diff --git a/stop_sensor.R b/stop_sensor.R
index 1139eea..dfe0124 100644
--- a/stop_sensor.R
+++ b/stop_sensor.R
@@ -1,115 +1,94 @@
-stop_sensor<- function(enriched_data,
-                       date_range = NULL,
-                       weekday_choice = NULL,
-                       hour_choice = NULL,
-                       vacation_choice=NULL,
-                       holiday_choice=NULL,
-                       segments = NULL,
-                       uptime_choice=0.5,
-                       successive_day=2,
-                       remove_data=FALSE)
-{
-  # Input parameter :
-  #       - df_init : Initial Dataframe 
-  #       - uptime_choice : Real which define the quality of the data below which we do not need the data
-  #       - successive_day : Integer which define number of successives days above which we do not need the data
-  #       - remove_data : Booleen which choose if we keep the row or not
-  # Output parameter :
-  #       - df_fin : Dataframe
-  # Description :
-  # Remove hours where we have no information as the night or less than 10% of information
-  # Remove periods when the sensor did not work
-  #
-  # Packages :
-  #       - lubridate
-  #       - dplyr
-
-  #Filtrer les données en fonction des demandes de l'utilisateur
-  if(!is.null(segments))
-    {enriched_data<-enriched_data[enriched_data$segment_name==segments,]}
-    
-  if(!is.null(date_range))
-    {enriched_data<-enriched_data[enriched_data$day>=date[1] & enriched_data$day<= date[2],]}
+#' @description
+#' A short description...
+#' Retrieve hours with no data and replace incomplete data with NA,
+#'
+#'
+#' @param enriched_data enriched data.frame containing all the data for all your sensors
+#' @param date_range Date vector. example: c('2021-01-01','2022-01-01'). Full period if NULL (default).
+#' @param segments Character vector. Selected road segment, all if NULL (default).
+#' @param weekday Character vector. Weekday choosen. Default to the all week.
+#' @param uptime Real. Uptime choosen. Default to 0.5.
+#' @param successive_day Integer. Number of day choosen. Default to 2
+#' 
+#' @return enriched_data 
+#' @export
+#'
+#' @import dplyr
+#' @import lubridate
+#'
+#' @examples
+#' retrieve_missing_data(traffic)
+#' retrieve_missing_data(traffic,
+#'                       date_range = c('2022-07-01','2022-09-01'),
+#'                       segment = 'RteVitre-06',
+#'                       uptime_choice=0.3,
+#'                       successive_day=1)
 
-  if(length(enriched_data$car)==0){print("Aucune donnée sur la période sélectionnée")}
-  
-  else
-  {
-  #Créer une colonne pour avoir les saisons
-  enriched_data$date <- ymd_hms(enriched_data$date) #mettre au format date
-  enriched_data$season <- ifelse(month(enriched_data$date) %in% c(3,4,5), "Spring",
-                        ifelse(month(enriched_data$date) %in% c(6,7,8), "Summer",
-                               ifelse(month(enriched_data$date) %in% c(9,10,11), "Autumn", "Winter")))
-  
-  
-  # Remove hours with no information by season
-  df_season<-enriched_data %>% group_by(segment_id,season,hour) %>% summarise(condition=any(car!=0 & uptime>uptime_choice))
-  
-  enriched_data <- enriched_data %>%
-    semi_join(df_season %>% filter(condition), by = c("segment_id","season", "hour"))
+retrieve_missing_data<- function(enriched_data,
+                                 date_range = NULL,
+                                 segments = NULL,
+                                 uptime_choice=0.5,
+                                 successive_day=2)
+{
   
-  print(length(enriched_data$car))
+  if(!is.null(segments))
+  {enriched_data<-enriched_data[enriched_data$segment_name==segments,]}
   
-  #Périodes d'inactivités
-  rm<-c()
-  rm_fin<-c()
+  if(!is.null(date_range))
+  {enriched_data<-enriched_data[enriched_data$day>=date_range[1] & enriched_data$day<= date_range[2],]}
   
-  list_clear_data <- list()
-  seg_id<-unique(enriched_data$segment_id)
+  if(length(enriched_data$car)==0){stop("No data in the selectionned period")}
   
