Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 2
challenge_2
Priyanka Perumalla
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
Read in one (or more) of the following data sets, available in the posts/_data folder, using the correct R package and command.
- railroad*.csv or StateCounty2012.xls ⭐
- FAOstat*.csv or birds.csv ⭐⭐⭐
- hotel_bookings.csv ⭐⭐⭐⭐
Code
hotel_data <- read_csv("_data/hotel_bookings.csv")
print(hotel_data,show_col_types = FALSE)# A tibble: 119,390 × 32
hotel is_canceled lead_time arrival_date_year arrival_date_month
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hotel 0 342 2015 July
2 Resort Hotel 0 737 2015 July
3 Resort Hotel 0 7 2015 July
4 Resort Hotel 0 13 2015 July
5 Resort Hotel 0 14 2015 July
6 Resort Hotel 0 14 2015 July
7 Resort Hotel 0 0 2015 July
8 Resort Hotel 0 9 2015 July
9 Resort Hotel 1 85 2015 July
10 Resort Hotel 1 75 2015 July
# ℹ 119,380 more rows
# ℹ 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
# meal <chr>, country <chr>, market_segment <chr>,
# distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, …Add any comments or documentation as needed. More challenging data may require additional code chunks and documentation.
Describe the data
Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).
Code
head(hotel_data)# A tibble: 6 × 32
hotel is_canceled lead_time arrival_date_year arrival_date_month
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hotel 0 342 2015 July
2 Resort Hotel 0 737 2015 July
3 Resort Hotel 0 7 2015 July
4 Resort Hotel 0 13 2015 July
5 Resort Hotel 0 14 2015 July
6 Resort Hotel 0 14 2015 July
# ℹ 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
# meal <chr>, country <chr>, market_segment <chr>,
# distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <chr>, assigned_room_type <chr>, …Code
str(hotel_data)spc_tbl_ [119,390 × 32] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ hotel : chr [1:119390] "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
$ is_canceled : num [1:119390] 0 0 0 0 0 0 0 0 1 1 ...
$ lead_time : num [1:119390] 342 737 7 13 14 14 0 9 85 75 ...
$ arrival_date_year : num [1:119390] 2015 2015 2015 2015 2015 ...
$ arrival_date_month : chr [1:119390] "July" "July" "July" "July" ...
$ arrival_date_week_number : num [1:119390] 27 27 27 27 27 27 27 27 27 27 ...
$ arrival_date_day_of_month : num [1:119390] 1 1 1 1 1 1 1 1 1 1 ...
$ stays_in_weekend_nights : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ stays_in_week_nights : num [1:119390] 0 0 1 1 2 2 2 2 3 3 ...
$ adults : num [1:119390] 2 2 1 1 2 2 2 2 2 2 ...
$ children : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ babies : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ meal : chr [1:119390] "BB" "BB" "BB" "BB" ...
$ country : chr [1:119390] "PRT" "PRT" "GBR" "GBR" ...
$ market_segment : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
$ distribution_channel : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
$ is_repeated_guest : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ previous_cancellations : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ previous_bookings_not_canceled: num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ reserved_room_type : chr [1:119390] "C" "C" "A" "A" ...
$ assigned_room_type : chr [1:119390] "C" "C" "C" "A" ...
$ booking_changes : num [1:119390] 3 4 0 0 0 0 0 0 0 0 ...
$ deposit_type : chr [1:119390] "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
$ agent : chr [1:119390] "NULL" "NULL" "NULL" "304" ...
$ company : chr [1:119390] "NULL" "NULL" "NULL" "NULL" ...
$ days_in_waiting_list : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ customer_type : chr [1:119390] "Transient" "Transient" "Transient" "Transient" ...
$ adr : num [1:119390] 0 0 75 75 98 ...
$ required_car_parking_spaces : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
$ total_of_special_requests : num [1:119390] 0 0 0 0 1 1 0 1 1 0 ...
$ reservation_status : chr [1:119390] "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
$ reservation_status_date : Date[1:119390], format: "2015-07-01" "2015-07-01" ...
