
"7109" ~ "ESTABELECIMENTO SEM GERACAO DE CREDITO TOTAL-MEC",
"7110" ~ "ESTABELECIMENTO DE SAUDE DA ESTRUTURA DO MINISTERIO DA SAUDE - SEM GERA\u00c7AO DE CREDITO TOTAL",
"7111" ~ "ESTABELECIMENTO DE SAUDE SEM GERACAO DE CREDITO - NASF, EXCETO FAEC",
"7112" ~ "ESTABELECIMENTO SEM GERA\u00c7\u00c3O DE CREDITO TOTAL - INCLUINDO FAEC - EXCLUSIVO PARA REDE SARAH",
"7113" ~ "ESTABELECIMENTO SEM GERA\u00c7\u00c3O DE CREDITO TOTAL - INCLUINDO FAEC - OUTROS ESTABELECIMENTOS FEDERAIS",
"7114" ~ "ESTABELECIMENTO DE SA\u00daDE SEM GERA\u00c7\u00c3O DE CR\u00c9DITO TOTAL, INCLUSIVE FAEC - PRONTO ATENDIMENTO",
"7115" ~ "ESTABELECIMENTO DE SA\u00daDE SEM GERA\u00c7\u00c3O DE CR\u00c9DITO NA M\u00c9DIA COMPLEXIDADE - HU/MEC",
"7116" ~ "ESTABELECIMENTO DE SA\u00daDE SEM GERA\u00c7\u00c3O DE CR\u00c9DITO NA M\u00c9DIA COMPLEXIDADE - LRPD",
"7117" ~ "Estabelecimento de Sa\u00fade sem gera\u00e7\u00e3o de cr\u00e9dito na m\u00e9dia complexidade (exceto OPM) - CER",
"0" ~ "Sem regra contratual",
.default = .data$REGCT
)) %>%
dplyr::mutate(REGCT = as.factor(.data$REGCT))
}
# RACA_COR
if("RACA_COR" %in% variables_names){
data <- data %>%
dplyr::mutate(RACA_COR = as.character(.data$RACA_COR)) %>%
dplyr::mutate(RACA_COR = dplyr::case_match(
.data$RACA_COR,
"1" ~ "Branca",
"2" ~ "Preta",
"3" ~ "Parda",
"4" ~ "Amarela",
"5" ~ "Ind\u00edgena",
"0" ~ NA,
"99" ~ NA,
.default = .data$RACA_COR
)) %>%
dplyr::mutate(RACA_COR = as.factor(.data$RACA_COR))
}
# ETNIA
if("ETNIA" %in% variables_names){
data <- data %>%
dplyr::mutate(ETNIA = as.character(.data$ETNIA)) %>%
dplyr::mutate(ETNIA = dplyr::case_match(
.data$ETNIA,
"0001" ~ "ACONA (WAKONAS, NACONAS, JAKONA, ACORANES)",
"0002" ~ "AIKANA (AIKANA, MAS SAKA,TUBARAO)",
"0003" ~ "AJURU",
"0004" ~ "AKUNSU (AKUNT'SU)",
"0005" ~ "AMANAYE",
"0006" ~ "AMONDAWA",
"0007" ~ "ANAMBE",
"0008" ~ "APARAI (APALAI)",
"0009" ~ "APIAKA (APIACA)",
"0010" ~ "APINAYE (APINAJE/APINAIE/APINAGE)",
"0011" ~ "APURINA (APORINA, IPURINA, IPURINA, IPURINAN)",
"0012" ~ "ARANA (ARACUAI DO VALE DO JEQUITINHONHA)",
"0013" ~ "ARAPASO (ARAPACO)",
"0014" ~ "ARARA DE RONDONIA (KARO, URUCU, URUKU)",
"0015" ~ "ARARA DO ACRE (SHAWANAUA, AMAWAKA)",
"0016" ~ "ARARA DO ARIPUANA (ARARA DO BEIRADAO/ARI-PUANA)",
"0017" ~ "ARARA DO PARA (UKARAGMA, UKARAMMA)",
"0018" ~ "ARAWETE (ARAUETE)",
"0019" ~ "ARIKAPU (ARICAPU, ARIKAPO, MASUBI, MAXUBI)",