-  for(id in 1:length(seg_id))
+  else
   {
-    df_segment<-enriched_data[enriched_data$segment_id==seg_id[id],]
-    for(i in 1:length(df_segment$car))
-      { j=i
+    #Hours with no data
+    enriched_data$date <- ymd_hms(enriched_data$date) 
+    enriched_data$season <- ifelse(month(enriched_data$date) %in% c(3,4,5), "Spring",
+                                   ifelse(month(enriched_data$date) %in% c(6,7,8), "Summer",
+                                          ifelse(month(enriched_data$date) %in% c(9,10,11), "Autumn", "Winter")))
+
+    df_season<-enriched_data %>% group_by(segment_id,season,hour) %>% summarise(condition=any(car!=0 & uptime>uptime_choice))
+    
+    enriched_data <- enriched_data %>% semi_join(df_season %>% filter(condition), by = c("segment_id","season", "hour"))
+    
+    #Inactivity Period
+    enriched_data <- enriched_data %>%
+      mutate(
+        heavy_NA = heavy,
+        car_NA = car,
+        bike_NA = bike,
+        pedestrian_NA = pedestrian
+      )   
+
+    list_clear_data <- list()
+    seg_id<-unique(enriched_data$segment_id)
+    
+    for(id in 1:length(seg_id))
+    {
+      df_segment<-enriched_data[enriched_data$segment_id==seg_id[id],]
+      for(i in 1:length(df_segment$car))
+      {
+        j=i
         while ((df_segment$car[i]==0 | df_segment$uptime[i]<uptime_choice) & i<length(df_segment$car))
-          {rm<-c(rm,i)
-           i<-i+1}
-  
+          {i<-i+1}
+      
         diff_days<-abs(as.numeric(difftime(df_segment$day[i], df_segment$day[j], units = "days")))
-  
+      
         if(diff_days>successive_day)
-          {rm_fin<-c(rm_fin,rm)}
-  
-        rm<-c()}
-  
-        if (is.null(rm_fin))
-          {df_segment<-df_segment}
-  
-        else
-          { if(remove_data==TRUE)
-              {df_segment<-df_segment[-rm_fin,]}
+        {
+          df_segment <- df_segment %>%
+            mutate_at(vars(heavy_NA, car_NA, bike_NA,pedestrian_NA), ~ ifelse(row_number() %in% j:i, NA,.))
+        }
+      }
+      list_clear_data[[id]]<-df_segment
+    }
+      enriched_data<-list_clear_data[[1]]
     
-            else
-              { colonnes_voulues<-c('heavy','car','bike','pedestrian','heavy_lft','heavy_rgt','car_rgt','car_lft','bike_lft','bike_rgt','pedestrian_lft','pedestrian_rgt')
-                df_segment[rm_fin,colonnes_voulues]=NA}
-          }
-    list_clear_data[[id]]<-df_segment
-  }
-  df_fin<-list_clear_data[[1]]
-  
-  if(length(seg_id)>1)
-    {for(i in 2:length(seg_id))
-      {df_fin<-rbind(df_fin,list_clear_data[[i]])}}
-  
-  
-  #Filtration des données avec les demandes de l'utilisateur
-  
-  if(!is.null(weekday_choice))
-  {  
-    df_fin$weekday<-tolower(df_fin$weekday)
-    tolower(weekday_choice)
-    df_fin<-df_fin %>% filter(weekday %in% weekday_choice) }
-
-  if(!is.null(hour_choice))
-  {df_fin<-df_fin %>% filter(hour %in% hour_choice)}
-  
-  if(!is.null(vacation_choice))
-  {df_fin<-df_fin %>% filter(vacation %in% vacation_choice)}
-  
-  if(!is.null(holiday_choice))
-  {df_fin<-df_fin %>% filter(holiday %in% holiday_choice)}
-  
-  return(df_fin)
-  }
+    if(length(seg_id)>1)
+    {
+      for(i in 2:length(seg_id))
+      {enriched_data<-rbind(enriched_data,list_clear_data[[i]])}
+    }
+    }
+    return(enriched_data)
 }