- attr(*, "spec")=
.. cols(
.. hotel = col_character(),
.. is_canceled = col_double(),
.. lead_time = col_double(),
.. arrival_date_year = col_double(),
.. arrival_date_month = col_character(),
.. arrival_date_week_number = col_double(),
.. arrival_date_day_of_month = col_double(),
.. stays_in_weekend_nights = col_double(),
.. stays_in_week_nights = col_double(),
.. adults = col_double(),
.. children = col_double(),
.. babies = col_double(),
.. meal = col_character(),
.. country = col_character(),
.. market_segment = col_character(),
.. distribution_channel = col_character(),
.. is_repeated_guest = col_double(),
.. previous_cancellations = col_double(),
.. previous_bookings_not_canceled = col_double(),
.. reserved_room_type = col_character(),
.. assigned_room_type = col_character(),
.. booking_changes = col_double(),
.. deposit_type = col_character(),
.. agent = col_character(),
.. company = col_character(),
.. days_in_waiting_list = col_double(),
.. customer_type = col_character(),
.. adr = col_double(),
.. required_car_parking_spaces = col_double(),
.. total_of_special_requests = col_double(),
.. reservation_status = col_character(),
.. reservation_status_date = col_date(format = "")
.. )
- attr(*, "problems")=<externalptr> Code
library(summarytools)
dfSummary(hotel_data)Data Frame Summary
hotel_data
Dimensions: 119390 x 32
Duplicates: 31994
-----------------------------------------------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
---- -------------------------------- -------------------------- ---------------------- ---------------------- ---------- ---------
1 hotel 1. City Hotel 79330 (66.4%) IIIIIIIIIIIII 119390 0
[character] 2. Resort Hotel 40060 (33.6%) IIIIII (100.0%) (0.0%)
2 is_canceled Min : 0 0 : 75166 (63.0%) IIIIIIIIIIII 119390 0
[numeric] Mean : 0.4 1 : 44224 (37.0%) IIIIIII (100.0%) (0.0%)
Max : 1
3 lead_time Mean (sd) : 104 (106.9) 479 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 69 < 737 :
IQR (CV) : 142 (1) : : .
: : : . .
4 arrival_date_year Mean (sd) : 2016.2 (0.7) 2015 : 21996 (18.4%) III 119390 0
[numeric] min < med < max: 2016 : 56707 (47.5%) IIIIIIIII (100.0%) (0.0%)
2015 < 2016 < 2017 2017 : 40687 (34.1%) IIIIII
IQR (CV) : 1 (0)
5 arrival_date_month 1. August 13877 (11.6%) II 119390 0
[character] 2. July 12661 (10.6%) II (100.0%) (0.0%)
3. May 11791 ( 9.9%) I
4. October 11160 ( 9.3%) I
5. April 11089 ( 9.3%) I
6. June 10939 ( 9.2%) I
7. September 10508 ( 8.8%) I
8. March 9794 ( 8.2%) I
9. February 8068 ( 6.8%) I
10. November 6794 ( 5.7%) I
[ 2 others ] 12709 (10.6%) II
6 arrival_date_week_number Mean (sd) : 27.2 (13.6) 53 distinct values . : . . . 119390 0
[numeric] min < med < max: . : : : : : : (100.0%) (0.0%)
1 < 28 < 53 . : : : : : : : : :
IQR (CV) : 22 (0.5) : : : : : : : : : :
: : : : : : : : : :
7 arrival_date_day_of_month Mean (sd) : 15.8 (8.8) 31 distinct values : 119390 0
[numeric] min < med < max: : : : . : : . : : (100.0%) (0.0%)
1 < 16 < 31 : : : : : : : : : :
IQR (CV) : 15 (0.6) : : : : : : : : : :
: : : : : : : : : :
8 stays_in_weekend_nights Mean (sd) : 0.9 (1) 17 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 1 < 19 :