"0020" ~ "ARIKEM (ARIQUEN, ARIQUEME, ARIKEME)",
"0021" ~ "ARIKOSE (ARICOBE)",
"0022" ~ "ARUA",
"0023" ~ "ARUAK (ARAWAK)",
"0024" ~ "ASHANINKA (KAMPA)",
"0025" ~ "ASURINI DO TOCANTINS (AKUAWA/AKWAWA)",
"0026" ~ "ASURINI DO XINGU (AWAETE)",
"0027" ~ "ATIKUM (ATICUM)",
"0028" ~ "AVA - CANOEIRO",
"0029" ~ "AWETI (AUETI/AUETO)",
"0030" ~ "BAKAIRI (KURA, BACAIRI)",
"0031" ~ "BANAWA YAFI (BANAWA, BANAWA-JAFI)",
"0032" ~ "BANIWA (BANIUA, BANIVA, WALIMANAI, WAKUENAI)",
"0033" ~ "BARA (WAIPINOMAKA)",
"0034" ~ "BARASANA (HANERA)",
"0035" ~ "BARE",
"0036" ~ "BORORO (BOE)",
"0037" ~ "BOTOCUDO (GEREN)",
"0038" ~ "CANOE",
"0039" ~ "CASSUPA",
"0040" ~ "CHAMACOCO",
"0041" ~ "CHIQUITANO (XIQUITANO)",
"0042" ~ "CIKIYANA (SIKIANA)",
"0043" ~ "CINTA LARGA (MATETAMAE)",
"0044" ~ "COLUMBIARA (CORUMBIARA)",
"0045" ~ "DENI",
"0046" ~ "DESANA (DESANA, DESANO, DESSANO, WIRA, UMUKOMASA)",
"0047" ~ "DIAHUI (JAHOI, JAHUI, DIARROI)",
"0048" ~ "ENAWENE-NAWE (SALUMA)",
"0049" ~ "FULNI-O",
"0050" ~ "GALIBI (GALIBI DO OIAPOQUE, KARINHA)",
"0051" ~ "GALIBI MARWORNO (GALIBI DO UACA, ARUA)",
"0052" ~ "GAVIAO DE RONDONIA (DIGUT)",
"0053" ~ "GAVIAO KRIKATEJE",
"0054" ~ "GAVIAO PARKATEJE (PARKATEJE)",
"0055" ~ "GAVIAO PUKOBIE (PUKOBIE, PYKOPJE, GAVIAO DO MARANHAO)",
"0056" ~ "GUAJA (AWA, AVA)",
"0057" ~ "GUAJAJARA (TENETEHARA)",
"0058" ~ "GUARANI KAIOWA (PAI TAVYTERA)",
"0059" ~ "GUARANI M'BYA",
"0060" ~ "GUARANI NANDEVA (AVAKATUETE, CHIRIPA,NHANDEWA, AVA GUARANI)",
"0061" ~ "GUATO",
"0062" ~ "HIMARIMA (HIMERIMA)",
"0063" ~ "INGARIKO (INGARICO, AKAWAIO, KAPON)",
"0064" ~ "IRANXE (IRANTXE)",
"0065" ~ "ISSE",
"0066" ~ "JABOTI (JABUTI, KIPIU, YABYTI)",
"0067" ~ "JAMAMADI (YAMAMADI, DJEOROMITXI)",
"0068" ~ "JARAWARA",
"0069" ~ "JIRIPANCO (JERIPANCO, GERIPANCO)",
"0070" ~ "JUMA (YUMA)",
"0071" ~ "JURUNA",
"0072" ~ "JURUTI (YURITI)",
"0073" ~ "KAAPOR (URUBU-KAAPOR, KA'APOR, KAAPORTE)",
"0074" ~ "KADIWEU (CADUVEO, CADIUEU)",
"0075" ~ "KAIABI (CAIABI, KAYABI)",
"0076" ~ "KAIMBE (CAIMBE)",
"0077" ~ "KAINGANG (CAINGANGUE)",
"0078" ~ "KAIXANA (CAIXANA)",
"0079" ~ "KALABASSA (CALABASSA, CALABACAS)",
"0080" ~ "KALANCO",
"0081" ~ "KALAPALO (CALAPALO)",
"0082" ~ "KAMAYURA (CAMAIURA, KAMAIURA)",
"0083" ~ "KAMBA (CAMBA)",
"0084" ~ "KAMBEBA (CAMBEBA, OMAGUA)",
"0085" ~ "KAMBIWA (CAMBIUA)",
"0086" ~ "KAMBIWA PIPIPA (PIPIPA)",
