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Merge pull request #34 from rfsaldanha/sinan-chikungunya
Sinan chikungunya
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,315 @@ | ||
| #' Process SINAN Chikungunya variables from DataSUS | ||
| #' | ||
| #' \code{process_sinan_chikungunya} processes SINAN Dengue variables retrieved by \code{fetch_datasus()}. | ||
| #' | ||
| #' This function processes SINAN Chikungunya variables retrieved by \code{fetch_datasus()}, informing labels for categoric variables including NA values. | ||
| #' | ||
| #' @param data \code{data.frame} created by \code{fetch_datasus()}. | ||
| #' @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 = 2016, year_end = 2016, uf = "RJ", information_system = "SINAN-CHIKUNGUNYA-FINAL") | ||
| #' df_a <- process_sinan_dengue(df) | ||
| #' df_b <- process_sinan_dengue(df, municipality_data = FALSE) | ||
| #' } | ||
| #' @export | ||
|
|
||
| process_sinan_chikungunya <- function(data, municipality_data = TRUE){ | ||
| # Variables names | ||
| variables_names <- names(data) | ||
|
|
||
| # Declara objetos | ||
| ano <- NULL | ||
| unidade <- NULL | ||
|
|
||
| # TP_NOT | ||
| if ("TP_NOT" %in% variables_names) { | ||
| data$TP_NOT <- as.numeric(levels(data$TP_NOT))[data$TP_NOT] | ||
| data$TP_NOT[data$TP_NOT == 1] <- "Negativa" | ||
| data$TP_NOT[data$TP_NOT == 2] <- "Individual" | ||
| data$TP_NOT[data$TP_NOT == 3] <- "Surto" | ||
| data$TP_NOT[data$TP_NOT == 4] <- "Agregado" | ||
| data$TP_NOT <- factor(data$TP_NOT) | ||
| } | ||
|
|
||
| # DT_NOTIFIC | ||
| if ("DT_NOTIFIC" %in% variables_names) { | ||
| data$DT_NOTIFIC <- as.Date(data$DT_NOTIFIC) | ||
| } | ||
|
|
||
| # SG_UF_NOT | ||
| if ("SG_UF_NOT" %in% variables_names) { | ||
| data$SG_UF_NOT <- as.numeric(levels(data$SG_UF_NOT))[data$SG_UF_NOT] | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 0] <- "Ignorado" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 99] <- "Ignorado" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 11] <- "Rond\u00f4nia" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 12] <- "Acre" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 13] <- "Amazonas" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 14] <- "Roraima" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 15] <- "Par\u00e1" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 16] <- "Amap\u00e1" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 17] <- "Tocantis" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 21] <- "Maranh\u00e3o" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 22] <- "Piau\u00ed" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 23] <- "Cear\u00e1" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 24] <- "Rio Grande do Norte" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 25] <- "Para\u00edba" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 26] <- "Pernambuco" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 27] <- "Alagoas" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 