A. Load to memory all neccesary packages

library(tidyverse)
library(ggplot2)
library(dplyr)
library(DT)
library(shiny)
library(plotly)
library(dygraphs)
library(ggiraph)
library(tmaptools)
library(ggalluvial)
library(viridis)

getwd()

## [1] "C:/Users/Adinda Rizky Herawat/Dropbox/Fall Semester 2020_2021/3. Visualization in R/Project"

B. Set language

Sys.setenv(LANG = "en")

C. Set default language of date

Sys.setlocale("LC_TIME", "C")

## [1] "C"

D. Loading data

#load data
setwd("C:/Users/Adinda Rizky Herawat/Dropbox/Fall Semester 2020_2021/3. Visualization in R/Project")
booking <- read.csv("hotel_bookings.csv")

E. Data description

1. Description

This dataset consist of 119,390 observations and 32 variables from kaggle (https://www.kaggle.com/jessemostipak/hotel-booking-demand). This dataset is about hotel booking information (city and resort hotel) such as room type, country, number of adults and children, number of staying and etc. Aim of this project, predicting average daily rates hotels based on some variables with the best regression methods.

Below is the explanation about our variables

Hotel H1 = Resort Hotel, H2 = City Hotel
is_canceled Value indicating if the booking was canceled (1) or not (0)
lead_time Number of days that elapsed between the entering date of the booking into the PMS and the arrival date
arrival_date_year Year of arrival date
arrival_date_month Month of arrival date
arrival_date_week_number Week number of year for arrival date
stays_in_weekend Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
adults Number of adults
children Number of children
babies Number of babies
meal Type of meal booked. Categories are presented in standard hospitality meal packages: Undefined, SC, BB, FB, HB
Country of origin Countries in below are represented by categories there are length(unique(hotel$country))
market_segment Market segment designation. In categories the term “TA = Travel Agents” and “TO = Tour Operators”
distribution_channel Market segment designation. In categories the term “TA = Travel Agents” and “TO = Tour Operators”
is_repeated_guest Value indicating if the booking name was from repeated guest (1) or not (0)
previous_cancellation Number of previous bookings that were cancelled by the customer prior to the current booking
previous_booking Number of previous booking not cancelled by the customer prior to the current booking
reserved_room_type Code of room type reserved. Code is presented instead of designation for anonymity reasons
assigned_room_type Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g.overbooking) or by customer request. Code is presented instead of designation for anonymity reasons.
booking_changes Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation
deposit_type Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories: No Deposit, Refundable and Non Refund.
agent ID of the travel agency that made the booking
company ID of the company/entity that made the booking or responsible for paying the
booking. ID is presented instead of designation for anonymity reasons
days_in_waiting_list Number of days the booking was in the waiting list before it was confirmed to the customer
customer_type Type of booking, assuming one of four categories:
1. Contract - when the booking has an allotment or other type of contract
  associated to it;
2. Group - when the booking is associated to a group;
3. Transient - when the booking is not part of a group or contract, and is not associated to other transient booking;
4. Transient-party - when the booking is transient, but is associated to at least other transient booking
adr Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights
required_car_parking_spaces Number of car parking spaces required by the customer
total_of_special_requests Number of special requests made by the customer (e.g. twin bed or high floor)
reservation_status Reservation last status, assuming one of three categories: Canceled - booking was canceled by the customer; Check-Out and No-Show
reservation_status_date Date at which the last status was set. This variable can be used in conjunction with the ReservationStatus to understand when was the booking canceled or when did the customer checked-out of the hotel

2. Initial descriptive statistic

head(booking) %>%
      knitr::kable(caption = "Booking Dataset") %>%
      kableExtra::kable_styling(bootstrap_options = "striped", full_width = FALSE)

Booking Dataset
hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	meal	country	market_segment	distribution_channel	reserved_room_type	assigned_room_type	booking_changes	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
Resort Hotel	342	2015	July	27	1	0	2	BB	PRT	Direct	Direct	C	C	3	No Deposit	NULL	NULL	Transient	0	0	Check-Out	2015-07-01
Resort Hotel	737	2015	July	27	1	0	2	BB	PRT	Direct	Direct	C	C	4	No Deposit	NULL	NULL	Transient	0	0	Check-Out	2015-07-01
Resort Hotel	7	2015	July	27	1	1	1	BB	GBR	Direct	Direct	A	C	0	No Deposit	NULL	NULL	Transient	75	0	Check-Out	2015-07-02
Resort Hotel	13	2015	July	27	1	1	1	BB	GBR	Corporate	Corporate	A	A	0	No Deposit	304	NULL	Transient	75	0	Check-Out	2015-07-02
Resort Hotel	14	2015	July	27	1	2	2	BB	GBR	Online TA	TA/TO	A	A	0	No Deposit	240	NULL	Transient	98	1	Check-Out	2015-07-03
Resort Hotel	14	2015	July	27	1	2	2	BB	GBR	Online TA	TA/TO	A	A	0	No Deposit	240	NULL	Transient	98	1	Check-Out	2015-07-03

#exploring data

#head(booking)

#tail(booking)

summary(booking)