"0087" ~ "KAMPE",
"0088" ~ "KANAMANTI (KANAMATI, CANAMANTI)",
"0089" ~ "KANAMARI (CANAMARI, KANAMARY, TUKUNA)",
"0090" ~ "KANELA APANIEKRA (CANELA)",
"0091" ~ "KANELA RANKOKAMEKRA (CANELA)",
"0092" ~ "KANINDE",
"0093" ~ "KANOE (CANOE)",
"0094" ~ "KANTARURE (CANTARURE)",
"0095" ~ "KAPINAWA (CAPINAUA)",
"0096" ~ "KARAJA (CARAJA)",
"0097" ~ "KARAJA/JAVAE (JAVAE)",
"0098" ~ "KARAJA/XAMBIOA (KARAJA DO NORTE)",
"0099" ~ "KARAPANA (CARAPANA, MUTEAMASA, UKOPINOPONA)",
"0100" ~ "KARAPOTO (CARAPOTO)",
"0101" ~ "KARIPUNA (CARIPUNA)",
"0102" ~ "KARIPUNA DO AMAPA (CARIPUNA)",
"0103" ~ "KARIRI (CARIRI)",
"0104" ~ "KARIRI-XOCO (CARIRI-CHOCO)",
"0105" ~ "KARITIANA (CARITIANA)",
"0106" ~ "KATAWIXI (KATAUIXI,KATAWIN, KATAWISI, CATAUICHI)",
"0107" ~ "KATUENA (CATUENA, KATWENA)",
"0108" ~ "KATUKINA (PEDA DJAPA)",
"0109" ~ "KATUKINA DO ACRE",
"0110" ~ "KAXARARI (CAXARARI)",
"0111" ~ "KAXINAWA (HUNI-KUIN, CASHINAUA, CAXINAUA)",
"0112" ~ "KAXIXO",
"0113" ~ "KAXUYANA (CAXUIANA)",
"0114" ~ "KAYAPO (CAIAPO)",
"0115" ~ "KAYAPO KARARAO (KARARAO)",
"0116" ~ "KAYAPO TXUKAHAMAE (TXUKAHAMAE)",
"0117" ~ "KAYAPO XICRIM (XIKRIN)",
"0118" ~ "KAYUISANA (CAIXANA, CAUIXANA, KAIXANA)",
"0119" ~ "KINIKINAWA (GUAN, KOINUKOEN, KINIKINAO)",
"0120" ~ "KIRIRI",
"0121" ~ "KOCAMA (COCAMA, KOKAMA)",
"0122" ~ "KOKUIREGATEJE",
"0123" ~ "KORUBO",
"0124" ~ "KRAHO (CRAO, KRAO)",
"0125" ~ "KREJE (KRENYE)",
"0126" ~ "KRENAK (BORUN, CRENAQUE)",
"0127" ~ "KRIKATI (KRINKATI)",
"0128" ~ "KUBEO (CUBEO, COBEWA, KUBEWA, PAMIWA, CUBEU)",
"0129" ~ "KUIKURO (KUIKURU, CUICURO)",
"0130" ~ "KUJUBIM (KUYUBI, CUJUBIM)",
"0131" ~ "KULINA PANO (CULINA)",
"0132" ~ "KULINA/MADIHA (CULINA, MADIJA, MADIHA)",
"0133" ~ "KURIPAKO (CURIPACO, CURRIPACO, CORIPACO, WAKUENAI)",
"0134" ~ "KURUAIA (CURUAIA)",
"0135" ~ "KWAZA (COAIA, KOAIA)",
"0136" ~ "MACHINERI (MANCHINERI, MANXINERI)",
"0137" ~ "MACURAP (MAKURAP)",
"0138" ~ "MAKU DOW (DOW)",
"0139" ~ "MAKU HUPDA (HUPDA)",
"0140" ~ "MAKU NADEB (NADEB)",
"0141" ~ "MAKU YUHUPDE (YUHUPDE)",
"0142" ~ "MAKUNA (MACUNA, YEBA-MASA)",
"0143" ~ "MAKUXI (MACUXI, MACHUSI, PEMON)",
"0144" ~ "MARIMAM (MARIMA)",
"0145" ~ "MARUBO",
"0146" ~ "MATIPU",
"0147" ~ "MATIS",
"0148" ~ "MATSE (MAYORUNA)",
"0149" ~ "MAXAKALI (MAXACALI)",
"0150" ~ "MAYA (MAYA)",
"0151" ~ "MAYTAPU",
"0152" ~ "MEHINAKO (MEINAKU, MEINACU)",
"0153" ~ "MEKEN (MEQUEM, MEKHEM, MICHENS)",
