From 830195e3e2fc596d9a411eee374865716bd23324 Mon Sep 17 00:00:00 2001
From: rfsaldanha <rfsaldanha@gmail.com>
Date: Tue, 14 Sep 2021 19:17:49 -0300
Subject: [PATCH 1/5] Correct dengue documentation

---
 R/process_sinan_dengue.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/R/process_sinan_dengue.R b/R/process_sinan_dengue.R
index 5bd04de..ea9ddfe 100644
--- a/R/process_sinan_dengue.R
+++ b/R/process_sinan_dengue.R
@@ -10,7 +10,6 @@
 #' @examples \dontrun{
 #' df <- fetch_datasus(year_start = 2016, year_end = 2016,
 #' uf = "RJ", information_system = "SINAN-DENGUE-FINAL")
-#' df <- process_sinan_dengue(year_start = 2016, year_end = 2016, uf = "RJ")
 #' df_a <- process_sinan_dengue(df)
 #' df_b <- process_sinan_dengue(df, municipality_data = FALSE)
 #' }

From 0e6dd3116fe9d41fee0a29d6245ae972d0581342 Mon Sep 17 00:00:00 2001
From: rfsaldanha <rfsaldanha@gmail.com>
Date: Tue, 14 Sep 2021 19:43:34 -0300
Subject: [PATCH 2/5] process_sinan_malaria function