##     hotel            is_canceled       lead_time   arrival_date_year
##  Length:119390      Min.   :0.0000   Min.   :  0   Min.   :2015     
##  Class :character   1st Qu.:0.0000   1st Qu.: 18   1st Qu.:2016     
##  Mode  :character   Median :0.0000   Median : 69   Median :2016     
##                     Mean   :0.3704   Mean   :104   Mean   :2016     
##                     3rd Qu.:1.0000   3rd Qu.:160   3rd Qu.:2017     
##                     Max.   :1.0000   Max.   :737   Max.   :2017     
##                                                                     
##  arrival_date_month arrival_date_week_number arrival_date_day_of_month
##  Length:119390      Min.   : 1.00            Min.   : 1.0             
##  Class :character   1st Qu.:16.00            1st Qu.: 8.0             
##  Mode  :character   Median :28.00            Median :16.0             
##                     Mean   :27.17            Mean   :15.8             
##                     3rd Qu.:38.00            3rd Qu.:23.0             
##                     Max.   :53.00            Max.   :31.0             
##                                                                       
##  stays_in_weekend_nights stays_in_week_nights     adults      
##  Min.   : 0.0000         Min.   : 0.0         Min.   : 0.000  
##  1st Qu.: 0.0000         1st Qu.: 1.0         1st Qu.: 2.000  
##  Median : 1.0000         Median : 2.0         Median : 2.000  
##  Mean   : 0.9276         Mean   : 2.5         Mean   : 1.856  
##  3rd Qu.: 2.0000         3rd Qu.: 3.0         3rd Qu.: 2.000  
##  Max.   :19.0000         Max.   :50.0         Max.   :55.000  
##                                                               
##     children           babies              meal             country         
##  Min.   : 0.0000   Min.   : 0.000000   Length:119390      Length:119390     
##  1st Qu.: 0.0000   1st Qu.: 0.000000   Class :character   Class :character  
##  Median : 0.0000   Median : 0.000000   Mode  :character   Mode  :character  
##  Mean   : 0.1039   Mean   : 0.007949                                        
##  3rd Qu.: 0.0000   3rd Qu.: 0.000000                                        
##  Max.   :10.0000   Max.   :10.000000                                        
##  NA's   :4                                                                  
##  market_segment     distribution_channel is_repeated_guest
##  Length:119390      Length:119390        Min.   :0.00000  
##  Class :character   Class :character     1st Qu.:0.00000  
##  Mode  :character   Mode  :character     Median :0.00000  
##                                          Mean   :0.03191  
##                                          3rd Qu.:0.00000  
##                                          Max.   :1.00000  
##                                                           
##  previous_cancellations previous_bookings_not_canceled reserved_room_type
##  Min.   : 0.00000       Min.   : 0.0000                Length:119390     
##  1st Qu.: 0.00000       1st Qu.: 0.0000                Class :character  
##  Median : 0.00000       Median : 0.0000                Mode  :character  
##  Mean   : 0.08712       Mean   : 0.1371                                  
##  3rd Qu.: 0.00000       3rd Qu.: 0.0000                                  
##  Max.   :26.00000       Max.   :72.0000                                  
##                                                                          
##  assigned_room_type booking_changes   deposit_type          agent          
##  Length:119390      Min.   : 0.0000   Length:119390      Length:119390     
##  Class :character   1st Qu.: 0.0000   Class :character   Class :character  
##  Mode  :character   Median : 0.0000   Mode  :character   Mode  :character  
##                     Mean   : 0.2211                                        
##                     3rd Qu.: 0.0000                                        
##                     Max.   :21.0000                                        
##                                                                            
##    company          days_in_waiting_list customer_type           adr         
##  Length:119390      Min.   :  0.000      Length:119390      Min.   :  -6.38  
##  Class :character   1st Qu.:  0.000      Class :character   1st Qu.:  69.29  
##  Mode  :character   Median :  0.000      Mode  :character   Median :  94.58  
##                     Mean   :  2.321                         Mean   : 101.83  
##                     3rd Qu.:  0.000                         3rd Qu.: 126.00  
##                     Max.   :391.000                         Max.   :5400.00  
##                                                                              
##  required_car_parking_spaces total_of_special_requests reservation_status
##  Min.   :0.00000             Min.   :0.0000            Length:119390     
##  1st Qu.:0.00000             1st Qu.:0.0000            Class :character  
##  Median :0.00000             Median :0.0000            Mode  :character  
##  Mean   :0.06252             Mean   :0.5714                              
##  3rd Qu.:0.00000             3rd Qu.:1.0000                              
##  Max.   :8.00000             Max.   :5.0000                              
##                                                                          
##  reservation_status_date
##  Length:119390          
##  Class :character       
##  Mode  :character       
##                         
##                         
##                         
##

nrow(booking)

## [1] 119390

ncol(booking)

## [1] 32

F. Feature Engineering

#convert variables to factor type:
booking$is_canceled <- as.factor(booking$is_canceled)

booking$customer_type <- as.factor(booking$customer_type)

booking$hotel <- as.factor(booking$hotel)

booking$meal <- factor(booking$meal, levels = c("BB","FB","HB","SC","Undefined"),
                     ordered = FALSE)

booking$arrival_date_month <- factor(booking$arrival_date_month, 
                             levels=c("January","Februari", "March",                                                          "April","May","June",
                                      "July","August","September",
                                      "October","November","December"),
                                        ordered = FALSE)

booking$distribution_channel <- factor(booking$distribution_channel, levels=
                                       c("Direct","Corporate","TA/TO","Undefined", "GDS"),
                                     ordered=FALSE)

booking <- booking %>%
              mutate(quarter = case_when (
          arrival_date_week_number >= 1 & arrival_date_week_number<= 14 ~ 'Q1',
          arrival_date_week_number > 14 & arrival_date_week_number<= 27 ~ 'Q2',
          arrival_date_week_number > 27 & arrival_date_week_number<= 40 ~ 'Q3',
          arrival_date_week_number > 40 & arrival_date_week_number<= 53 ~ 'Q4'))


booking$quarter <- factor(booking$quarter, levels=c("Q1","Q2","Q3","Q4"), ordered=TRUE)


booking$market_segment <- factor(booking$market_segment, levels = c(
                 "Direct","Corporate","Online TA","Offline 
                 TA/TO","Complementary","Groups","Undefined","Aviation" ), ordered = FALSE)