28] <- "Sergipe" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 29] <- "Bahia" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 31] <- "Minas Gerais" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 32] <- "Esp\u00edrito Santo" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 33] <- "Rio de Janeiro" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 35] <- "S\u00e3o Paulo" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 41] <- "Paran\u00e1" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 42] <- "Santa Catarina" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 43] <- "Rio Grande do Sul" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 50] <- "Mato Grosso do Sul" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 51] <- "Mato Grosso" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 52] <- "Goi\u00e1s" | ||
| data$SG_UF_NOT[data$SG_UF_NOT == 53] <- "Distrito Federal" | ||
| data$SG_UF_NOT <- factor(data$SG_UF_NOT) | ||
| } | ||
|
|
||
| # IDADE | ||
| if ("NU_IDADE_N" %in% variables_names) { | ||
| data$NU_IDADE_N <- as.character(data$NU_IDADE_N) | ||
| data$NU_IDADE_N[data$NU_IDADE_N == "000" | data$NU_IDADE_N == "999"] <- NA | ||
| unidade <- substr(data$NU_IDADE_N, 1, 1) | ||
| # Horas | ||
| data$IDADEhoras <- | ||
| as.numeric(ifelse(unidade == 1, substr(data$NU_IDADE_N, 2, 3), NA)) | ||
| # Dias | ||
| data$IDADEdias <- | ||
| as.numeric(ifelse(unidade == 2, substr(data$NU_IDADE_N, 2, 3), NA)) | ||
| # Meses | ||
| data$IDADEmeses <- | ||
| as.numeric(ifelse(unidade == 3, substr(data$NU_IDADE_N, 2, 3), NA)) | ||
| # Anos | ||
| data$IDADEanos <- | ||
| as.numeric(ifelse( | ||
| unidade == 4, | ||
| substr(data$NU_IDADE_N, 2, 3), | ||
| ifelse(unidade == 5, 100 + as.numeric(substr(data$NU_IDADE_N, 2, 3)), NA) | ||
| )) | ||
| # Apaga campo original | ||
| data$NU_IDADE_N <- NULL | ||
| } | ||
|
|
||
| # CS_SEXO | ||
| if ("CS_SEXO" %in% variables_names) { | ||
| data$CS_SEXO <- as.character(levels(data$CS_SEXO))[data$CS_SEXO] | ||
| data$CS_SEXO[data$CS_SEXO == "M"] <- "Masculino" | ||
| data$CS_SEXO[data$CS_SEXO == "F"] <- "Feminino" | ||
| data$CS_SEXO[data$CS_SEXO == "I"] <- "Ignorado" | ||
| data$CS_SEXO <- factor(data$CS_SEXO) | ||
| } | ||
|
|
||
| # CS_GESTANT | ||
| if ("CS_GESTANT" %in% variables_names) { | ||
| data$CS_GESTANT <- as.numeric(levels(data$CS_GESTANT))[data$CS_GESTANT] | ||
| data$CS_GESTANT[data$CS_GESTANT == 1] <- "1o trimestre" | ||
| data$CS_GESTANT[data$CS_GESTANT == 2] <- "2o trimestre" | ||
| data$CS_GESTANT[data$CS_GESTANT == 3] <- "3o trimestre" | ||
| data$CS_GESTANT[data$CS_GESTANT == 4] <- "Idade gestacional ignorada" | ||
| data$CS_GESTANT[data$CS_GESTANT == 5] <- "N\u00e3o" | ||
| data$CS_GESTANT[data$CS_GESTANT == 6] <- "N\u00e3o se aplica" | ||
| data$CS_GESTANT[data$CS_GESTANT == 9] <- "Ignorado" | ||
| data$CS_GESTANT <- factor(data$CS_GESTANT) | ||
| } | ||
|
|
||
| # CS_RACA | ||
| if ("CS_RACA" %in% variables_names) { | ||
| data$CS_RACA <- as.numeric(levels(data$CS_RACA))[data$CS_RACA] | ||
| data$CS_RACA[data$CS_RACA == 1] <- "Branca" | ||
