Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge_2
challenge_2
XinyangMao
hotel_bookings
Data wrangling: using group() and summarise()
Challenge Overview
Today’s challenge is to
- read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
- provide summary statistics for different interesting groups within the data, and interpret those statistics
Read in the Data
- hotel_bookings.csv
Code
df <- read.csv("_data/hotel_bookings.csv")
data <- na.omit(df)In this challenge,I chose hotel_bookings.csv to analysis.
Describe the data
We can see that there are 119390 rows and 32 columns in this dataset.
Code
dim(data)[1] 119386 32The structure of the data set also tells us the number of rows and columns, but it provides even more information. It tells us the column names, the class of each column (what kind of data is stored in it), and the first few observations of each variable.
Code
str(data)'data.frame': 119386 obs. of 32 variables:
$ hotel : chr "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
$ is_canceled : int 0 0 0 0 0 0 0 0 1 1 ...
$ lead_time : int 342 737 7 13 14 14 0 9 85 75 ...
$ arrival_date_year : int 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
$ arrival_date_month : chr "July" "July" "July" "July" ...
$ arrival_date_week_number : int 27 27 27 27 27 27 27 27 27 27 ...
$ arrival_date_day_of_month : int 1 1 1 1 1 1 1 1 1 1 ...
$ stays_in_weekend_nights : int 0 0 0 0 0 0 0 0 0 0 ...
$ stays_in_week_nights : int 0 0 1 1 2 2 2 2 3 3 ...
$ adults : int 2 2 1 1 2 2 2 2 2 2 ...
$ children : int 0 0 0 0 0 0 0 0 0 0 ...
$ babies : int 0 0 0 0 0 0 0 0 0 0 ...
$ meal : chr "BB" "BB" "BB" "BB" ...
$ country : chr "PRT" "PRT" "GBR" "GBR" ...
$ market_segment : chr "Direct" "Direct" "Direct" "Corporate" ...
$ distribution_channel : chr "Direct" "Direct" "Direct" "Corporate" ...
$ is_repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
$ previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
$ previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
$ reserved_room_type : chr "C" "C" "A" "A" ...
$ assigned_room_type : chr "C" "C" "C" "A" ...
$ booking_changes : int 3 4 0 0 0 0 0 0 0 0 ...
$ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
$ agent : chr "NULL" "NULL" "NULL" "304" ...
$ company : chr "NULL" "NULL" "NULL" "NULL" ...
$ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
$ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
$ adr : num 0 0 75 75 98 ...
$ required_car_parking_spaces : int 0 0 0 0 0 0 0 0 0 0 ...
$ total_of_special_requests : int 0 0 0 0 1 1 0 1 1 0 ...
$ reservation_status : chr "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
$ reservation_status_date : chr "2015-07-01" "2015-07-01" "2015-07-02" "2015-07-02" ...
- attr(*, "na.action")= 'omit' Named int [1:4] 40601 40668 40680 41161
..- attr(*, "names")= chr [1:4] "40601" "40668" "40680" "41161"The summary provides descriptive statistics including the min, max, mean, median, and quartiles of each column. For example, we can see in this data set that the mean rate of is_canaceled is 37.04% and the maximum number of baby is 10.
Code
summary(data) hotel is_canceled lead_time arrival_date_year
Length:119386 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:119386 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 :27.16 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:119386 Length:119386
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
market_segment distribution_channel is_repeated_guest
Length:119386 Length:119386 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:119386
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:119386 Min. : 0.0000 Length:119386 Length:119386
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:119386 Min. : 0.000 Length:119386 Min. : -6.38
Class :character 1st Qu.: 0.000 Class :character 1st Qu.: 69.29
Mode :character Median : 0.000 Mode :character Median : 94.59
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:119386
1st Qu.:0.00000 1st Qu.:0.0000 Class :character
Median :0.00000 Median :0.0000 Mode :character
Mean :0.06252 Mean :0.5713
3rd Qu.:0.00000 3rd Qu.:1.0000
Max. :8.00000 Max. :5.0000
reservation_status_date
Length:119386
Class :character
Mode :character
Provide Grouped Summary Statistics
We can see there are lots of variables now,we can use cor() function to show the relationships between these variables.Obviously,we can find that for arrival_date_year variables,the most related numeric variable is arrival_date_week_number.But all of the numeric variables seem don’t have strong relationships.