"0154" ~ "MENKY (MYKY, MUNKU, MENKI, MYNKY)",
"0155" ~ "MIRANHA (MIRANHA, MIRANA)",
"0156" ~ "MIRITI TAPUIA (MIRITI-TAPUYA, BUIA-TAPUYA)",
"0157" ~ "MUNDURUKU (MUNDURUCU)",
"0158" ~ "MURA",
"0159" ~ "NAHUKWA (NAFUQUA)",
"0160" ~ "NAMBIKWARA DO CAMPO (HALOTESU, KITHAULU, WAKALITESU, SAWENTES, MANDUKA)",
"0161" ~ "NAMBIKWARA DO NORTE (NEGAROTE ,MAMAINDE, LATUNDE, SABANE E MANDUKA, TAWANDE)",
"0162" ~ "NAMBIKWARA DO SUL (WASUSU ,HAHAINTESU, ALANTESU, WAIKISU, ALAKETESU, WASUSU, SARARE)",
"0163" ~ "NARAVUTE (NARUVOTO)",
"0164" ~ "NAWA (NAUA)",
"0165" ~ "NUKINI (NUQUINI, NUKUINI)",
"0166" ~ "OFAIE (OFAYE-XAVANTE)",
"0167" ~ "ORO WIN",
"0168" ~ "PAIAKU (JENIPAPO-KANINDE)",
"0169" ~ "PAKAA NOVA (WARI, PACAAS NOVOS)",
"0170" ~ "PALIKUR (AUKWAYENE, AUKUYENE, PALIKU'ENE)",
"0171" ~ "PANARA (KRENHAKARORE , KRENAKORE, KRENA-KARORE)",
"0172" ~ "PANKARARE (PANCARARE)",
"0173" ~ "PANKARARU (PANCARARU)",
"0174" ~ "PANKARARU KALANKO (KALANKO)",
"0175" ~ "PANKARARU KARUAZU (KARUAZU)",
"0176" ~ "PANKARU (PANCARU)",
"0177" ~ "PARAKANA (PARACANA, APITEREWA, AWAETE)",
"0178" ~ "PARECI (PARESI, HALITI)",
"0179" ~ "PARINTINTIN",
"0180" ~ "PATAMONA (KAPON)",
"0181" ~ "PATAXO",
"0182" ~ "PATAXO HA-HA-HAE",
"0183" ~ "PAUMARI (PALMARI)",
"0184" ~ "PAUMELENHO",
"0185" ~ "PIRAHA (MURA PIRAHA)",
"0186" ~ "PIRATUAPUIA (PIRATAPUYA, PIRATAPUYO, PIRA-TAPUYA, WAIKANA)",
"0187" ~ "PITAGUARI",
"0188" ~ "POTIGUARA",
"0189" ~ "POYANAWA (POIANAUA)",
"0190" ~ "RIKBAKTSA (CANOEIROS, ERIGPAKTSA)",
"0191" ~ "SAKURABIAT(MEKENS, SAKIRABIAP, SAKIRABIAR)",
"0192" ~ "SATERE-MAWE (SATERE-MAUE)",
"0193" ~ "SHANENAWA (KATUKINA)",
"0194" ~ "SIRIANO (SIRIA-MASA)",
"0195" ~ "SURIANA",
"0196" ~ "SURUI DE RONDONIA (PAITER)",
"0197" ~ "SURUI DO PARA (AIKEWARA)",
"0198" ~ "SUYA (SUIA/KISEDJE)",
"0199" ~ "TAPAYUNA (BEICO-DE-PAU)",
"0200" ~ "TAPEBA",
"0201" ~ "TAPIRAPE (TAPI'IRAPE)",
"0202" ~ "TAPUIA (TAPUIA-XAVANTE, TAPUIO)",
"0203" ~ "TARIANO (TARIANA, TALIASERI)",
"0204" ~ "TAUREPANG (TAULIPANG, PEMON, AREKUNA, PAGEYN)",
"0205" ~ "TEMBE",
"0206" ~ "TENHARIM",
"0207" ~ "TERENA",
"0208" ~ "TICUNA (TIKUNA, TUKUNA, MAGUTA)",
"0209" ~ "TINGUI BOTO",
"0210" ~ "TIRIYO EWARHUYANA (TIRIYO, TRIO, TARONA, YAWI, PIANOKOTO)",
"0211" ~ "TIRIYO KAH'YANA (TIRIYO, TRIO, TARONA, YAWI, PIANOKOTO)",
"0212" ~ "TIRIYO TSIKUYANA (TIRIYO, TRIO, TARONA, YAWI, PIANOKOTO)",
