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Update stop_sensor.R
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@cpetinon
cpetinon committed Jun 5, 2024
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Showing 1 changed file with 83 additions and 104 deletions.
187 changes: 83 additions & 104 deletions stop_sensor.R
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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)
}

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