preprocessing3.R

### Author Information #####
### Coded by Rischan Mafrur ###
### 01/01/2015 ###
### For Thesis Purpose ###
### Language : R ###
### Dataset : Private Data ###
### Function of preprocessing 3 are : 
# 1. Some of data such as Wi-Fi, Bluetooth, in the same time has many of values.
#    So, we have to aggreagte by time and make in the one row value. 
# 2. For research purpose, I round the time to the nearest time.
# 3. In this case because we want to developing human behavior model, we tried to remove
#    week days. Usually people have similar activities in work days. 


library(dplyr)


# list.files()

#removing duplicate data
remove_duplicate <- function(data){
  return (data[!duplicated(data),])
}


f_df_wifi <- function(df_wifi){
  library(dplyr)
  new_df_wifi <- df_wifi %>% group_by(time) %>% summarise(type=type[1], value2=value2[1], value3=value3[1], value1=toString(value1))
  df_wifi <- new_df_wifi[c(1,2,5,3,4)]
  
  return (df_wifi)
}

f_df_screen <- function(df_screen){
  library(dplyr)
  new_df_screen <- df_screen %>% group_by(time) %>% summarise(type=type[1], value2=value2[1], value3=value3[1], value1=toString(value1))
  df_screen <- new_df_screen[c(1,2,5,3,4)]
  
  return (df_screen)
}

f_df_bluetooth <- function(df_bluetooth){
  library(dplyr)
  new_df_bluetooth <- df_bluetooth %>% group_by(time) %>% summarise(type=type[1], value2=value2[1], value3=value3[1], value1=toString(value1))
  df_bluetooth <- new_df_bluetooth[c(1,2,5,3,4)]
  return (df_bluetooth)
}




f_df_activity <- function(df_activity){
  library(dplyr)
  new_df_activity <- df_activity %>% group_by(time) %>% summarise(type=type[1], value2=value2[1], value3=value3[1], value1=toString(value1))
  df_activity <- new_df_activity[c(1,2,5,3,4)]
  
  return (df_activity)
}

f_df_runapps <- function(df_runapps){
  library(dplyr)
  new_df_runapps <- df_runapps %>% group_by(time) %>% summarise(type=type[1], value2=value2[1], value3=value3[1], value1=toString(value1))
  df_runapps <- new_df_runapps[c(1,2,5,3,4)]
  
  return (df_runapps)
}





f_preprocessing3 <- function(file){
  df <- NULL
  df_wifi <- NULL
  df_screen <- NULL
  df_bluetooth <- NULL
  df_activity <- NULL
  df_runapps <- NULL
  df_battery <- NULL
  df_location <- NULL
  df_call <- NULL
  df_sms <- NULL

  df <- read.csv(file)
  df <- df[c(2,3,4,5,6)]
  #removing duplicated data
  df <- remove_duplicate(df)
  
  df_wifi <- subset(df, df$type=="wifi")
  df_wifi <- subset(df_wifi, df_wifi$value1 !="")
  df_wifi <- f_df_wifi(df_wifi)
  
  df_screen <- subset(df, df$type=="screen")
  df_screen <- subset(df_screen, df_screen$value1=="FALSE")
  df_screen$value1 <- "OFF"
  df_screen <- f_df_screen(df_screen)
  
  
  df_bluetooth <- subset(df, df$type=="bluetooth")
  df_bluetooth <- subset(df_bluetooth, df_bluetooth$value1!="NULL")
  df_bluetooth <- f_df_bluetooth(df_bluetooth)
  
  
  df_activity <- subset(df, df$type=="activity")
  df_activity <- subset(df_activity, df_activity$value1!="none")
  df_activity <- f_df_activity(df_activity)
  
  
  df_runapps <- subset(df, df$type=="runapps")
  df_runapps <- f_df_runapps(df_runapps)
  
  df_battery <- subset(df, df$type=="battery")
  df_battery <- subset(df_battery, df_battery$value1!="discharging")
  
  
  df_location <- subset(df, df$type=="location")
  
  df_call <- subset(df, df$type=="call")
  df_call$value1 <- paste(df_call$value2, df_call$value1)
  
  df_sms <- subset(df, df$type=="sms")
  df_sms$value1 <- paste(df_sms$value2, df_sms$value1)
  
  
  df <- NULL
  df <- rbind(df_activity,df_battery,df_bluetooth,df_call,df_location,df_runapps,df_screen,df_sms,df_wifi)
  
  df <- df[order(df$time),]
  
  ## remove weekdays
  ## df <- df[!weekdays(as.Date(df$time)) %in% c("Saturday", "Sunday"),]
  
  ## round time to nearest hour
  df$HP <- format(round(as.POSIXct(df$time), units="hours"),"%H:%M")
  df$Weekday <- weekdays(as.Date(df$time))
  
  df <- df[,c(1,7,6,2,3)]
  names(df) <- c("Timestamp","Weekday","HP","Sensor Name","Sensor Value")
  df$Timestamp <- format(as.POSIXct(df$Timestamp), "%m-%d-%Y %H:%M:%S")
  
  return (df)
  