"0213" ~ "TORA",
"0214" ~ "TREMEMBE",
"0215" ~ "TRUKA",
"0216" ~ "TRUMAI",
"0217" ~ "TSOHOM DJAPA (TSUNHUM-DJAPA)",
"0218" ~ "TUKANO (TUCANO, YE'PA-MASA, DASEA)",
"0219" ~ "TUMBALALA",
"0220" ~ "TUNAYANA",
"0221" ~ "TUPARI",
"0222" ~ "TUPINAMBA",
"0223" ~ "TUPINIQUIM",
"0224" ~ "TURIWARA",
"0225" ~ "TUXA",
"0226" ~ "TUYUKA (TUIUCA, DOKAPUARA, UTAPINOMAKAPHONA)",
"0227" ~ "TXIKAO (TXICAO, IKPENG)",
"0228" ~ "UMUTINA (OMOTINA, BARBADOS)",
"0229" ~ "URU-EU-WAU-WAU (URUEU-UAU-UAU, URUPAIN, URUPA)",
"0230" ~ "WAI WAI HIXKARYANA (HIXKARYANA)",
"0231" ~ "WAI WAI KARAFAWYANA (KARAFAWYANA, KARA-PAWYANA)",
"0232" ~ "WAI WAI XEREU (XEREU)",
"0233" ~ "WAI WAI KATUENA (KATUENA)",
"0234" ~ "WAI WAI MAWAYANA (MAWAYANA)",
"0235" ~ "WAIAPI (WAYAMPI, OYAMPI, WAYAPY, )",
"0236" ~ "WAIMIRI ATROARI (KINA)",
"0237" ~ "WANANO (UANANO, WANANA)",
"0238" ~ "WAPIXANA (UAPIXANA, VAPIDIANA, WAPISIANA, WAPISHANA)",
"0239" ~ "WAREKENA (UAREQUENA, WEREKENA)",
"0240" ~ "WASSU",
"0241" ~ "WAURA (UAURA, WAUJA)",
"0242" ~ "WAYANA (WAIANA, UAIANA)",
"0243" ~ "WITOTO (UITOTO, HUITOTO)",
"0244" ~ "XAKRIABA (XACRIABA)",
"0245" ~ "XAVANTE (A'UWE, AKWE, AWEN, AKWEN)",
"0246" ~ "XERENTE (AKWE, AWEN, AKWEN)",
"0247" ~ "XETA",
"0248" ~ "XIPAIA (SHIPAYA, XIPAYA)",
"0249" ~ "XOKLENG (SHOKLENG, XOCLENG)",
"0250" ~ "XOKO (XOCO, CHOCO)",
"0251" ~ "XUKURU (XUCURU)",
"0252" ~ "XUKURU KARIRI (XUCURU-KARIRI)",
"0253" ~ "YAIPIYANA",
"0254" ~ "YAMINAWA (JAMINAWA, IAMINAWA)",
"0255" ~ "YANOMAMI NINAM (IANOMAMI, IANOAMA, XIRIANA)",
"0256" ~ "YANOMAMI SANUMA (IANOMAMI, IANOAMA, XIRIANA)",
"0257" ~ "YANOMAMI YANOMAM (IANOMAMI, IANOAMA, XIRIANA)",
"0258" ~ "YAWALAPITI (IAUALAPITI)",
"0259" ~ "YAWANAWA (IAUANAUA)",
"0260" ~ "YEKUANA (MAIONGON, YE'KUANA, YEKWANA, MAYONGONG)",
"0261" ~ "YUDJA (JURUNA, YURUNA)",
"0262" ~ "ZO'E (POTURU)",
"0263" ~ "ZORO (PAGEYN)",
"0264" ~ "ZURUAHA (SOROWAHA, SURUWAHA)",
"X265" ~ "AHANENAWA",
"X266" ~ "AICABA",
"X267" ~ "AIKAN\\u00c3-KWAS\\u00c1",
"X268" ~ "AKUNTSU",
"X269" ~ "ALANTESU",
"X271" ~ "AMAW\\u00c1KA",
"X272" ~ "ANAC\\u00c9",
"X273" ~ "APURIN\\u00c3",
"X274" ~ "ARAN\\u00c3",
"X275" ~ "ARAPA\\u00c7O",
"X276" ~ "ARARA APOLIMA",
"X277" ~ "ARARA DO ARIPUANA",
"X278" ~ "ARIPUAN\\u00c1",
"X279" ~ "ASSURINI",
"X280" ~ "AWUAR\\u00c1",
"X281" ~ "BORBA",
"X282" ~ "CABIXI",
"X283" ~ "CAMARAR\\u00c9",
"X284" ~ "CAMASURI",