#Convert the location
booking <- booking %>%
              mutate(country_name = case_when (
              country == 'PRT' ~ 'Portugal',
              country == 'GBR' ~ 'United Kingdom',
              country == 'USA' ~ 'United States of America',
              country == 'ESP' ~ 'Spain',
              country == 'IRL' ~ 'Ireland',
              country == 'FRA' ~ 'France',
              country == 'ROU' ~ 'Romania',
              country == 'NOR' ~ 'Norway',
              country == 'OMN' ~ 'Oman',
              country == 'ARG' ~ 'Argentina',
              country == 'POL' ~ 'Poland',
              country == 'DEU' ~ 'Germany',
              country == 'BEL' ~ 'Belgium',
              country == 'CHE' ~ 'Switzerland',
              country == 'CN' ~  'China',
              country == 'GRC' ~ 'Greece',
              country == 'ITA' ~ 'Italy',
              country == 'NLD' ~ 'Netherlands',
              country == 'DNK' ~ 'Denmark',
              country == 'RUS' ~ 'Russian Federation',
              country == 'SWE' ~ 'Sweden',
              country == 'AUS' ~ 'Australia',
              country == 'EST' ~ 'Estonia',
              country == 'CZE' ~ 'Czech Republic',
              country == 'BRA' ~ 'Brazil',
              country == 'FIN' ~ 'Finland',
              country == 'MOZ' ~ 'Mozambique',
              country == 'BWA' ~ 'Botswana',
              country == 'LUX' ~ 'Luxembourg',
              country == 'SVN' ~ 'Slovenia',
              country == 'ALB' ~ 'Albania',
              country == 'IND' ~ 'India',
              country == 'CHN' ~ 'China',
              country == 'MEX' ~ 'Mexico',
              country == 'MAR' ~ 'Morocco',
              country == 'UKR' ~ 'Ukraine',
              country == 'SMR' ~ 'San Marino',
              country == 'LVA' ~ 'Latvia',
              country == 'PRI' ~ 'Puerto Rico',
              country == 'SRB' ~ 'Serbia',
              country == 'CHL' ~ 'Chile',
              country == 'AUT' ~ 'Austria',
              country == 'BLR' ~ 'Belarus',
              country == 'LTU' ~ 'Lithuania',
              country == 'TUR' ~ 'Turkey',
              country == 'ZAF' ~ 'South Africa',
              country == 'AGO' ~ 'Angola',
              country == 'ISR' ~ 'Israel',
              country == 'CYM' ~ 'Cayman Islands',
              country == 'ZMB' ~ 'Zambia',
              country == 'CPV' ~ 'Cape Verde',
              country == 'ZWE' ~ 'Zimbabwe',
              country == 'DZA' ~ 'Algeria',
              country == 'KOR' ~ 'South Korea',
              country == 'CRI' ~ 'Costa Rica',
              country == 'HUN' ~ 'Hungary',
              country == 'ARE' ~ 'United Arab Emirates',
              country == 'TUN' ~ 'Tunisia',
              country == 'JAM' ~ 'Jamaica',
              country == 'HRV' ~ 'Croatia',
              country == 'HKG' ~ 'Hongkong',
              country == 'IRN' ~ 'Iran',
              country == 'GEO' ~ 'Georgia',
              country == 'AND' ~ 'Andorra',
              country == 'GIB' ~ 'Gibraltar',
              country == 'URY' ~ 'Uruguay',
              country == 'JEY' ~ 'Jersey',
              country == 'CAF' ~ 'Central African Republic',
              country == 'CYP' ~ 'Cyprus',
              country == 'COL' ~ 'Colombia',
              country == 'GGY' ~ 'Guernsey',
              country == 'KWT' ~ 'Kuwait',
              country == 'NGA' ~ 'Nigeria',
              country == 'MDV' ~ 'Maldives',
              country == 'VEN' ~ 'Venezuela',
              country == 'SVK' ~ 'Slovakia',
              country == 'FJI' ~ 'Fiji',
              country == 'KAZ' ~ 'Kazakhstan',
              country == 'PAK' ~ 'Pakistan',
              country == 'IDN' ~ 'Indonesia',
              country == 'LBN' ~ 'Lebanon',
              country == 'PHL' ~ 'Philippines',
              country == 'SEN' ~ 'Senegal',
              country == 'SYC' ~ 'Seychelles',
              country == 'AZE' ~ 'Azerbaijan',
              country == 'BHR' ~ 'Bahrain',
              country == 'NZL' ~ 'New Zealand',
              country == 'THA' ~ 'Thailand',
              country == 'DOM' ~ 'Dominican Republic',
              country == 'MKD' ~ 'Republic of Macedonia',
              country == 'MYS' ~ 'Malaysia',
              country == 'ARM' ~ 'Armenia',
              country == 'JPN' ~ 'Japan',
              country == 'LKA' ~ 'Sri Lanka',
              country == 'CUB' ~ 'Cuba',
              country == 'CMR' ~ 'Cameroon',
              country == 'BIH' ~ 'Bosnia and Herzegovina',
              country == 'MUS' ~ 'Mauritius',
              country == 'COM' ~ 'Comoros',
              country =='SUR' ~ 'Suriname',
              country == 'UGA' ~ 'Uganda',
              country == 'BGR' ~ 'Bulgaria',
              country == 'CIV' ~ 'Côte d Ivoire',
              country == 'JOR' ~ 'Jordan',
              country == 'SYR' ~ 'Syria',
              country == 'SGP' ~ 'Singapore',
              country == 'BDI' ~ 'Burundi',
              country == 'SAU' ~ 'Saudi Arabia',
              country == 'VNM' ~ 'Vietnam',
              country == 'PLW' ~ 'Palau',
              country == 'QAT' ~ 'Qatar',
              country == 'EGY' ~ 'Egypt',
              country == 'PER' ~ 'Peru',
              country == 'MLT' ~ 'Malta',
              country == 'MWI' ~ 'Malawi',
              country == 'ECU' ~ 'Ecuador',
              country == 'MDG' ~ 'Madagascar',
              country == 'ISL' ~ 'Iceland',
              country == 'UZB' ~ 'Uzbekistan',
              country == 'NPL' ~ 'Nepal',
              country == 'BHS' ~ 'Bahamas',
              country == 'MAC' ~ 'Macao',
              country == 'TGO' ~ 'Togo',
              country == 'TWN' ~ 'Taiwan',
              country == 'DJI' ~ 'Djibouti',
              country == 'STP' ~ 'Sao Tome and Principe',
              country == 'KNA' ~ 'Saint Kitts and Nevis',
              country == 'ETH' ~ 'Ethiopia',
              country == 'IRQ' ~ 'Iraq',
              country == 'HND' ~ 'Honduras',
              country == 'RWA' ~ 'Rwanda',
              country == 'KHM' ~ 'Cambodia',
              country == 'MCO' ~ 'Monaco',
              country == 'BGD' ~ 'Bangladesh',
              country == 'IMN' ~ 'Isle of Man',
              country == 'TJK' ~ 'Tajikistan',
              country == 'NIC' ~ 'Nicaragua',
              country == 'BEN' ~ 'Benin',
              country == 'VGB' ~ 'British Virgin Islands',
              country == 'TZA' ~ 'Tanzania',
              country == 'GAB' ~ 'Gabon',
              country == 'GHA' ~ 'Ghana',
              country == 'TMP' ~ 'East Timor',
              country == 'GLP' ~ 'Guadeloupe',
              country == 'KEN' ~ 'Kenya',
              country == 'LIE' ~ 'Liechtenstein',
              country == 'GNB' ~ 'Guinea-Bissau',
              country == 'MNE' ~ 'Montenegro',
              #country == 'UMI' ~ 'US Minor Outlying Islands',
              country == 'MYT' ~ 'Mayotte',
              country == 'FRO' ~ 'Faroe Islands',
              country == 'MMR' ~ 'Myanmar',
              country == 'PAN' ~ 'Panama',
              country == 'BFA' ~ 'Burkina Faso',
              country == 'LBY' ~ 'Libya',
              country == 'MLI' ~ 'Mali',
              country == 'NAM' ~ 'Namibia',
              country == 'BOL' ~ 'Bolivia',
              country == 'PRY' ~ 'Paraguay',
              country == 'BRB' ~ 'Barbados',
              country == 'ABW' ~ 'Aruba',
              country == 'AIA' ~ 'Anguilla',
              country == 'SLV' ~ 'El Salvador',
              country == 'DMA' ~ 'Dominica',
              country == 'PYF' ~ 'French Polynesia',
              country == 'GUY' ~ 'Guyana',
              country == 'LCA' ~ 'Saint Lucia',
              country == 'ATA' ~ 'Antarctica',
              country == 'GTM' ~ 'Guatemala',
              country == 'ASM' ~ 'American Samoa',
              country == 'MRT' ~ 'Mauritania',
              country == 'NCL' ~ 'New Caledonia',
              country == 'KIR' ~ 'Kiribati',
              country == 'SDN' ~ 'Sudan',
              country == 'ATF' ~ 'French Southern Territories',
              country == 'SLE' ~ 'Sierra Leone',
              country == 'LAO' ~ 'Lao PDR'))