}

#setwd("D:/DATA")
file_list <- list.files("D:/DATA/output2", full.names = TRUE)

for (file in file_list){
  file_proc <- f_preprocessing3(file)
  cat(sprintf("Storing data %s to csv file.......",file))
  cat("\n")
  new_path <- gsub('output2', 'output3', file)
  write.csv(file_proc, sprintf("%s",new_path),row.names=FALSE)
}


# # 
# # # 
# # # 
# 
# View(df)

# 
# setwd("D:/DATA/output2")
# df <- read.csv("ENFP_0719.csv")
# #head(df)
# df <- df[c(2,3,4,5,6)]
# #removing duplicated data
# df <- remove_duplicate(df)
# 
# df_wifi <- subset(df, df$type=="wifi")
# df_wifi <- subset(df_wifi, df_wifi$value1 !="")
# df_wifi <- f_df_wifi(df_wifi)
# 
# df_screen <- subset(df, df$type=="screen")
# df_screen <- subset(df_screen, df_screen$value1=="FALSE")
# df_screen$value1 <- "OFF"
# df_screen <- f_df_screen(df_screen)
# 
# 
# df_bluetooth <- subset(df, df$type=="bluetooth")
# df_bluetooth <- subset(df_bluetooth, df_bluetooth$value1!="NULL")
# df_bluetooth <- f_df_bluetooth(df_bluetooth)
# 
# 
# df_activity <- subset(df, df$type=="activity")
# df_activity <- subset(df_activity, df_activity$value1!="none")
# df_activity <- f_df_activity(df_activity)
# 
# 
# df_runapps <- subset(df, df$type=="runapps")
# df_runapps <- f_df_runapps(df_runapps)
# 
# df_battery <- subset(df, df$type=="battery")
# df_battery <- subset(df_battery, df_battery$value1!="discharging")
# 
# 
# df_location <- subset(df, df$type=="location")
# 
# df_call <- subset(df, df$type=="call")
# df_call$value1 <- paste(df_call$value2, df_call$value1)
# 
# df_sms <- subset(df, df$type=="sms")
# df_sms$value1 <- paste(df_sms$value2, df_sms$value1)
# 
# 
# df <- NULL
# df <- rbind(df_activity,df_battery,df_bluetooth,df_call,df_location,df_runapps,df_screen,df_sms,df_wifi)
# 
# df <- df[order(df$time),]
# 
# ## remove weekdays
# df <- df[!weekdays(as.Date(df$time)) %in% c("Saturday", "Sunday"),]
# 
# ## round time to nearest hour
# df$HP <- format(round(as.POSIXct(df$time), units="hours"),"%k:%M")
# df$Weekday <- weekdays(as.Date(df$time))
# 
# df <- df[,c(1,7,6,2,3)]
# names(df) <- c("Timestamp","Weekday","HP","Sensor Name","Sensor Value")
# 
# View(df)
# 
# #format(as.POSIXct(df$Timestamp), "%m/%d/%Y %k:%M")
# 
# df$Timestamp <- format(as.POSIXct(df$Timestamp), "%m-%d-%Y %H:%M:%S")
# 
# 
# write.csv(df, "data.csv",row.names=FALSE)




# 
# head(df)

# round.POSIXct <- function(x, units = c("mins", "5 mins", "10 mins", "15 mins", "quarter hours", "30 mins", "half hours", "hours")){
#   if(is.numeric(units)) units <- as.character(units)
#   units <- match.arg(units)
#   r <- switch(units,
#               "mins" = 60,
#               "5 mins" = 60*5,
#               "10 mins" = 60*10,
#               "15 mins"=, "quarter hours" = 60*15,
#               "30 mins"=, "half hours" = 60*30,
#               "hours" = 60*60
#   )
#   H <- as.integer(format(x, "%H")
#                   M <- as.integer(format(x, "%M"))
#                   S <- as.integer(format(x, "%S"))
#                   D <- format(x, "%Y-%m-%d")
#                   secs <- 3600*H + 60*M + S
#                   as.POSIXct(round(secs/r)*r, origin=D)
# }
# 
# (x <- Sys.time() + 1:10*60)
# round(x, "5 mins")
# round(x, 5)
# round(x, "10 mins")
# round(x, "quarter")
# round(x, "15 mins")

# 
# 
# head(df)
# ## round time to nearest hour
# df$time <- round(as.POSIXct(df$time), units="hours")
# ## remove weekdays
# x <- df[!weekdays(as.Date(df$time)) %in% c("Saturday", "Sunday"),]
# 
# 
# #write to csv file
# write.csv(df, "data.csv")