"X285" ~ "CARA PRETA",
"X286" ~ "CHARRUA",
"X287" ~ "CUJUBIM",
"X288" ~ "DAW",
"X289" ~ "GAVI\\u00c3O",
"X290" ~ "GUARANI",
"X291" ~ "HALANTESU",
"X292" ~ "HALOTESU",
"X293" ~ "HENGAT\\u00da",
"X294" ~ "HIXKARYANA",
"X295" ~ "HUPDE",
"X296" ~ "HUPDES",
"X297" ~ "IAUANAUA",
"X298" ~ "IAUARETE A\\u00c7U",
"X299" ~ "IKPENG",
"X300" ~ "INAMBU",
"X301" ~ "INHABARANA",
"X302" ~ "JAVAE",
"X303" ~ "JENIPAPO",
"X304" ~ "JENIPAPO-KANINDE",
"X305" ~ "JIAHOI",
"X306" ~ "KAIOWA",
"X307" ~ "KAMPA",
"X308" ~ "KANELA",
"X309" ~ "KARAFAWYANA",
"X310" ~ "KARARAO",
"X311" ~ "KARUBO",
"X312" ~ "KASSUP\\u00c1",
"X313" ~ "KATITH\\u00c3ULU",
"X314" ~ "KATOKIN",
"X315" ~ "KATUKINA PANO",
"X316" ~ "KATUKINA PEDA DJAPA",
"X317" ~ "KATUKINA SHANENAUWA",
"X318" ~ "KAXAGO",
"X319" ~ "KAYABI",
"X320" ~ "KIN\\u00c3 (WAIMIRI-ATROARI)",
"X321" ~ "KIRIRI-BARRA",
"X322" ~ "KITH\\u00c3ULU",
"X323" ~ "KOIAI\\u00c1",
"X324" ~ "KOIUPANK\\u00c1",
"X325" ~ "KONTANAWA",
"X326" ~ "KRAH\\u00d4 KANELA",
"X327" ~ "KULINA",
"X328" ~ "LATUND\\u00ca",
"X329" ~ "MAKU",
"X330" ~ "MAKUNAMB\\u00c9",
"X331" ~ "MAMAIND\\u00ca",
"X332" ~ "MAMURI",
"X333" ~ "MANACAPURU",
"X334" ~ "MANAIRISSU",
"X335" ~ "MANCHINERI",
"X336" ~ "MANDUCA",
"X337" ~ "MARIBONDO",
"X338" ~ "MASSAKA",
"X339" ~ "MAWAYANA",
"X340" ~ "MAW\\u00c9",
"X341" ~ "MAYORUNA",
"X342" ~ "MIQUELENO",
"X343" ~ "MOKURI\\u00d1",
"X344" ~ "MON ORO WARAM",
"X345" ~ "MUTUM",
"X346" ~ "MYKY",
"X347" ~ "NADEB",
"X348" ~ "NAMBIKWARA",
"X349" ~ "NEGAROT\\u00ca",
"X350" ~ "NHENGATU",
"X351" ~ "OFAIE XAVANTE",
"X352" ~ "ON\\u00c7A",
"X353" ~ "ORO AT",
"X354" ~ "ORO EO",
"X355" ~ "ORO JOWIN",
"X356" ~ "ORO MIYLIN",
"X357" ~ "ORO MON",
"X358" ~ "ORO N\\u00c1O",
"X359" ~ "ORO WAM",
"X360" ~ "ORO WARAM",
"X361" ~ "ORO WARAM XIJEIN",
"X362" ~ "PACA",
"X363" ~ "PANKAR\\u00c1",
"X364" ~ "PAPAGAIO",
"X365" ~ "PAYAY\\u00c1",
"X366" ~ "PIPIPAN",
"X367" ~ "PIRATA",
"X368" ~ "PUROBOR\\u00c1",
"X369" ~ "SABAN\\u00ca",
"X370" ~ "SANUMA",
"X371" ~ "SAWENTES\\u00da",
"X372" ~ "SILCY-TAPUYA",
"X373" ~ "SIUCI",
"X374" ~ "TABAJARA",
"X375" ~ "TAKUARA",
"X376" ~ "TATU",
"X377" ~ "TAWAND\\u00ca",
"X378" ~ "TEF\\u00c9",
"X379" ~ "TIMBIRA",
"X380" ~ "TOR\\u00c1 DO BAIXO GRANDE",
"X381" ~ "TSUNHUM-DJAP\\u00c1",
"X382" ~ "TUBAR\\u00c3O",
"X383" ~ "TUPAIU",
"X384" ~ "TUPI",
"X385" ~ "TUPINAMB\\u00c1 DE BELMONTE",