booking <- booking %>%
              mutate(continent = case_when (
              country == 'PRT' ~ 'Europe & UK',
              country == 'GBR' ~ 'Europe & UK',
              country == 'USA' ~ 'North America',
              country == 'ESP' ~ 'Europe & UK',
              country == 'IRL' ~ 'Europe & UK',
              country == 'FRA' ~ 'Europe & UK',
              country == 'ROU' ~ 'Europe & UK',
              country == 'NOR' ~ 'Europe & UK',
              country == 'OMN' ~ 'Asia',
              country == 'ARG' ~ 'South America',
              country == 'POL' ~ 'Europe & UK',
              country == 'DEU' ~ 'Europe & UK',
              country == 'BEL' ~ 'Europe & UK',
              country == 'CHE' ~ 'Europe & UK',
              country == 'CN' ~  'Asia',
              country == 'GRC' ~ 'Europe & UK',
              country == 'ITA' ~ 'Europe & UK',
              country == 'NLD' ~ 'Europe & UK',
              country == 'DNK' ~ 'Europe & UK',
              country == 'RUS' ~ 'Europe & UK',
              country == 'SWE' ~ 'Europe & UK',
              country == 'AUS' ~ 'Australia/Oceania',
              country == 'EST' ~ 'Europe & UK',
              country == 'CZE' ~ 'Europe & UK',
              country == 'BRA' ~ 'South America',
              country == 'FIN' ~ 'Europe & UK',
              country == 'MOZ' ~ 'Africa',
              country == 'BWA' ~ 'Africa',
              country == 'LUX' ~ 'Europe & UK',
              country == 'SVN' ~ 'Europe & UK',
              country == 'ALB' ~ 'Europe & UK',
              country == 'IND' ~ 'Asia',
              country == 'CHN' ~ 'Asia',
              country == 'MEX' ~ 'North America',
              country == 'MAR' ~ 'Africa',
              country == 'UKR' ~ 'Europe & UK',
              country == 'SMR' ~ 'Europe & UK',
              country == 'LVA' ~ 'Europe & UK',
              country == 'PRI' ~ 'North America',
              country == 'SRB' ~ 'Europe & UK',
              country == 'CHL' ~ 'South America',
              country == 'AUT' ~ 'Europe & UK',
              country == 'BLR' ~ 'Europe & UK',
              country == 'LTU' ~ 'Europe & UK',
              country == 'TUR' ~ 'Europe & UK',
              country == 'ZAF' ~ 'Africa',
              country == 'AGO' ~ 'Africa',
              country == 'ISR' ~ 'Asia',
              country == 'CYM' ~ 'Europe & UK',
              country == 'ZMB' ~ 'Africa',
              country == 'CPV' ~ 'Africa',
              country == 'ZWE' ~ 'Africa',
              country == 'DZA' ~ 'Africa',
              country == 'KOR' ~ 'Asia',
              country == 'CRI' ~ 'North America',
              country == 'HUN' ~ 'Europe & UK',
              country == 'ARE' ~ 'Asia',
              country == 'TUN' ~ 'Africa',
              country == 'JAM' ~ 'North America',
              country == 'HRV' ~ 'Europe & UK',
              country == 'HKG' ~ 'Asia',
              country == 'IRN' ~ 'Asia',
              country == 'GEO' ~ 'Europe & UK',
              country == 'AND' ~ 'Europe & UK',
              country == 'GIB' ~ 'Europe & UK',
              country == 'URY' ~ 'South America',
              country == 'JEY' ~ 'Europe & UK',
              country == 'CAF' ~ 'Africa',
              country == 'CYP' ~ 'Europe & UK',
              country == 'COL' ~ 'South America',
              country == 'GGY' ~ 'Europe & UK',
              country == 'KWT' ~ 'Asia',
              country == 'NGA' ~ 'Africa',
              country == 'MDV' ~ 'Asia',
              country == 'VEN' ~ 'South America',
              country == 'SVK' ~ 'Europe & UK',
              country == 'FJI' ~ 'Australia/Oceania',
              country == 'KAZ' ~ 'Asia',
              country == 'PAK' ~ 'Asia',
              country == 'IDN' ~ 'Asia',
              country == 'LBN' ~ 'Asia',
              country == 'PHL' ~ 'Asia',
              country == 'SEN' ~ 'Africa',
              country == 'SYC' ~ 'Africa',
              country == 'AZE' ~ 'Europe & UK',
              country == 'BHR' ~ 'Asia',
              country == 'NZL' ~ 'Australia/Oceania',
              country == 'THA' ~ 'Asia',
              country == 'DOM' ~ 'North America',
              country == 'MKD' ~ 'Europe & UK',
              country == 'MYS' ~ 'Asia',
              country == 'ARM' ~ 'Asia',
              country == 'JPN' ~ 'Asia',
              country == 'LKA' ~ 'Asia',
              country == 'CUB' ~ 'North America',
              country == 'CMR' ~ 'Africa',
              country == 'BIH' ~ 'Europe & UK',
              country == 'MUS' ~ 'Africa',
              country == 'COM' ~ 'Africa',
              country =='SUR' ~ 'South America',
              country == 'UGA' ~ 'Africa',
              country == 'BGR' ~ 'Europe & UK',
              country == 'CIV' ~ 'Africa',
              country == 'JOR' ~ 'Asia',
              country == 'SYR' ~ 'Asia',
              country == 'SGP' ~ 'Asia',
              country == 'BDI' ~ 'Africa',
              country == 'SAU' ~ 'Asia',
              country == 'VNM' ~ 'Asia',
              country == 'PLW' ~ 'Australia/Oceania',
              country == 'QAT' ~ 'Asia',
              country == 'EGY' ~ 'Africa',
              country == 'PER' ~ 'South America',
              country == 'MLT' ~ 'Europe & UK',
              country == 'MWI' ~ 'Africa',
              country == 'ECU' ~ 'South America',
              country == 'MDG' ~ 'Africa',
              country == 'ISL' ~ 'Europe & UK',
              country == 'UZB' ~ 'Asia',
              country == 'NPL' ~ 'Asia',
              country == 'BHS' ~ 'North America',
              country == 'MAC' ~ 'Asia',
              country == 'TGO' ~ 'Africa',
              country == 'TWN' ~ 'Asia',
              country == 'DJI' ~ 'Africa',
              country == 'STP' ~ 'Africa',
              country == 'KNA' ~ 'North America',
              country == 'ETH' ~ 'Africa',
              country == 'IRQ' ~ 'Asia',
              country == 'HND' ~ 'North America',
              country == 'RWA' ~ 'Africa',
              country == 'KHM' ~ 'Asia',
              country == 'MCO' ~ 'Europe & UK',
              country == 'BGD' ~ 'Asia',
              country == 'IMN' ~ 'Europe & UK',
              country == 'TJK' ~ 'Asia',
              country == 'NIC' ~ 'North America',
              country == 'BEN' ~ 'Africa',
              country == 'VGB' ~ 'Europe & UK',
              country == 'TZA' ~ 'Africa',
              country == 'GAB' ~ 'Africa',
              country == 'GHA' ~ 'Africa',
              country == 'TMP' ~ 'Asia',
              country == 'GLP' ~ 'Europe & UK',
              country == 'KEN' ~ 'Africa',
              country == 'LIE' ~ 'Europe & UK',
              country == 'GNB' ~ 'Africa',
              country == 'MNE' ~ 'Europe & UK',
              country == 'UMI' ~ 'North America',
              country == 'MYT' ~ 'Africa',
              country == 'FRO' ~ 'Europe & UK',
              country == 'MMR' ~ 'Asia',
              country == 'PAN' ~ 'North America',
              country == 'BFA' ~ 'Africa',
              country == 'LBY' ~ 'North America',
              country == 'MLI' ~ 'Africa',
              country == 'NAM' ~ 'Africa',
              country == 'BOL' ~ 'South America',
              country == 'PRY' ~ 'South America',
              country == 'BRB' ~ 'North America',
              country == 'ABW' ~ 'South America',
              country == 'AIA' ~ 'Europe & UK',
              country == 'SLV' ~ 'North America',
              country == 'DMA' ~ 'North America',
              country == 'PYF' ~ 'Australia/Oceania',
              country == 'GUY' ~ 'South America',
              country == 'LCA' ~ 'North America',
              country == 'ATA' ~ 'Antartica',
              country == 'GTM' ~ 'North America',
              country == 'ASM' ~ 'Australia/Oceania',
              country == 'MRT' ~ 'Africa',
              country == 'NCL' ~ 'Australia/Oceania',
              country == 'KIR' ~ 'Australia/Oceania',
              country == 'SDN' ~ 'Africa',
              country == 'ATF' ~ 'Antartica',
              country == 'SLE' ~ 'Africa',
              country == 'LAO' ~ 'Asia'))