---
 NAMESPACE                    |   1 +
 R/process_sinan_malaria.R    | 345 +++++++++++++++++++++++++++++++++++
 man/process_sinan_dengue.Rd  |   1 -
 man/process_sinan_malaria.Rd |  27 +++
 4 files changed, 373 insertions(+), 1 deletion(-)
 create mode 100644 R/process_sinan_malaria.R
 create mode 100644 man/process_sinan_malaria.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 367feca..bfb9830 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -7,5 +7,6 @@ export(process_sih)
 export(process_sim)
 export(process_sinan_chikungunya)
 export(process_sinan_dengue)
+export(process_sinan_malaria)
 export(process_sinan_zika)
 export(process_sinasc)
diff --git a/R/process_sinan_malaria.R b/R/process_sinan_malaria.R
new file mode 100644
index 0000000..e92d51e
--- /dev/null
+++ b/R/process_sinan_malaria.R
@@ -0,0 +1,345 @@
+#' Process SINAN Malaria variables from DataSUS
+#'
+#' \code{process_sinan_malaria} processes SINAN Malaria variables retrieved by \code{fetch_datasus()}.
+#'
+#' This function processes SINAN Malaria 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-MALARIA-FINAL")
+#' df_a <- process_sinan_malaria(df)
+#' df_b <- process_sinan_malaria(df, municipality_data = FALSE)
+#' }
+#' @export
+
+process_sinan_malaria <- 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 == 1] <- "Confirmado"
+    data$CLASSI_FIN[data$CLASSI_FIN == 2] <- "Descartado"
+    data$CLASSI_FIN <- factor(data$CLASSI_FIN)
+  }
+
+  # AT_ATIVIDA
+  if ("AT_ATIVIDA" %in% variables_names) {
+    data$AT_ATIVIDA <- as.numeric(levels(data$AT_ATIVIDA))[data$AT_ATIVIDA]
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 1] <- "Agricultura"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 2] <- "Pecu\u00e1ria"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 3] <- "Dom\u00e9stica"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 4] <- "Turismo"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 5] <- "Garimpagem"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 6] <- "Explora\u00e7\u00e3o vegetal"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 7] <- "Ca\u00e7a/Pesca"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 8] <- "Construtor de estradas/barragens"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 9] <- "Minera\u00e7\u00e3o"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 10] <- "Viajante"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 11] <- "Outros"
+    data$AT_ATIVIDA[data$AT_ATIVIDA == 99] <- "Ignorado"
+    data$AT_ATIVIDA <- factor(data$AT_ATIVIDA)
+  }
+
+  # AT_LAMINA
+  if ("AT_LAMINA" %in% variables_names) {
+    data$AT_LAMINA <- as.numeric(levels(data$AT_LAMINA))[data$AT_LAMINA]
+    data$AT_LAMINA[data$AT_LAMINA == 1] <- "BP"
+    data$AT_LAMINA[data$AT_LAMINA == 2] <- "BA"
+    data$AT_LAMINA[data$AT_LAMINA == 3] <- "LVC"
+    data$AT_LAMINA <- factor(data$AT_LAMINA)
+  }
+
+  # AT_SINTOMA
+  if ("AT_SINTOMA" %in% variables_names) {
+    data$AT_SINTOMA <- as.numeric(levels(data$AT_SINTOMA))[data$AT_SINTOMA]
+    data$AT_SINTOMA[data$AT_SINTOMA == 1] <- "Com sintomas"
+    data$AT_SINTOMA[data$AT_SINTOMA == 2] <- "Sem sintomas"
+    data$AT_SINTOMA <- factor(data$AT_SINTOMA)
+  }
+
+  # TPAUTOCTO
+  if ("TPAUTOCTO" %in% variables_names) {
+    data$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
+  }
+
+  # RESULT
+  if ("RESULT" %in% variables_names) {
+    data$RESULT <- as.numeric(levels(data$RESULT))[data$RESULT]
+    data$RESULT[data$RESULT == 1] <- "Negativo"
+    data$RESULT[data$RESULT == 2] <- "F"
+    data$RESULT[data$RESULT == 3] <- "F + FG"
+    data$RESULT[data$RESULT == 4] <- "V"
+    data$RESULT[data$RESULT == 5] <- "F + V"
+    data$RESULT[data$RESULT == 6] <- "V + FG"
+    data$RESULT[data$RESULT == 7] <- "FG"
+    data$RESULT[data$RESULT == 8] <- "M"
+    data$RESULT[data$RESULT == 9] <- "F + M"
+    data$RESULT[data$RESULT == 10] <- "O"
+    data$RESULT <- factor(data$RESULT)
+  }
+
+  # PCRUZ
+  if ("PCRUZ" %in% variables_names) {
+    data$PCRUZ <- as.numeric(levels(data$PCRUZ))[data$PCRUZ]
+    data$PCRUZ[data$PCRUZ == 1] <- "Menor que meia cruz"
+    data$PCRUZ[data$PCRUZ == 2] <- "Meia cruz"
+    data$PCRUZ[data$PCRUZ == 3] <- "Uma cruz"
+    data$PCRUZ[data$PCRUZ == 4] <- "Duas cruzes"
+    data$PCRUZ[data$PCRUZ == 5] <- "Tr\u00eas cruzes"
+    data$PCRUZ[data$PCRUZ == 6] <- "Quatro cruzes"
+    data$PCRUZ <- factor(data$PCRUZ)
+  }
+
+  # TRA_ESQUEM
+  if ("TRA_ESQUEM" %in% variables_names) {
+    data$TRA_ESQUEM <- as.numeric(levels(data$TRA_ESQUEM))[data$TRA_ESQUEM]
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 1] <- "Infec\u00e7\u00f5es por Pv com Cloroquina em 3 dias e Primaquina em 7 dias"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 2] <- "Infec\u00e7\u00f5es por Pf com Quinina em 3 dias + Doxiciclina em 5 dias + primaquina no 6o dia"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 3] <- "Infec\u00e7\u00f5es mistas por Pv + Pf com Mefloquina em dose \u00fanica e primaquina em 7 dias"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 4] <- "Infec\u00e7\u00f5es por Pm com cloroquina em 3 dias"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 5] <- "Infec\u00e7\u00f5es por Pv em crian\u00e7as apresentando v\u00f4mitos, com c\u00e1psulas retais de artesunato em 4 dias e Primaquina em 7 dias"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 6] <- "Infec\u00e7\u00f5es por Pf com Mefloquina em dose \u00fanica e primaquina no segundo dia"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 7] <- "Infec\u00e7\u00f5es por Pf com Quinina em 7 dias"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 8] <- "Infec\u00e7\u00f5es por Pf de crian\u00e7as com c\u00e1psulas retais de artesunato em 4 dias e dose \u00fanica de Mefloquina no 3o dia e Primaquina no 5o dia"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 9] <- "Infec\u00e7\u00f5es mistas por Pv + Pf com Quinina em 3 dias, doxiciclina em 5 dias e Primaquina em 7 dias"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 10] <- "Preven\u00e7\u00e3o de reca\u00edda da mal\u00e1ria por Pv com Cloroquina em dose \u00fanica semanal durante 3 meses"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 11] <- "Mal\u00e1ria grave e complicada"
+    data$TRA_ESQUEM[data$TRA_ESQUEM == 99] <- "Outro esquema utilizado (por m\u00e9dico)"
+    data$TRA_ESQUEM <- factor(data$TRA_ESQUEM)
+  }
+
+  # Purge levels
+  data <- droplevels(data)
+
+  # Unescape unicode characters
+  data <- as.data.frame(lapply(X = data, FUN = stringi::stri_unescape_unicode))
+
+  # Return
+  return(data)
+}
diff --git a/man/process_sinan_dengue.Rd b/man/process_sinan_dengue.Rd
index 697c5e8..d4408bf 100644
--- a/man/process_sinan_dengue.Rd
+++ b/man/process_sinan_dengue.Rd
@@ -21,7 +21,6 @@ This function processes SINAN Dengue variables retrieved by \code{fetch_datasus(
 \dontrun{
 df <- fetch_datasus(year_start = 2016, year_end = 2016,
 uf = "RJ", information_system = "SINAN-DENGUE-FINAL")
-df <- process_sinan_dengue(year_start = 2016, year_end = 2016, uf = "RJ")
 df_a <- process_sinan_dengue(df)
 df_b <- process_sinan_dengue(df, municipality_data = FALSE)
 }
diff --git a/man/process_sinan_malaria.Rd b/man/process_sinan_malaria.Rd
new file mode 100644
index 0000000..23f8edc
--- /dev/null
+++ b/man/process_sinan_malaria.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/process_sinan_malaria.R
+\name{process_sinan_malaria}
+\alias{process_sinan_malaria}
+\title{Process SINAN Malaria variables from DataSUS}
+\usage{
+process_sinan_malaria(data, municipality_data = TRUE)
+}
+\arguments{
+\item{data}{\code{data.frame} created by \code{fetch_datasus()}.}
+
+\item{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.}
+}
+\description{
+\code{process_sinan_malaria} processes SINAN Malaria variables retrieved by \code{fetch_datasus()}.
+}
+\details{
+This function processes SINAN Malaria variables retrieved by \code{fetch_datasus()}, informing labels for categoric variables including NA values.
+}
+\examples{
+\dontrun{
+df <- fetch_datasus(year_start = 2016, year_end = 2016,
+uf = "RJ", information_system = "SINAN-MALARIA-FINAL")
+df_a <- process_sinan_malaria(df)
+df_b <- process_sinan_malaria(df, municipality_data = FALSE)
+}
+}