IQR (CV) : 2 (1.1) : :
: :
9 stays_in_week_nights Mean (sd) : 2.5 (1.9) 35 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 2 < 50 :
IQR (CV) : 2 (0.8) :
:
10 adults Mean (sd) : 1.9 (0.6) 14 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 2 < 55 :
IQR (CV) : 0 (0.3) :
:
11 children Mean (sd) : 0.1 (0.4) 0 : 110796 (92.8%) IIIIIIIIIIIIIIIIII 119386 4
[numeric] min < med < max: 1 : 4861 ( 4.1%) (100.0%) (0.0%)
0 < 0 < 10 2 : 3652 ( 3.1%)
IQR (CV) : 0 (3.8) 3 : 76 ( 0.1%)
10 : 1 ( 0.0%)
12 babies Mean (sd) : 0 (0.1) 0 : 118473 (99.2%) IIIIIIIIIIIIIIIIIII 119390 0
[numeric] min < med < max: 1 : 900 ( 0.8%) (100.0%) (0.0%)
0 < 0 < 10 2 : 15 ( 0.0%)
IQR (CV) : 0 (12.3) 9 : 1 ( 0.0%)
10 : 1 ( 0.0%)
13 meal 1. BB 92310 (77.3%) IIIIIIIIIIIIIII 119390 0
[character] 2. FB 798 ( 0.7%) (100.0%) (0.0%)
3. HB 14463 (12.1%) II
4. SC 10650 ( 8.9%) I
5. Undefined 1169 ( 1.0%)
14 country 1. PRT 48590 (40.7%) IIIIIIII 119390 0
[character] 2. GBR 12129 (10.2%) II (100.0%) (0.0%)
3. FRA 10415 ( 8.7%) I
4. ESP 8568 ( 7.2%) I
5. DEU 7287 ( 6.1%) I
6. ITA 3766 ( 3.2%)
7. IRL 3375 ( 2.8%)
8. BEL 2342 ( 2.0%)
9. BRA 2224 ( 1.9%)
10. NLD 2104 ( 1.8%)
[ 168 others ] 18590 (15.6%) III
15 market_segment 1. Aviation 237 ( 0.2%) 119390 0
[character] 2. Complementary 743 ( 0.6%) (100.0%) (0.0%)
3. Corporate 5295 ( 4.4%)
4. Direct 12606 (10.6%) II
5. Groups 19811 (16.6%) III
6. Offline TA/TO 24219 (20.3%) IIII
7. Online TA 56477 (47.3%) IIIIIIIII
8. Undefined 2 ( 0.0%)
16 distribution_channel 1. Corporate 6677 ( 5.6%) I 119390 0
[character] 2. Direct 14645 (12.3%) II (100.0%) (0.0%)
3. GDS 193 ( 0.2%)
4. TA/TO 97870 (82.0%) IIIIIIIIIIIIIIII
5. Undefined 5 ( 0.0%)
17 is_repeated_guest Min : 0 0 : 115580 (96.8%) IIIIIIIIIIIIIIIIIII 119390 0
[numeric] Mean : 0 1 : 3810 ( 3.2%) (100.0%) (0.0%)
Max : 1
18 previous_cancellations Mean (sd) : 0.1 (0.8) 15 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 26 :
IQR (CV) : 0 (9.7) :
:
19 previous_bookings_not_canceled Mean (sd) : 0.1 (1.5) 73 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 72 :
IQR (CV) : 0 (10.9) :
:
20 reserved_room_type 1. A 85994 (72.0%) IIIIIIIIIIIIII 119390 0
[character] 2. B 1118 ( 0.9%) (100.0%) (0.0%)
3. C 932 ( 0.8%)
4. D 19201 (16.1%) III
5. E 6535 ( 5.5%) I
6. F 2897 ( 2.4%)
7. G 2094 ( 1.8%)
8. H 601 ( 0.5%)
9. L 6 ( 0.0%)
10. P 12 ( 0.0%)
21 assigned_room_type 1. A 74053 (62.0%) IIIIIIIIIIII 119390 0
[character] 2. D 25322 (21.2%) IIII (100.0%) (0.0%)
3. E 7806 ( 6.5%) I
4. F 3751 ( 3.1%)
5. G 2553 ( 2.1%)
6. C 2375 ( 2.0%)
7. B 2163 ( 1.8%)
8. H 712 ( 0.6%)
9. I 363 ( 0.3%)
10. K 279 ( 0.2%)
[ 2 others ] 13 ( 0.0%)
22 booking_changes Mean (sd) : 0.2 (0.7) 21 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 21 :
IQR (CV) : 0 (2.9) :
:
23 deposit_type 1. No Deposit 104641 (87.6%) IIIIIIIIIIIIIIIII 119390 0
[character] 2. Non Refund 14587 (12.2%) II (100.0%) (0.0%)
3. Refundable 162 ( 0.1%)
24 agent 1. 9 31961 (26.8%) IIIII 119390 0
[character] 2. NULL 16340 (13.7%) II (100.0%) (0.0%)
3. 240 13922 (11.7%) II
4. 1 7191 ( 6.0%) I
5. 14 3640 ( 3.0%)
6. 7 3539 ( 3.0%)
7. 6 3290 ( 2.8%)
8. 250 2870 ( 2.4%)