Code
library(dplyr)
data <- select(data,-where(is.character))
cor(data) is_canceled lead_time arrival_date_year
is_canceled 1.000000000 0.2931773844 0.0167324934
lead_time 0.293177384 1.0000000000 0.0400927219
arrival_date_year 0.016732493 0.0400927219 1.0000000000
arrival_date_week_number 0.008131991 0.1268848204 -0.5405663357
arrival_date_day_of_month -0.006084131 0.0022337238 -0.0002790381
stays_in_weekend_nights -0.001783382 0.0856671122 0.0214888460
stays_in_week_nights 0.024771434 0.1657994743 0.0308779571
adults 0.059990266 0.1195443171 0.0296741970
children 0.005047790 -0.0376216776 0.0546241639
babies -0.032488454 -0.0209179156 -0.0131971353
is_repeated_guest -0.084787949 -0.1244175906 0.0103318406
previous_cancellations 0.110140471 0.0860398385 -0.1198331083
previous_bookings_not_canceled -0.057355365 -0.0735521797 0.0292143485
booking_changes -0.144370572 0.0001380965 0.0308552231
days_in_waiting_list 0.054193146 0.1700826037 -0.0565063742
adr 0.047622225 -0.0631259954 0.1975183834
required_car_parking_spaces -0.195492464 -0.1164605134 -0.0136989991
total_of_special_requests -0.234705902 -0.0956878275 0.1085851089
arrival_date_week_number
is_canceled 0.008131991
lead_time 0.126884820
arrival_date_year -0.540566336
arrival_date_week_number 1.000000000
arrival_date_day_of_month 0.066823684
stays_in_weekend_nights 0.018209372
stays_in_week_nights 0.015559322
adults 0.025901318
children 0.005518419
babies 0.010396527
is_repeated_guest -0.030128572
previous_cancellations 0.035502291
previous_bookings_not_canceled -0.020902457
booking_changes 0.005511772
days_in_waiting_list 0.022934739
adr 0.075811103
required_car_parking_spaces 0.001923621
total_of_special_requests 0.026139352
arrival_date_day_of_month
is_canceled -0.0060841312
lead_time 0.0022337238
arrival_date_year -0.0002790381
arrival_date_week_number 0.0668236844
arrival_date_day_of_month 1.0000000000
stays_in_weekend_nights -0.0163702417
stays_in_week_nights -0.0281924631
adults -0.0015394616
children 0.0145438342
babies -0.0002332642
is_repeated_guest -0.0061516010
previous_cancellations -0.0270150357
previous_bookings_not_canceled -0.0003030418
booking_changes 0.0106010824
days_in_waiting_list 0.0227233609
adr 0.0301886003
required_car_parking_spaces 0.0086746240
total_of_special_requests 0.0030944208
stays_in_weekend_nights stays_in_week_nights
is_canceled -0.001783382 0.024771434
lead_time 0.085667112 0.165799474
arrival_date_year 0.021488846 0.030877957
arrival_date_week_number 0.018209372 0.015559322
arrival_date_day_of_month -0.016370242 -0.028192463
stays_in_weekend_nights 1.000000000 0.498967239
stays_in_week_nights 0.498967239 1.000000000
adults 0.091888610 0.092983373
children 0.045792937 0.044202919
babies 0.018482550 0.020190920
is_repeated_guest -0.087241563 -0.097247278
previous_cancellations -0.012775390 -0.013993096
previous_bookings_not_canceled -0.042716302 -0.048743688
booking_changes 0.063280182 0.096209580
days_in_waiting_list -0.054152569 -0.002020421
adr 0.049323279 0.065219447
required_car_parking_spaces -0.018555572 -0.024860942
total_of_special_requests 0.072685999 0.068199125
adults children babies
is_canceled 0.059990266 0.005047790 -0.0324884545
lead_time 0.119544317 -0.037621678 -0.0209179156
arrival_date_year 0.029674197 0.054624164 -0.0131971353
arrival_date_week_number 0.025901318 0.005518419 0.0103965273