"X386" ~ "URUBU",
"X387" ~ "URUBU KAAPOR",
"X388" ~ "URUP\\u00c1",
"X389" ~ "WAI WAI",
"X390" ~ "WAIKISU",
"X391" ~ "WAKALITES\\u00da",
"X392" ~ "WASSUSU",
"X393" ~ "XEREU",
"X394" ~ "XI EIN",
"X395" ~ "XICRIN",
"X396" ~ "XIPAYA",
"X397" ~ "XIRIANA",
"X398" ~ "XIRUAI",
"X399" ~ "YEPAMASS\\u00c3",
"X400" ~ "TIRIY\\u00d3",
"X401" ~ "YANOMAMI",
"X402" ~ "ARARA",
"X403" ~ "SAKIRIABAR",
"X404" ~ "TATZ",
"X405" ~ "SEM INFORMACAO",
.default = .data$ETNIA
)) %>%
dplyr::mutate(ETNIA = as.factor(.data$ETNIA))
}
# VAL_SH_FED
if("VAL_SH_FED" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SH_FED = as.numeric(.data$VAL_SH_FED))
}
# VAL_SP_FED
if("VAL_SP_FED" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SP_FED = as.numeric(.data$VAL_SP_FED))
}
# VAL_SH_GES
if("VAL_SH_GES" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SH_GES = as.numeric(.data$VAL_SH_GES))
}
# VAL_SP_GES
if("VAL_SP_GES" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_SP_GES = as.numeric(.data$VAL_SP_GES))
}
# VAL_UCI
if("VAL_UCI" %in% variables_names){
data <- data %>%
dplyr::mutate(VAL_UCI = as.numeric(.data$VAL_UCI))
}
# MARCA_UCI
if("MARCA_UCI" %in% variables_names){
data <- data %>%
dplyr::mutate(MARCA_UCI = as.character(.data$MARCA_UCI)) %>%
dplyr::mutate(MARCA_UCI = dplyr::case_match(
.data$MARCA_UCI,
"0" ~ "N\u00e3o utilizou UCI",
"1" ~ "Unidade de cuidados intermed neonatal convencional",
"2" ~ "Unidade de cuidados intermed neonatal canguru",
"3" ~ "Unidade intermedi\u00e1ria neonatal",
"88" ~ "Utilizou dois tipos de leitos UCI",
.default = .data$MARCA_UCI
)) %>%
dplyr::mutate(MARCA_UCI = as.factor(.data$MARCA_UCI))
}
# TPDISEC1
if("TPDISEC1" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC1 = as.character(.data$TPDISEC1)) %>%
dplyr::mutate(TPDISEC1 = dplyr::case_match(
.data$TPDISEC1,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC1
)) %>%
dplyr::mutate(TPDISEC1 = as.factor(.data$TPDISEC1))
}
# TPDISEC2
if("TPDISEC2" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC2 = as.character(.data$TPDISEC2)) %>%
dplyr::mutate(TPDISEC2 = dplyr::case_match(
.data$TPDISEC2,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC2
)) %>%
dplyr::mutate(TPDISEC2 = as.factor(.data$TPDISEC2))
}
# TPDISEC3
if("TPDISEC3" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC3 = as.character(.data$TPDISEC3)) %>%