| data$CS_RACA[data$CS_RACA == 2] <- "Preta" | ||
| data$CS_RACA[data$CS_RACA == 3] <- "Amarela" | ||
| data$CS_RACA[data$CS_RACA == 4] <- "Parda" | ||
| data$CS_RACA[data$CS_RACA == 5] <- "Ind\u00edgena" | ||
| data$CS_RACA[data$CS_RACA == 9] <- "Ignorado" | ||
| data$CS_RACA <- factor(data$CS_RACA) | ||
| } | ||
|
|
||
| # CS_ESCOL_N | ||
| if ("CS_ESCOL_N" %in% variables_names) { | ||
| data$CS_ESCOL_N <- as.numeric(levels(data$CS_ESCOL_N))[data$CS_ESCOL_N] | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 1] <- "1a a 4a s\u00e9rie incompleta do EF" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 2] <- "4a s\u00e9rie completa do EF (antigo 1o grau)" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 3] <- "5a \u00e0 8a s\u00e9rie incompleta do EF (antigo gin\u00e1sio ou 1o grau)" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 4] <- "Ensino fundamental completo (antigo gin\u00e1sio ou 1o grau)" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 5] <- "Ensino m\u00e9dio incompleto (antigo colegial ou 2o grau)" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 6] <- "Ensino m\u00e9dio completo (antigo colegial ou 2o grau)" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 7] <- "Educa\u00e7\u00e3o superior incompleta" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 8] <- "Educa\u00e7\u00e3o superior completa" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 9] <- "Ignorado" | ||
| data$CS_ESCOL_N[data$CS_ESCOL_N == 10] <- "N\u00e3o se aplica" | ||
| data$CS_ESCOL_N <- factor(data$CS_ESCOL_N) | ||
| } | ||
|
|
||
| # SG_UF | ||
| if ("SG_UF" %in% variables_names) { | ||
| data$SG_UF <- as.numeric(levels(data$SG_UF))[data$SG_UF] | ||
| data$SG_UF[data$SG_UF == 0] <- "Ignorado" | ||
| data$SG_UF[data$SG_UF == 99] <- "Ignorado" | ||
| data$SG_UF[data$SG_UF == 11] <- "Rond\u00f4nia" | ||
| data$SG_UF[data$SG_UF == 12] <- "Acre" | ||
| data$SG_UF[data$SG_UF == 13] <- "Amazonas" | ||
| data$SG_UF[data$SG_UF == 14] <- "Roraima" | ||
| data$SG_UF[data$SG_UF == 15] <- "Par\u00e1" | ||
| data$SG_UF[data$SG_UF == 16] <- "Amap\u00e1" | ||
| data$SG_UF[data$SG_UF == 17] <- "Tocantis" | ||
| data$SG_UF[data$SG_UF == 21] <- "Maranh\u00e3o" | ||
| data$SG_UF[data$SG_UF == 22] <- "Piau\u00ed" | ||
| data$SG_UF[data$SG_UF == 23] <- "Cear\u00e1" | ||
| data$SG_UF[data$SG_UF == 24] <- "Rio Grande do Norte" | ||
| data$SG_UF[data$SG_UF == 25] <- "Para\u00edba" | ||
| data$SG_UF[data$SG_UF == 26] <- "Pernambuco" | ||
| data$SG_UF[data$SG_UF == 27] <- "Alagoas" | ||
| data$SG_UF[data$SG_UF == 28] <- "Sergipe" | ||
| data$SG_UF[data$SG_UF == 29] <- "Bahia" | ||
| data$SG_UF[data$SG_UF == 31] <- "Minas Gerais" | ||
| data$SG_UF[data$SG_UF == 32] <- "Esp\u00edrito Santo" | ||
| data$SG_UF[data$SG_UF == 33] <- "Rio de Janeiro" | ||
| data$SG_UF[data$SG_UF == 35] <- "S\u00e3o Paulo" | ||
| data$SG_UF[data$SG_UF == 41] <- "Paran\u00e1" | ||
| data$SG_UF[data$SG_UF == 42] <- "Santa Catarina" | ||
| data$SG_UF[data$SG_UF == 43] <- "Rio Grande do Sul" | ||
| data$SG_UF[data$SG_UF == 50] <- "Mato Grosso do Sul" | ||
