library(tibble) # used to create tibbles
library(tidyr) # used to tidy up data
library(prettydoc) # document themes for R Markdown
library(DT) # used for displaying R data objects (matrices or data frames) as tables on HTML pages
library(lubridate) # used for date/time functions
library(magrittr) # used for piping
library(ggplot2) # used for data visualization
library(dplyr) # used for data manipulation

# Read data via the source data url provided above
AirBnB <- read.delim(url
                     ("http://databits.io/static_content/challenges/airbnb-user-pathways-challenge/airbnb_session_data.txt"), 
                     sep = "|", na.strings = 'NULL')

# Store data as tibble
AirB_tib <- as_tibble(AirBnB)

# Find total number of observations  
nrow(AirB_tib)

# Get variable names
names(AirB_tib)

# Get summary information on the AirBnB data set variables
summary(AirB_tib)

# Read source data
AirBnB <- read.delim(url
                     ("http://databits.io/static_content/challenges/airbnb-user-pathways-challenge/airbnb_session_data.txt"), 
                     sep = "|", na.strings = 'NULL')

# Store data as tibble
AirB_tib <- as_tibble(AirBnB)

# Separate into Device and Application columns for the "dim_device_app_combo" fields

AirB_tibClean <- AirB_tib %>% 
  separate(dim_device_app_combo, 
           into = c("Device", "Application"), sep = " - ") %>%
  separate(next_dim_device_app_combo,
           into = c("Next_Device", "Next_Application"), 
           sep = " - ") 

# Convert the start and end times from string to date/time format

AirB_tibClean$Start_Time <- ymd_hms(AirB_tibClean$ts_min)
AirB_tibClean$End_Time <- ymd_hms(AirB_tibClean$ts_max)

AirB_tibClean$Next_Start_Time <- ymd_hms(AirB_tibClean$next_ts_min)
AirB_tibClean$Next_End_Time <- ymd_hms(AirB_tibClean$next_ts_max)

# When we substract the difference of the times, the result is in seconds,
# so we divide by 60 to get the duration in minutes

AirB_tibClean$Duration <- 
  (AirB_tibClean$End_Time - AirB_tibClean$Start_Time) / 60

AirB_tibClean$Next_Duration <- 
  (AirB_tibClean$Next_End_Time - AirB_tibClean$Next_Start_Time) / 60

# Read first 8 rows of the cleaned data set

datatable(head(AirB_tibClean, 8),options = list(scrollX=TRUE, pageLength=4))

# Get summary statistics for each variable
summary(AirB_tibClean)

# Display structure of cleaned data set 
str(AirB_tibClean)

# Average duration of each session

mean(AirB_tibClean$Duration)

# Show number of sessions by device, sorted from most used to least used
AirB_Dev <- AirB_tibClean %>% group_by(Device) %>% count(Device, sort = TRUE)

# Show top 3 devices used to access the website
datatable(head(AirB_Dev, 3))

# Show number of sessions by application, sorted from most used to least used
AirB_App <- AirB_tibClean %>% group_by(Application) %>% count(Application, sort = TRUE)

# Show all the devices used to access the website
datatable(AirB_App)

# Create a function to display a barchart of the number of sessions by application, based on the chosen device

DeviceChart <- function(selectdevice) {
  AirBDevice <- AirB_tibClean %>% filter(Device == selectdevice) %>% 
    group_by(Application) %>% count(Application, sort = TRUE)
  
  ggplot(AirBDevice, 
         aes(x = Application, y = n, fill = Application)) +
    geom_bar(stat="identity") + 
    labs(title = "Total Sessions by Application", 
         x = "Application", y = "Number of Sessions") +
    theme(legend.position="none", panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(), 
          axis.line = element_line(colour = "black"))
}

# Display barchart of Total Sessions by application for the top three most used devices
DeviceChart('iPhone')

DeviceChart('Android Phone')

DeviceChart('Desktop')

AirB_Visit <- AirB_tibClean %>% 
  group_by(id_visitor) %>%
  count(id_visitor, sort = TRUE)

ggplot(AirB_Visit, aes(n)) + 
  geom_density(kernel = "gaussian", color = "red") + 
  labs(title = "Frequency Distribution of Visits per User", 
       x = "Number of Visits", y = "Frequency") + 
  theme(legend.position="none", 
        panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
        axis.line = element_blank())

# Create table showing the total number of searches, messages, and bookings made by each unique frequent visitor, where a frequent visitor is someone who visited the site at least 10 times

ActionByVisitor <- AirB_tibClean %>% group_by(id_visitor) %>% filter(n() >= 10) %>% summarize(Search = sum(did_search), Message = sum(sent_message), Booking = sum(sent_booking_request))

# See if there are any patterns between number of searches made and number of messages sent

ggplot(ActionByVisitor, aes(Search, Message)) + 
  geom_jitter(color = 'purple') +
  labs(title = "Actions Made by Frequent Users") + 
  theme(legend.position="none", 
        panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
        axis.line = element_blank())

# See if there are any patterns between number of searches made and number of bookings placed

ggplot(ActionByVisitor, aes(Search, Booking)) + 
  geom_jitter(color = 'blue') +
  labs(title = "Actions Made by Frequent Users") + 
  theme(legend.position="none", 
        panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
        axis.line = element_blank())

# See if there are any patterns between number of messages made and number of bookings placed

ggplot(ActionByVisitor, aes(Message, Booking)) + 
  geom_jitter(color = 'green') +
  labs(title = "Actions Made by Frequent Users") + 
  theme(legend.position="none", 
        panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
        axis.line = element_blank())