booking$continent <- factor(booking$continent, levels=c("Europe & UK","North America",
                                                        "Asia", 'South America', 
                                                    'Australia/Oceania', 'Antartica','Africa'), ordered=FALSE)

G. Visualisation

1. Customer type and cancellation status

A guest regardless customer type is more likely to book a city hotel because number of bookings for city hotel is much higher than resort hotel
- But city hotel booking is more likely to be canceled.
- In both hotels, transient customer type has the most highest booking number.

customer.type <- booking %>%
  dplyr::select(is_canceled, customer_type, hotel )%>%
  group_by(is_canceled, customer_type, hotel) %>%
  summarise(customer.count = n())

## `summarise()` regrouping output by 'is_canceled', 'customer_type' (override with `.groups` argument)

ggplot(data = customer.type, 
       aes( x = reorder(customer_type, customer.count),
            y = customer.count,
            fill = is_canceled)) +
  geom_histogram(stat = "identity", position = position_dodge(width = 1)) +
  facet_wrap(~hotel)+
  labs(title = 'Cancellation by Customer Type',
       subtitle = 'Based on number of customers',
       caption = 'Data Source: Kaggle competition')+
  xlab("Customer type") + 
  ylab("Number of Customers [in thousands]")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  ) +
  scale_y_continuous(breaks = seq(0,100000,by = 25000), 
                     labels = format(seq(0,100000,by=25000)/1000),
                     limits = c(0, 60000))+
  geom_text(stat = "identity",position = position_dodge(width = 1), hjust = -0.1, label.size = 0.05, angle = 90, aes(label = paste(round(customer.count/1000,1), "K")))+
  scale_fill_discrete(
    name = "Booking Status",
    breaks = c("0", "1"),
    labels = c("Cancelled", "Not Cancelled"))

## Warning: Ignoring unknown parameters: binwidth, bins, pad

## Warning: Ignoring unknown parameters: label.size

2. Revenue by month

As expected, City Hotel earn more than Resort hotel. Moreover, both hotels have highest income in Summer. This might be because people are less willing to travel during its winter season.

revenue <- booking %>%
  dplyr :: select(adr, hotel, arrival_date_month) %>% 
  group_by(arrival_date_month, hotel)%>%
  summarise(adr = sum(adr))

## `summarise()` regrouping output by 'arrival_date_month' (override with `.groups` argument)

class(revenue$adr)

## [1] "numeric"

#order month

order <- c ("January","February", "March", "April", "May", "June", "July", "August", "September" , "October", "November", "December" )

revenue$arrival_date_month <- factor(revenue$arrival_date_month,
                                     levels = order)

ggplot(data = revenue, 
       aes( x = arrival_date_month,
            y = adr,
            linetype = hotel,
            group = hotel,
            color = hotel)) +
  geom_line()+
  geom_point()+
labs(title = 'Revenues by Month',
       subtitle = 'Based on total of Average Daily Rate',
       caption = 'Data Source: Kaggle competition')+
  xlab("Month") + 
  ylab("Revenue [in thousands]")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  ) +
  scale_y_continuous(breaks = seq(0,1500000,by = 100000), 
                     labels = format(seq(0,1500000,by = 100000)/1000))

3. Interactive plot Lead Time vs Average Daily

Lead time vs Average Daily rate demonstrates that lead time doesnt impact the average revenue earn per rooms. The data is of Resort Hotel in 2015.

myplot <- ggplot(data = booking[booking$adr > 150 & booking$arrival_date_year ==  "2015" & booking$hotel ==  "Resort Hotel" & booking$customer_type == "Transient",], 
       aes( x = adr ,
            y = lead_time,
            color = factor(reserved_room_type)) ) +
  geom_point()+
labs(title = 'Lead Time vs Average Daily Rate',
       subtitle = 'Data in 2015, Resort Hotel',
       caption = 'Data Source: Kaggle competition')+
  xlab("Lead Time") + 
  ylab("Average Daily Rate (ADR)")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  ) +
  scale_color_discrete(
    name = "Reserved room type")
  

myplot_int <- myplot + geom_point_interactive(aes(tooltip = factor(market_segment),
                                                     data_id = factor(market_segment)), size = 2)

ggiraph(code = print(myplot_int), hover_css = "cursor:pointer;fill:yellow;stroke:red;",  zoom_max = 5,
        tooltip_offx = 20, tooltip_offy = -10)

4. Request by customer type

Number of requests is highest in July and August for both hotels and lowest in November, December and January.
- Transient customer type has the biggest number of request. This is because transient customer type has the most largest booking number and we are looking at the total number of requests

request <- booking %>%
  dplyr :: select(total_of_special_requests, customer_type, hotel, arrival_date_month) %>% 
  group_by(customer_type, hotel, arrival_date_month) %>%
  summarise(total_of_special_requests = sum(total_of_special_requests))

## `summarise()` regrouping output by 'customer_type', 'hotel' (override with `.groups` argument)

#order month
levels(as.factor(request$arrival_date_month))

##  [1] "January"   "Februari"  "March"     "April"     "May"       "June"     
##  [7] "July"      "August"    "September" "October"   "November"  "December"

request$arrival_date_month <- factor(request$arrival_date_month,
                                     levels = c ("January","February", "March", "April", "May", "June", "July", "August", "September" , "October", "November", "December" ))

ggplot(data = request, 
       aes( x = arrival_date_month,
            y = total_of_special_requests,
            fill = customer_type)) +
  geom_bar(stat = "identity") +
  facet_wrap(~hotel)+
  labs(title = 'Requests by Month',
       subtitle = 'Based on total number of requests by customer type',
       caption = 'Data Source: Kaggle competition')+
  xlab("Month") + 
  ylab("Number of Request [in thousands]")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  ) +
  guides(fill = guide_legend(title = "Customer Type",override.aes = aes(label = "")))

5. Average Daily rate by Hotel

Looking at the average daily rate distribution by hotel type, we can see that on average rooms revenue earned per number of rooms sold of city hotel is higher than resort hotel.