| data$SG_UF[data$SG_UF == 51] <- "Mato Grosso" | ||
| data$SG_UF[data$SG_UF == 52] <- "Goi\u00e1s" | ||
| data$SG_UF[data$SG_UF == 53] <- "Distrito Federal" | ||
| data$SG_UF <- factor(data$SG_UF) | ||
| } | ||
|
|
||
| # ID_PAIS | ||
| if ("ID_PAIS" %in% variables_names) { | ||
| data$ID_PAIS <- as.character(levels(data$ID_PAIS))[data$ID_PAIS] | ||
| data$ID_PAIS <- dplyr::left_join(data, microdatasus::paisnet, by = c("ID_PAIS" = "ID_PAIS"))$NM_PAIS | ||
| } | ||
|
|
||
| # ID_OCUPA_N | ||
| if ("ID_OCUPA_N" %in% variables_names) { | ||
| data$ID_OCUPA_N <- as.character(levels(data$ID_OCUPA_N))[data$ID_OCUPA_N] | ||
| data$ID_OCUPA_N <- factor(dplyr::left_join(data, microdatasus::tabCBO, by = c("ID_OCUPA_N" = "cod"))$nome) | ||
| } | ||
|
|
||
| # CLASSI_FIN | ||
| if ("CLASSI_FIN" %in% variables_names) { | ||
| data$CLASSI_FIN <- as.numeric(levels(data$CLASSI_FIN))[data$CLASSI_FIN] | ||
| data$CLASSI_FIN[data$CLASSI_FIN == 5] <- "Descartado" | ||
| data$CLASSI_FIN[data$CLASSI_FIN == 8] <- "Inconclusivo" | ||
| data$CLASSI_FIN[data$CLASSI_FIN == 10] <- "Dengue" | ||
| data$CLASSI_FIN[data$CLASSI_FIN == 11] <- "Dengue com sinais de alarme" | ||
| data$CLASSI_FIN[data$CLASSI_FIN == 12] <- "Dengue grave" | ||
| data$CLASSI_FIN[data$CLASSI_FIN == 13] <- "Chikungunya" | ||
| data$CLASSI_FIN <- factor(data$CLASSI_FIN) | ||
| } | ||
|
|
||
| # CRITERIO | ||
| if ("CRITERIO" %in% variables_names) { | ||
| data$CRITERIO <- as.numeric(levels(data$CRITERIO))[data$CRITERIO] | ||
| data$CRITERIO[data$CRITERIO == 1] <- "Laborat\u00f3rio" | ||
| data$CRITERIO[data$CRITERIO == 2] <- "Cl\u00ednico epidemiol\u00f3gico" | ||
| data$CRITERIO[data$CRITERIO == 3] <- "Em investiga\u00e7\u00e3o" | ||
| data$CRITERIO <- factor(data$CRITERIO) | ||
| } | ||
|
|
||
| # TPAUTOCTO | ||
| if ("TPAUTOCTO" %in% variables_names) { | ||
| data$TPAUTOCTO <- as.numeric(levels(data$TPAUTOCTO))[data$TPAUTOCTO] | ||
| data$TPAUTOCTO[data$TPAUTOCTO == 1] <- "Sim" | ||
| data$TPAUTOCTO[data$TPAUTOCTO == 2] <- "N\u00e3o" | ||
| data$TPAUTOCTO[data$TPAUTOCTO == 3] <- "Indeterminado" | ||
| data$TPAUTOCTO <- factor(data$TPAUTOCTO) | ||
| } | ||
|
|
||
| # COUFINF | ||
| if ("COUFINF" %in% variables_names) { | ||
| data$COUFINF <- as.numeric(levels(data$COUFINF))[data$COUFINF] | ||
| data$COUFINF[data$COUFINF == 0] <- "Ignorado" | ||
| data$COUFINF[data$COUFINF == 99] <- "Ignorado" | ||
| data$COUFINF[data$COUFINF == 11] <- "Rond\u00f4nia" | ||
| data$COUFINF[data$COUFINF == 12] <- "Acre" | ||
| data$COUFINF[data$COUFINF == 13] <- "Amazonas" | ||
| data$COUFINF[data$COUFINF == 14] <- "Roraima" | ||
| data$COUFINF[data$COUFINF == 15] <- "Par\u00e1" | ||
| data$COUFINF[data$COUFINF == 16] <- "Amap\u00e1" | ||
| data$COUFINF[data$COUFINF == 17] <- "Tocantis" | ||
| data$COUFINF[data$COUFINF == 21] <- "Maranh\u00e3o" | ||
| data$COUFINF[data$COUFINF == 22] <- "Piau\u00ed" | ||
| data$COUFINF[data$COUFINF == 23] <- "Cear\u00e1" | ||
| data$COUFINF[data$COUFINF == 24] <- "Rio Grande do Norte" | ||
| data$COUFINF[data$COUFINF == 25] <- "Para\u00edba" | ||