arrival_date_day_of_month -0.001539462 0.014543834 -0.0002332642
stays_in_weekend_nights 0.091888610 0.045792937 0.0184825497
stays_in_week_nights 0.092983373 0.044202919 0.0201909200
adults 1.000000000 0.030446849 0.0181478114
children 0.030446849 1.000000000 0.0240295524
babies 0.018147811 0.024029552 1.0000000000
is_repeated_guest -0.146424566 -0.032859382 -0.0089431363
previous_cancellations -0.006735864 -0.024730099 -0.0075012826
previous_bookings_not_canceled -0.107982949 -0.021072490 -0.0065507062
booking_changes -0.051666039 0.048948888 0.0834390248
days_in_waiting_list -0.008280488 -0.033272616 -0.0106209075
adr 0.230690021 0.324853605 0.0291829276
required_car_parking_spaces 0.014790889 0.056252847 0.0373827041
total_of_special_requests 0.122854601 0.081744822 0.0978928336
is_repeated_guest previous_cancellations
is_canceled -0.084787949 0.110140471
lead_time -0.124417591 0.086039838
arrival_date_year 0.010331841 -0.119833108
arrival_date_week_number -0.030128572 0.035502291
arrival_date_day_of_month -0.006151601 -0.027015036
stays_in_weekend_nights -0.087241563 -0.012775390
stays_in_week_nights -0.097247278 -0.013993096
adults -0.146424566 -0.006735864
children -0.032859382 -0.024730099
babies -0.008943136 -0.007501283
is_repeated_guest 1.000000000 0.082292667
previous_cancellations 0.082292667 1.000000000
previous_bookings_not_canceled 0.418055728 0.152727847
booking_changes 0.012089755 -0.026993891
days_in_waiting_list -0.022235787 0.005928488
adr -0.134328457 -0.065653153
required_car_parking_spaces 0.077088149 -0.018493155
total_of_special_requests 0.013055449 -0.048382004
previous_bookings_not_canceled booking_changes
is_canceled -0.0573553652 -0.1443705725
lead_time -0.0735521797 0.0001380965
arrival_date_year 0.0292143485 0.0308552231
arrival_date_week_number -0.0209024574 0.0055117720
arrival_date_day_of_month -0.0003030418 0.0106010824
stays_in_weekend_nights -0.0427163025 0.0632801821
stays_in_week_nights -0.0487436875 0.0962095799
adults -0.1079829493 -0.0516660388
children -0.0210724903 0.0489488885
babies -0.0065507062 0.0834390248
is_repeated_guest 0.4180557276 0.0120897552
previous_cancellations 0.1527278470 -0.0269938913
previous_bookings_not_canceled 1.0000000000 0.0116065130
booking_changes 0.0116065130 1.0000000000
days_in_waiting_list -0.0093973866 -0.0116354687
adr -0.0721514059 0.0196022487
required_car_parking_spaces 0.0476523637 0.0656174943
total_of_special_requests 0.0378270602 0.0528441726
days_in_waiting_list adr
is_canceled 0.054193146 0.04762222
lead_time 0.170082604 -0.06312600
arrival_date_year -0.056506374 0.19751838
arrival_date_week_number 0.022934739 0.07581110
arrival_date_day_of_month 0.022723361 0.03018860
stays_in_weekend_nights -0.054152569 0.04932328
stays_in_week_nights -0.002020421 0.06521945
adults -0.008280488 0.23069002
children -0.033272616 0.32485360
babies -0.010620908 0.02918293
is_repeated_guest -0.022235787 -0.13432846
previous_cancellations 0.005928488 -0.06565315
previous_bookings_not_canceled -0.009397387 -0.07215141
booking_changes -0.011635469 0.01960225
days_in_waiting_list 1.000000000 -0.04076433
adr -0.040764331 1.00000000
required_car_parking_spaces -0.030601215 0.05661816
total_of_special_requests -0.082727388 0.17223837
required_car_parking_spaces
is_canceled -0.195492464
lead_time -0.116460513
arrival_date_year -0.013698999
arrival_date_week_number 0.001923621