# where are the people coming from
booking_1 <- booking[booking$reservation_status == "Check-Out",]
# Subset the data to include the countries which has more than 1500 reservation request
# otherwise including all the country with few or occassional request to avoid the graph
# from being clumsy
sub_booking <- booking_1 %>% 
  group_by(country) %>% 
  filter(n() > 1500)

ggplot(sub_booking, aes(x = adr, fill = hotel, color = hotel)) + 
  geom_histogram(aes(y = ..density..), position = position_dodge(), binwidth = 20 ) +
  geom_density(alpha = 0.1) + 
  labs(title = "Average Daily rate by Hotel",
       caption = 'Data Source: Kaggle competition') +
 scale_color_brewer(palette = "Paired") + 
  xlab("Hotel Price") + 
  ylab("Count")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")) +
 theme(legend.position = "top")

6. Does hotel charge differently depending on deposit type?

dodge <- position_dodge(width = 0.4)

ggplot(data = sub_booking, 
       aes(x = hotel,
           y = adr,
           fill = factor(deposit_type))) +
  geom_violin(position = dodge) +
   geom_boxplot(position = dodge, width=0.1, color="black", alpha=0.2) +
  #facet_wrap(~ deposit_type)+
    labs(title = 'Average Daily Rate (ADR) by Hotel Type',
       subtitle = 'By Deposit type',
       caption = 'Data Source: Kaggle competition')+
  xlab("Hotel Type") + 
  ylab("Average Daily Rate (ADR)")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  )+
  scale_y_continuous(limits = c(0, 500))

## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

#+
  #scale_fill_discrete(
   # name = "Booking Status",
    #breaks = c("0", "1"),
    #labels = c("Cancelled", "Not Cancelled"))

7. Does hotel charge differently depending on number of special requests?

The average daily rate by number of request shows that there is unclear trend if hotels charge differently depending on number of special requests

dodge <- position_dodge(width = 0.4)

ggplot(data = sub_booking, 
       aes(x = hotel,
           y = adr,
           fill = factor(total_of_special_requests))) +
  geom_violin(position = dodge) +
   geom_boxplot(position = dodge, width=0.1, color="black", alpha=0.2) +
 # facet_wrap(~ customer_type)+
    labs(title = 'Average Daily Rate by Hotel Type',
       subtitle = 'By Customer Type',
       caption = 'Data Source: Kaggle competition')+
  xlab("Hotel Type") + 
  ylab("Average Daily Rate (ADR)")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  )+
  scale_y_continuous(limits = c(0, 500)) +
  scale_fill_discrete(
    name = "Number of requests",
    breaks = c("0", "1", "2", "3", "4", "5"),
    labels = c("No request", "1 request", "2 requests", "3 requests", "4 requests", "5 requests"))

## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

8. Does hotel charge differently depending on customer type?

City hotel earns more than resort hotel by each customer type
- Interestingly, transient customer type has the highest number of bookings but contract customer type has the highest average revenue income for the hotels.

dodge <- position_dodge(width = 0.4)

ggplot(data = sub_booking, 
       aes(x = factor(customer_type),
           y = adr,
           fill = factor(hotel))) +
  geom_violin(position = dodge) +
   geom_boxplot(position = dodge, width=0.1, color="black", alpha=0.2) +
 # facet_wrap(~ customer_type)+
    labs(title = 'Average Daily Rate by Hotel Type',
       subtitle = 'By Customer Type',
       caption = 'Data Source: Kaggle competition')+
  xlab("Hotel Type") + 
  ylab("Average Daily Rate (ADR)")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  )+
  scale_y_continuous(limits = c(0, 500))

## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

9. How many percentage of customers get the right room type as reserved?

We would like to examine how many percentage the customer checked-in with the same room type as they reserved.
- In City hotel about 91% of customer get the reserved room type. This number is 10 % lower in Resort hotel.

booking$right_room <- booking$reserved_room_type == booking$assigned_room_type
booking$right_room <- as.factor(booking$right_room)

right.room <- booking %>% 
  dplyr:: select(right_room, hotel) %>%
  group_by(hotel,right_room) %>%
  summarise(n = n()) %>%
  mutate( prob = n/sum(n) * 100)

## `summarise()` regrouping output by 'hotel' (override with `.groups` argument)

ggplot(data = right.room, 
       aes(x = hotel, 
           y = prob,
           fill = right_room))+
  geom_bar(stat = "identity", position = position_dodge(width = 1)) +
 # facet_wrap(~ customer_type)+
    labs(title = 'Do customer get the reserved room type ',
       subtitle = 'By reservation status',
       caption = 'Data Source: Kaggle competition')+
  ylab("% Customers")+
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.text=element_text(face = "bold"),
        axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
        panel.grid.minor = element_line(colour = "gray",linetype = "dotted"),
        panel.background=element_rect(fill = "white")
  ) +
    geom_text(stat = "identity",position = position_dodge(width = 1),vjust = -0.5,  label.size = 0.05, aes(label = paste(round(prob,2),"%"))) +
  scale_y_continuous(limits = c(0, 100))

## Warning: Ignoring unknown parameters: label.size

10. Where is the location of the hotel in the world?

Location of the hotel in the world, mostly hotel are in Europe area. It’s probably because number of visitors in Europe is the highest

#hotel_locations <- booking %>%
#                  group_by(continent, country_name, hotel, adr) %>%
#                  arrange(desc(adr)) %>%
#                  summarise(n=n()) %>% head(100)

#hotel_locations$loc <- geocode_OSM(hotel_locations$country_name)
#hotel_locations$lat <- hotel_locations$loc$lat
#hotel_locations$lon <- hotel_locations$loc$lon


#leaflet(hotel_locations) %>%
#  addTiles() %>%
#  addCircleMarkers(~lon, ~lat, label=~as.character(country_name),
#                   weight = 3, fillOpacity = 0.5, radius=4) 
#  addProviderTiles(providers$Stamen.TonerLite) %>%
#  addMiniMap(
#    toggleDisplay = TRUE,
#    tiles = providers$Stamen.TonerLite)

11. Where is the location of Top 50 The Most Expensive Hotel?

Top 50 the most expensive hotel are in Europe and UK

#top50 <- hotel_locations %>%
#          arrange(desc(adr))%>%
#        head(50) 

#city_hotel <- top50 %>% filter(hotel=="City Hotel")
#resort_hotel <- top50 %>% filter(hotel=="Resort Hotel")

#type_hotel <- rbind(city_hotel, resort_hotel)
#cof <- colorFactor(c("#ffa500", "#13ED3F"), domain=c("City Hotel", "Resort Hotel"))

#labels <- sprintf(
#  "<strong>%s</strong><br/>%g USD",
#  type_hotel$country_name, type_hotel$adr
#) %>% lapply(htmltools::HTML)