dplyr::mutate(TPDISEC3 = dplyr::case_match(
.data$TPDISEC3,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC3
)) %>%
dplyr::mutate(TPDISEC3 = as.factor(.data$TPDISEC3))
}
# TPDISEC4
if("TPDISEC4" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC4 = as.character(.data$TPDISEC4)) %>%
dplyr::mutate(TPDISEC4 = dplyr::case_match(
.data$TPDISEC4,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC4
)) %>%
dplyr::mutate(TPDISEC4 = as.factor(.data$TPDISEC4))
data$TPDISEC4 <- factor(data$TPDISEC4)
}
# TPDISEC5
if("TPDISEC5" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC5 = as.character(.data$TPDISEC5)) %>%
dplyr::mutate(TPDISEC5 = dplyr::case_match(
.data$TPDISEC5,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC5
)) %>%
dplyr::mutate(TPDISEC5 = as.factor(.data$TPDISEC5))
}
# TPDISEC6
if("TPDISEC6" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC6 = as.character(.data$TPDISEC6)) %>%
dplyr::mutate(TPDISEC6 = dplyr::case_match(
.data$TPDISEC6,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC6
)) %>%
dplyr::mutate(TPDISEC6 = as.factor(.data$TPDISEC6))
}
# TPDISEC7
if("TPDISEC7" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC7 = as.character(.data$TPDISEC7)) %>%
dplyr::mutate(TPDISEC7 = dplyr::case_match(
.data$TPDISEC7,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC7
)) %>%
dplyr::mutate(TPDISEC7 = as.factor(.data$TPDISEC7))
}
# TPDISEC8
if("TPDISEC8" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC8 = as.character(.data$TPDISEC8)) %>%
dplyr::mutate(TPDISEC8 = dplyr::case_match(
.data$TPDISEC8,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC8
)) %>%
dplyr::mutate(TPDISEC8 = as.factor(.data$TPDISEC8))
}
# TPDISEC9
if("TPDISEC9" %in% variables_names){
data <- data %>%
dplyr::mutate(TPDISEC9 = as.character(.data$TPDISEC9)) %>%
dplyr::mutate(TPDISEC9 = dplyr::case_match(
.data$TPDISEC9,
"0" ~ NA,
"1" ~ "Pr\u00e9-existente",
"2" ~ "Adquirido",
.default = .data$TPDISEC9
)) %>%
dplyr::mutate(TPDISEC9 = as.factor(.data$TPDISEC9))
}
}
# From data.table to tibble
data <- tibble::as_tibble(data)
# Purge levels
data <- droplevels(data.table::as.data.table(data))
# Unescape unicode characters
data <- suppressWarnings(tibble::as_tibble(lapply(X = data, FUN = stringi::stri_unescape_unicode)))
# Return
return(data)
}
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