9. 241 1721 ( 1.4%)
10. 28 1666 ( 1.4%)
[ 324 others ] 33250 (27.8%) IIIII
25 company 1. NULL 112593 (94.3%) IIIIIIIIIIIIIIIIII 119390 0
[character] 2. 40 927 ( 0.8%) (100.0%) (0.0%)
3. 223 784 ( 0.7%)
4. 67 267 ( 0.2%)
5. 45 250 ( 0.2%)
6. 153 215 ( 0.2%)
7. 174 149 ( 0.1%)
8. 219 141 ( 0.1%)
9. 281 138 ( 0.1%)
10. 154 133 ( 0.1%)
[ 343 others ] 3793 ( 3.2%)
26 days_in_waiting_list Mean (sd) : 2.3 (17.6) 128 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 391 :
IQR (CV) : 0 (7.6) :
:
27 customer_type 1. Contract 4076 ( 3.4%) 119390 0
[character] 2. Group 577 ( 0.5%) (100.0%) (0.0%)
3. Transient 89613 (75.1%) IIIIIIIIIIIIIII
4. Transient-Party 25124 (21.0%) IIII
28 adr Mean (sd) : 101.8 (50.5) 8879 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
-6.4 < 94.6 < 5400 :
IQR (CV) : 56.7 (0.5) :
:
29 required_car_parking_spaces Mean (sd) : 0.1 (0.2) 0 : 111974 (93.8%) IIIIIIIIIIIIIIIIII 119390 0
[numeric] min < med < max: 1 : 7383 ( 6.2%) I (100.0%) (0.0%)
0 < 0 < 8 2 : 28 ( 0.0%)
IQR (CV) : 0 (3.9) 3 : 3 ( 0.0%)
8 : 2 ( 0.0%)
30 total_of_special_requests Mean (sd) : 0.6 (0.8) 0 : 70318 (58.9%) IIIIIIIIIII 119390 0
[numeric] min < med < max: 1 : 33226 (27.8%) IIIII (100.0%) (0.0%)
0 < 0 < 5 2 : 12969 (10.9%) II
IQR (CV) : 1 (1.4) 3 : 2497 ( 2.1%)
4 : 340 ( 0.3%)
5 : 40 ( 0.0%)
31 reservation_status 1. Canceled 43017 (36.0%) IIIIIII 119390 0
[character] 2. Check-Out 75166 (63.0%) IIIIIIIIIIII (100.0%) (0.0%)
3. No-Show 1207 ( 1.0%)
32 reservation_status_date min : 2014-10-17 926 distinct values . : : : : 119390 0
[Date] med : 2016-08-07 : : : : : : . (100.0%) (0.0%)
max : 2017-09-14 . : : : : : : :
range : 2y 10m 28d : : : : : : : :
. : : : : : : : :
-----------------------------------------------------------------------------------------------------------------------------------Code
dim(hotel_data)[1] 119390 32It can be observed that there are 119390 rows and 32 columns
Printing the column names of the hotel bookings dataset :
Code
colnames(hotel_data) [1] "hotel" "is_canceled"
[3] "lead_time" "arrival_date_year"
[5] "arrival_date_month" "arrival_date_week_number"
[7] "arrival_date_day_of_month" "stays_in_weekend_nights"
[9] "stays_in_week_nights" "adults"
[11] "children" "babies"
[13] "meal" "country"
[15] "market_segment" "distribution_channel"
[17] "is_repeated_guest" "previous_cancellations"
[19] "previous_bookings_not_canceled" "reserved_room_type"
[21] "assigned_room_type" "booking_changes"
[23] "deposit_type" "agent"
[25] "company" "days_in_waiting_list"
[27] "customer_type" "adr"
[29] "required_car_parking_spaces" "total_of_special_requests"
[31] "reservation_status" "reservation_status_date" Description : The data set involved gathering information of hotel bookings in different hotels. Every booking entry is a row having information on which hotel the booking corresponds to, when it happened, what is the start date and what is the end date, who made the booking, for who all the booking was made, the channel used for booking, the number of days for which it was made, how much money was paid, all the preferences that were entered along with the booking etc.