#leaflet(type_hotel) %>% 
#  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
#  addCircleMarkers(~lon, ~lat, weight = 3, radius=4, 
#                 color=~cof(hotel), stroke = F, fillOpacity = 0.5,
#                 label = labels) %>%
#  addLegend("bottomright", colors= c("#ffa500", "#13ED3F"), 
#            labels=c("City Hotel", "Resort Hotel"), title="Type of Hotel")

12. How about the distribution channel of the hotel?

As we can see here that the highest visitors in the hotel are in Europe & UK, but on the other hand other
continents only have few distribution channels. Mostly people book the hotel with travel agents. It shows with purple area

booking_distribution_channel <- booking %>%
                            group_by(arrival_date_week_number, distribution_channel,
                                     continent) %>%
                            drop_na() %>%
                            summarise(n=n())

## `summarise()` regrouping output by 'arrival_date_week_number', 'distribution_channel' (override with `.groups` argument)

ggplot(data=booking_distribution_channel, aes(x=arrival_date_week_number, y= n, 
                                 alluvium = distribution_channel)) +
  geom_alluvium(aes(fill=distribution_channel),
                alpha=.75, decreasing=FALSE) +
  scale_x_continuous(breaks=seq(1,52,13)) +
  theme_bw() +
  scale_fill_brewer(type = "qual", palette = "Dark2") +
  scale_color_brewer(type = "qual", palette = "Dark2") +
  facet_wrap(~ continent, scales = "fixed") +
  ggtitle("Distribution Channel In each Continent") +
  xlab("Arrival Date Week Number")+
  ylab("Number of Distribution Channel") +
  labs(fill = "Distribution Channel") +
  theme(plot.title = element_text(hjust=0.5, face="bold"),
        axis.title.x = element_text(hjust=0.5, face="bold"),
        axis.title.y = element_text(hjust=0.5, face="bold"))

13. How About the Lead Time of booking hotel based on continent?

Visitors prefer to have long lead time to book the hotel around July-September in Summer seasons, as we can see in the barplot here, also there is additional information about the continents of the visitors.

booking_month <- booking %>%
                  select(hotel, arrival_date_month, lead_time, 
                         arrival_date_year,continent) %>%
                  drop_na(continent, arrival_date_month) %>%
                  group_by(arrival_date_month, hotel, continent) %>%
                  summarise(total_lead_time=mean(lead_time))

## `summarise()` regrouping output by 'arrival_date_month', 'hotel' (override with `.groups` argument)

ggplot(booking_month, aes(fill=continent, y=total_lead_time, x=arrival_date_month)) +
  geom_bar(position = "stack", stat="identity") +
    scale_fill_viridis(discrete = T) +
    ggtitle("Number of Lead Time to Book the Hotel Based on Continent") +
    xlab("Month") +
    ylab("Lead Time") +
    labs(fill="Continent") +
    theme(plot.title = element_text(hjust = 0.5, face="bold", size=12), 
          axis.text.x = element_text(angle = 40, hjust = 1),
          axis.text.y = element_text(hjust = 0.5),
        panel.background = element_rect(fill = "white", colour ="white",linetype = "solid"),
    panel.grid.minor = element_line(colour="#A9A9A9", size=0.5))+
    #geom_text(aes(label = total_lead_time), hjust = 0.5, color = 'white', size = 3) +
  coord_flip()

14. How about the lead time of the booking hotel per Month?

If in the barplot people have the higher number of lead time in July. In boxplot here, it’s more clear that because of there is one extreme value BUT the highest number of people booking the hotel is in September. Probably it’s because of the promotion in September

total_lead_time <- booking %>%
                        select(arrival_date_month, lead_time) %>%
                        group_by(arrival_date_month) %>%
                        drop_na(arrival_date_month)

ggplot(total_lead_time, aes(x=arrival_date_month, y=lead_time,
                       fill=as.factor(arrival_date_month))) +
  geom_boxplot() +
 # scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  ggtitle("Boxplot of  Lead Time") +
  ylab("Total Number of Visitors") +
  xlab("") +
  theme(legend.position = "none",
        plot.title = element_text(face="bold", hjust=.5),
        axis.text.x = element_text(size = 12, angle = 90, 
                                   hjust = .5, vjust = .5, face = "bold"),
        axis.text.y = element_text(size = 12, angle = 0, 
                                   hjust = 1, vjust = 0, face = "bold"),  
        axis.title.x = element_text(size = 12, angle = 0, 
                                    hjust = .5, vjust = 0, face = "bold"),
        axis.title.y = element_text(size = 12, angle = 90, 
                                    hjust = .5, vjust = .5, face = "bold"),
          axis.ticks = element_line(colour = "#808080", size = 2), 
          axis.line = element_line(colour = "#808080", linetype ="solid",size = 1),            
          panel.grid.major = element_line(colour = "#808080"), 
           panel.grid.major.x = element_line(size=1, colour="#808080"), 
           panel.grid.minor.y = element_line(size = 1, colour = "#808080"),
          panel.background = element_rect(fill = "white", colour = "white"))+
    coord_flip()

15. How many people stays in the hotel during the year?

Among all the contingents, people who are from Europe & UK has the biggest number to stay in the hotel
especially in Quarter 2 (April, May and June)

booking_quarter <- booking %>%
                    select(continent,quarter, adults, children, babies) %>%
             mutate(total_visitors=adults+children,babies) %>%
             group_by(continent, quarter) %>%
             drop_na(continent) %>%
             summarise(total = sum(total_visitors))

## `summarise()` regrouping output by 'continent' (override with `.groups` argument)

booking_quarter$tooltips <- paste(
                            "Continent :", booking_quarter$continent, 
                            "\nNumofPpl :", booking_quarter$total, 
                            "\nQuarter : ", booking_quarter$quarter, 
                            sep="") %>%
                            sapply(htmltools::HTML)
                            

plot0 <-ggplot(booking_quarter, aes(fill=continent, 
                                    y=total, x=quarter, text=tooltips))+
      geom_area(aes(fill=continent, group=continent)) +
      scale_fill_viridis(discrete = TRUE) +
      ggtitle("Number of people staying in Hotel by Quarter") +
      ylab("Number of People") +
      xlab("Quarter") +
      labs(fill="Continent") +
      theme(plot.title = element_text(hjust=0.5, face="bold"),
        panel.background = element_rect(fill = "white", colour ="white",linetype = "solid"),
        panel.grid.minor = element_line(colour="#A9A9A9", size=0.5))
      
plot1 <- ggplotly(plot0, tooltip="tooltips")

## Warning: Removed 1 rows containing missing values (position_stack).

## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

plot1

16. How about market segment of the hotel?

Based on the plot, mostly customers of the hotel are people from group which means like Family, Group of School and etc. The smallest percentage of customers are from Aviation

booking_market <- booking %>%
             select(market_segment,adults, children, babies) %>%
             mutate(total_visitors=adults+children,babies) %>%
             group_by(market_segment) %>%
             summarise(total = sum(total_visitors)) %>%
             drop_na(market_segment, total) %>%
          mutate(prop=round(total/sum(total)*100,2)) %>%
          mutate(ypos=cumsum(prop)-0.5*prop)