From 04b896aa0c8c9663b072da6a3c82e8811d055f31 Mon Sep 17 00:00:00 2001
From: rfsaldanha <rfsaldanha@gmail.com>
Date: Tue, 14 Sep 2021 19:43:44 -0300
Subject: [PATCH 3/5] Test SINAN Malaria

---
 tests/testthat/test-sinan-malaria.R | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 tests/testthat/test-sinan-malaria.R

diff --git a/tests/testthat/test-sinan-malaria.R b/tests/testthat/test-sinan-malaria.R
new file mode 100644
index 0000000..61c098e
--- /dev/null
+++ b/tests/testthat/test-sinan-malaria.R
@@ -0,0 +1,9 @@
+test_that("sinan-malaria-final at 2016", {
+  sinan_malaria_final <- fetch_datasus(year_start = 2016, year_end = 2016, uf = "AM", information_system = "SINAN-MALARIA-FINAL")
+  sinan_malaria_final <- process_sinan_malaria(sinan_malaria_final, municipality_data = TRUE)
+})
+
+test_that("sinan-malaria-preliminar at 2020", {
+  sinan_malaria_preliminar <- fetch_datasus(year_start = 2020, year_end = 2020, uf = "AM", information_system = "SINAN-MALARIA-PRELIMINAR")
+  sinan_malaria_preliminar <- process_sinan_malaria(sinan_malaria_preliminar, municipality_data = TRUE)
+})

From bc9526d358945ef69670bdc6251a3f5c9beac9fb Mon Sep 17 00:00:00 2001
From: rfsaldanha <rfsaldanha@gmail.com>
Date: Tue, 14 Sep 2021 19:44:33 -0300
Subject: [PATCH 4/5] Increment version number

---
 DESCRIPTION | 2 +-
 NEWS.md     | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index fff08d2..bda52aa 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: microdatasus
 Title: Download and preprocess DataSUS files
-Version: 1.3.1
+Version: 1.4.0
 Authors@R: 
     person(given = "Raphael",
            family = "Saldanha",
diff --git a/NEWS.md b/NEWS.md
index 347daf7..ec62e58 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,6 @@
+# microdatasus 1.4.0
+* Download and preprocess SINAN Malaria files.
+
 # microdatasus 1.3.1
 * Update functions documentation.
 

From b596dd796aaa3d95df15d409727fc15111033a9b Mon Sep 17 00:00:00 2001
From: rfsaldanha <rfsaldanha@gmail.com>
Date: Tue, 14 Sep 2021 19:45:32 -0300
Subject: [PATCH 5/5] Update GitHub readme

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index b8b0863..82ee6d3 100644
--- a/README.md
+++ b/README.md
@@ -25,8 +25,8 @@ O manual para a utilização do pacote encontra-se na [Wiki do projeto](https://
 
 ### Sistemas de Informação em Saúde suportados
 
-* Download: SIM, SINASC, SIH, CNES, SIA, SINAN-DENGUE, SINAN-CHIKUNGUNYA, SINAN-ZIKA.
-* Pré-processamento: SIM, SINASC, SIH-RD, CNES-ST, CNES-PF, SINAN-DENGUE, SINAN-CHIKUNGUNYA, SINAN-ZIKA.
+* Download: SIM, SINASC, SIH, CNES, SIA, SINAN-DENGUE, SINAN-CHIKUNGUNYA, SINAN-ZIKA, SINAN-MALARIA.
+* Pré-processamento: SIM, SINASC, SIH-RD, CNES-ST, CNES-PF, SINAN-DENGUE, SINAN-CHIKUNGUNYA, SINAN-ZIKA, SINAN-MALARIA.
 
 ## Agradecimento