arrival_date_day_of_month 0.008674624
stays_in_weekend_nights -0.018555572
stays_in_week_nights -0.024860942
adults 0.014790889
children 0.056252847
babies 0.037382704
is_repeated_guest 0.077088149
previous_cancellations -0.018493155
previous_bookings_not_canceled 0.047652364
booking_changes 0.065617494
days_in_waiting_list -0.030601215
adr 0.056618163
required_car_parking_spaces 1.000000000
total_of_special_requests 0.082635165
total_of_special_requests
is_canceled -0.234705902
lead_time -0.095687827
arrival_date_year 0.108585109
arrival_date_week_number 0.026139352
arrival_date_day_of_month 0.003094421
stays_in_weekend_nights 0.072685999
stays_in_week_nights 0.068199125
adults 0.122854601
children 0.081744822
babies 0.097892834
is_repeated_guest 0.013055449
previous_cancellations -0.048382004
previous_bookings_not_canceled 0.037827060
booking_changes 0.052844173
days_in_waiting_list -0.082727388
adr 0.172238374
required_car_parking_spaces 0.082635165
total_of_special_requests 1.000000000According to the dataset,we can notice that it provide the number of adults, children and baby.Let’s take a look at the relationship between the type of occupant and the month of occupancy.
Code
df %>%
select(arrival_date_month,children,adults,babies)%>%
group_by(arrival_date_month)# A tibble: 119,390 × 4
# Groups: arrival_date_month [12]
arrival_date_month children adults babies
<chr> <int> <int> <int>
1 July 0 2 0
2 July 0 2 0
3 July 0 1 0
4 July 0 1 0
5 July 0 2 0
6 July 0 2 0
7 July 0 2 0
8 July 0 2 0
9 July 0 2 0
10 July 0 2 0
# … with 119,380 more rowsWe can use arrange(),desc(),select(),group_by() and slice() to show which months have most children guests below. The result is the largest number of children be hosted in a hotel happened in July,the less time happened in September.
Code
df %>%
arrange(desc(children))%>%
select(arrival_date_month,children)%>%
group_by(arrival_date_month)%>%
slice(1)%>%
arrange(desc(children))# A tibble: 12 × 2
# Groups: arrival_date_month [12]
arrival_date_month children
<chr> <int>
1 July 10
2 April 3
3 August 3
4 December 3
5 February 3
6 January 3
7 June 3
8 March 3
9 May 3
10 November 3
11 October 3
12 September 2Same,for adults,they’re October and January.
Code
df %>%
arrange(desc(adults))%>%
select(arrival_date_month,adults)%>%
group_by(arrival_date_month)%>%
slice(1)%>%
arrange(desc(adults))# A tibble: 12 × 2
# Groups: arrival_date_month [12]
arrival_date_month adults
<chr> <int>
1 October 55
2 September 50
3 April 4
4 August 4
5 December 4
6 February 4
7 July 4
8 June 4
9 March 4
10 May 4
11 November 4
12 January 3As for babies,the maximum number happened in October too.
Code
df %>%
arrange(desc(babies))%>%
select(arrival_date_month,babies)%>%
group_by(arrival_date_month)%>%
slice(1)%>%
arrange(desc(babies))# A tibble: 12 × 2
# Groups: arrival_date_month [12]
arrival_date_month babies
<chr> <int>
1 January 10
2 October 9
3 August 2
4 December 2
5 February 2
6 July 2
7 June 2
8 March 2
9 November 2
10 September 2
11 April 1
12 May 1Explain and Interpret
Some interesting findings are that hotel stays for adults and babies are relatively similar, with the most and least occurring in October and January, respectively, probably because October is a good month to travel and January is usually colder. For children, the highest number of children occurred in July, probably during the summer holidays.