## `summarise()` ungrouping output (override with `.groups` argument)

booking_market$pos = (cumsum(c(0, booking_market$prop)) + c(booking_market$ypos/ 2, .01))[1:nrow(booking_market)]

ggplot(booking_market, aes(x="",y=prop, fill=as.factor(market_segment))) +
          geom_bar(stat="identity", width = 1, color="white") +
          coord_polar("y", start=0) +
          theme_void() +
          labs(fill="Market Segment") +
          geom_text(aes(x=1.6, label=paste(prop, "%")), 
                    position = position_stack(vjust = 0.5)) +
          scale_fill_brewer(palette="Set1") +
          ggtitle('Market Segment for Hotel') +
          theme(plot.title = element_text(hjust = 0.5, face="bold", size=16))

17. Correlation between Daily Total Visitors and Total Stays (Both Weekend and Weekdays)

There is no correlation between average daily rate and lead time because there is no pattern between those two variables

booking_europe <- booking %>%
  filter(continent == "Europe & UK") %>%
  mutate(total_visitors=adults+children,babies,
         total_days = stays_in_week_nights + stays_in_weekend_nights)  %>%
  filter(adr<=1000 & adr>=0) %>%
  mutate(adr_price = adr*100000)

booking_europe %>%
  arrange(desc(adr_price)) %>%
  ggplot(aes(x=total_visitors, y=total_days, size=adr_price, fill=as.factor(continent))) +
    geom_point(alpha=0.5, shape=21, color="black") +
    scale_size(range = c(.1, 24), name="Total Visitors") +
    theme(legend.position="none") +
    ylab("Averate Daily Rate") +
    xlab("Lead Time") +
    ggtitle('Correlation between Average Daily Rate vs Lead Time') +
    theme(plot.title = element_text(hjust=0.5),
          axis.ticks = element_line(colour = "#808080", size = 2), 
          axis.line = element_line(colour = "#808080", linetype ="solid",size = 1),            
          panel.grid.major = element_line(colour = "#808080"), 
           panel.grid.major.x = element_line(size=1, colour="#808080"), 
           panel.grid.minor.y = element_line(size = 1, colour = "#808080"),
          panel.background = element_rect(fill = "white", colour = "white"))

## Warning: Removed 4 rows containing missing values (geom_point).

18. What’s the variance of the meal and price of the hotel?

This plot shows comparison average daily rate with meal from 2015-2017 . From bed breakfast, full board, half board, self catering and undefined.
- First we would like to talks about the full board, Full board means hotel provides bed, breakfast, packed
  lunch and evening meal. Full board showed with the black bards, Full board has the highest demand in 2017, the average daily rate of the hotel which provides of full board are the cheapest compare the other type of the meals.
- Then we would like to talk about bed breakfast, bed breakfast means people who stay in private family homes with the host living there. It showed with blue bars However, the number of Bed breakfast are less than Full Board. The average daily price of the hotel that provides this type of breakfast is the most expensive.
- From this plot, It means that people prefer to have full board because it’s more comfortable for them also it has the lowest average daily rate in the hotel

num_18 <- booking %>%
          select(continent, meal, arrival_date_year, adr) %>%
          group_by(arrival_date_year, meal) %>%
          filter(adr<=1000 & adr>=0) %>%
          drop_na(continent) %>%
          summarise(n=n(),
                    price= mean(adr*1000))

## `summarise()` regrouping output by 'arrival_date_year' (override with `.groups` argument)

num_18$meal <- ifelse(num_18$meal=="BB","Bed_Breakfast",
                      ifelse(num_18$meal=="SC","Self_Catering",ifelse(
                      num_18$meal=="HB","Half_Board",
                      ifelse(num_18$meal=="FB","Full_Board","Undefined"))))


ggplot(num_18, aes(x=arrival_date_year, y=price, fill=meal, color=meal))  + 
    geom_bar(aes(x=arrival_date_year, y=price),stat="identity",position="dodge") +
    geom_line(aes(x=arrival_date_year, y=2.5*n), linetype="solid", size=2) +
    xlab("Arrival Date Year") +
    ylab("Price") +
    labs(fill="Meal", color="Avg Daily Rate", subtitle ="2015-2017") +
    ggtitle("Average daily rate in Meal") +
    scale_x_continuous(breaks = c(2015,2016,2017)) +
    scale_fill_manual(values = c("Bed_Breakfast" = "darkblue", "Full_Board" = "black", 
          "Half_Board" = "darkgreen", "Self_Catering" = "maroon", "Undefined"="gray"))+
    theme(legend.position = "right",
          plot.title = element_text(hjust=0.5, face="bold"),
          plot.subtitle = element_text(hjust=0.5, face="bold"),
          panel.grid.major = element_line(colour = "#808080"), 
          panel.grid.major.x = element_line(size=1, colour="#808080"), 
          panel.grid.minor.y = element_line(size = 1, colour = "#808080"),
          panel.background = element_rect(fill = "white", colour = "white"))

19. How about the difference trend between Adults and Family stay in the hotel?

Based on the plot, barplot showed for number of adults who stayed in the hotel and line chart showed number of family who stayed in the hotel. As a result that mostly people who stayed in the hotels are family but around week 30-40, the number of adults are greater than number of family who stayed in the hotels

coeff <- 10

data_adult_family <- booking %>%  
                    mutate(family= adults+children+babies) %>%
                    group_by(arrival_date_week_number) %>%
                    summarise(family_num = sum(family),
                              adult_num = sum(adults)) %>% 
                    drop_na(arrival_date_week_number)

## `summarise()` ungrouping output (override with `.groups` argument)

ggplot(data_adult_family, aes(x=arrival_date_week_number, fill=adult_num)) +
  geom_line(aes(y=family_num), size=2, color="#FC4E07") + 
  geom_bar( aes(y=adult_num), stat="identity") +
    scale_y_continuous(
    name = "Total Number of Families",
    sec.axis = sec_axis(~.*coeff, name="Total Number of Adults")) + 
    labs(fill="Number of Adults") +
    theme(legend.position="right",
          axis.title.y = element_text(size=13,hjust=0.5),
    axis.title.y.right = element_text(size=13, hjust=.5),
    plot.title = element_text(hjust=0.5),
    axis.text.x = element_text(size = 12, angle = 90, 
                               hjust = .5, vjust = .5, face = "bold"),
        axis.title.x = element_text(size = 12, angle = 0, 
                                    hjust = .5, vjust = 0, face = "bold"),
        axis.ticks = element_line(colour = "#808080", size = 2), 
        axis.line = element_line(colour = "#808080", linetype ="solid",size = 1),         
        panel.grid.major = element_line(colour = "#808080"), 
        panel.grid.major.x = element_line(size=1, colour="#808080"), 
        panel.grid.minor.y = element_line(size = 1, colour = "#808080"),
        panel.background = element_rect(fill = "white", colour = "white")) +
  ggtitle("Number of Adults Vs Family") +
  xlab("Week Number")

Advanced visualisation in R Project

Hotel booking Data

Cuc Thi Nguyen-Marcinczyk & Adinda Rizky Herawati

2021-05-24 10:51:30