| data$COUFINF[data$COUFINF == 26] <- "Pernambuco" | ||
| data$COUFINF[data$COUFINF == 27] <- "Alagoas" | ||
| data$COUFINF[data$COUFINF == 28] <- "Sergipe" | ||
| data$COUFINF[data$COUFINF == 29] <- "Bahia" | ||
| data$COUFINF[data$COUFINF == 31] <- "Minas Gerais" | ||
| data$COUFINF[data$COUFINF == 32] <- "Esp\u00edrito Santo" | ||
| data$COUFINF[data$COUFINF == 33] <- "Rio de Janeiro" | ||
| data$COUFINF[data$COUFINF == 35] <- "S\u00e3o Paulo" | ||
| data$COUFINF[data$COUFINF == 41] <- "Paran\u00e1" | ||
| data$COUFINF[data$COUFINF == 42] <- "Santa Catarina" | ||
| data$COUFINF[data$COUFINF == 43] <- "Rio Grande do Sul" | ||
| data$COUFINF[data$COUFINF == 50] <- "Mato Grosso do Sul" | ||
| data$COUFINF[data$COUFINF == 51] <- "Mato Grosso" | ||
| data$COUFINF[data$COUFINF == 52] <- "Goi\u00e1s" | ||
| data$COUFINF[data$COUFINF == 53] <- "Distrito Federal" | ||
| data$COUFINF <- factor(data$COUFINF) | ||
| } | ||
|
|
||
| # COPAISINF | ||
| if ("COPAISINF" %in% variables_names) { | ||
| data$COPAISINF <- as.character(levels(data$COPAISINF))[data$COPAISINF] | ||
| data$COPAISINF <- dplyr::left_join(data, microdatasus::paisnet, by = c("COPAISINF" = "COPAISINF"))$NM_PAIS | ||
| } | ||
|
|
||
| # DOENCA_TRA | ||
| if ("DOENCA_TRA" %in% variables_names) { | ||
| data$DOENCA_TRA <- as.numeric(levels(data$DOENCA_TRA))[data$DOENCA_TRA] | ||
| data$DOENCA_TRA[data$DOENCA_TRA == 1] <- "Sim" | ||
| data$DOENCA_TRA[data$DOENCA_TRA == 2] <- "N\u00e3o" | ||
| data$DOENCA_TRA[data$DOENCA_TRA == 9] <- "Ignorado" | ||
| data$DOENCA_TRA <- factor(data$DOENCA_TRA) | ||
| } | ||
|
|
||
| # EVOLUCAO | ||
| if ("EVOLUCAO" %in% variables_names) { | ||
| data$EVOLUCAO <- as.numeric(levels(data$EVOLUCAO))[data$EVOLUCAO] | ||
| data$EVOLUCAO[data$EVOLUCAO == 1] <- "Cura" | ||
| data$EVOLUCAO[data$EVOLUCAO == 2] <- "\u00d3bito por dengue" | ||
| data$EVOLUCAO[data$EVOLUCAO == 3] <- "\u00d3bito por outras causas" | ||
| data$EVOLUCAO[data$EVOLUCAO == 4] <- "\u00d3bito em investiga\u00e7\u00e3o" | ||
| data$EVOLUCAO[data$EVOLUCAO == 9] <- "Ignorado" | ||
| data$EVOLUCAO <- factor(data$EVOLUCAO) | ||
| } | ||
|
|
||
| # DT_OBITO | ||
| if ("DT_OBITO" %in% variables_names) { | ||
| data$DT_OBITO <- as.Date(data$DT_OBITO) | ||
| } | ||
|
|
||
| # DT_ENCERRA | ||
| if ("DT_ENCERRA" %in% variables_names) { | ||
| data$DT_ENCERRA <- as.Date(data$DT_ENCERRA) | ||
| } | ||
|
|
||
| # CS_FLXRET | ||
| if ("CS_FLXRET" %in% variables_names) { | ||
| data$CS_FLXRET <- as.numeric(levels(data$CS_FLXRET))[data$CS_FLXRET] | ||
| data$CS_FLXRET[data$CS_FLXRET == 0] <- "N\u00e3o" | ||
| data$CS_FLXRET[data$CS_FLXRET == 1] <- "Habilitado para envio" | ||
| data$CS_FLXRET[data$CS_FLXRET == 2] <- "Enviado" | ||
| data$CS_FLXRET <- factor(data$CS_FLXRET) | ||
| } | ||
|
|
||
| # Purge levels | ||
| data <- droplevels(data) | ||
|
|
||
| # Unescape unicode characters | ||
| data <- as.data.frame(lapply(X = data, FUN = stringi::stri_unescape_unicode)) | ||
|
|
||
| # Return | ||
| return(data) | ||
| } |
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