Provide Grouped Summary Statistics
Conduct some exploratory data analysis, using dplyr commands such as group_by(), select(), filter(), and summarise(). Find the central tendency (mean, median, mode) and dispersion (standard deviation, mix/max/quantile) for different subgroups within the data set.
Group by Arrival date month:
Code
hotel_data %>%
group_by(arrival_date_month) %>%
summarise(stays_in_week_nights = mean(stays_in_week_nights, na.rm=TRUE), StandardDeviation = sd(stays_in_week_nights, na.rm = TRUE))# A tibble: 12 × 3
arrival_date_month stays_in_week_nights StandardDeviation
<chr> <dbl> <dbl>
1 April 2.42 NA
2 August 2.84 NA
3 December 2.36 NA
4 February 2.18 NA
5 January 2.19 NA
6 July 2.81 NA
7 June 2.66 NA
8 March 2.56 NA
9 May 2.41 NA
10 November 2.40 NA
11 October 2.23 NA
12 September 2.52 NACode
df<- hotel_data %>%
filter(country == "GBR")
print(df)# A tibble: 12,129 × 32
hotel is_canceled lead_time arrival_date_year arrival_date_month
<chr> <dbl> <dbl> <dbl> <chr>
1 Resort Hotel 0 7 2015 July
2 Resort Hotel 0 13 2015 July
3 Resort Hotel 0 14 2015 July
4 Resort Hotel 0 14 2015 July
5 Resort Hotel 0 7 2015 July
6 Resort Hotel 0 37 2015 July
7 Resort Hotel 0 127 2015 July
8 Resort Hotel 0 95 2015 July
9 Resort Hotel 0 90 2015 July
10 Resort Hotel 0 364 2015 July
# ℹ 12,119 more rows
# ℹ 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
# meal <chr>, country <chr>, market_segment <chr>,
# distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, …Code
df<- hotel_data %>%
filter(babies > 0 && meal=="BB")
print(df)# A tibble: 0 × 32
# ℹ 32 variables: hotel <chr>, is_canceled <dbl>, lead_time <dbl>,
# arrival_date_year <dbl>, arrival_date_month <chr>,
# arrival_date_week_number <dbl>, arrival_date_day_of_month <dbl>,
# stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>,
# children <dbl>, babies <dbl>, meal <chr>, country <chr>,
# market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, …Code
#IQR for railroads
hotel_data %>%
summarize_all(IQR, na.rm = TRUE)# A tibble: 1 × 32
hotel is_canceled lead_time arrival_date_year arrival_date_month
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 1 142 1 NA
# ℹ 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
# meal <dbl>, country <dbl>, market_segment <dbl>,
# distribution_channel <dbl>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <dbl>, assigned_room_type <dbl>, …Code
#Mean for railroads
hotel_data %>%
summarize_all(mean, na.rm = TRUE)# A tibble: 1 × 32
hotel is_canceled lead_time arrival_date_year arrival_date_month
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 0.370 104. 2016. NA
# ℹ 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
# meal <dbl>, country <dbl>, market_segment <dbl>,
# distribution_channel <dbl>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <dbl>, assigned_room_type <dbl>, …Code
#Median for railroads
hotel_data %>%
summarize_all(median, na.rm = TRUE)# A tibble: 1 × 32
hotel is_canceled lead_time arrival_date_year arrival_date_month
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 0 69 2016 NA
# ℹ 27 more variables: arrival_date_week_number <dbl>,
# arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
# stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
# meal <dbl>, country <dbl>, market_segment <dbl>,
# distribution_channel <dbl>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <dbl>, assigned_room_type <dbl>, …Total number of days spent(stays in week nights) via bookings for each hotel:
Code
hotel_wise_ct = select(hotel_data, hotel,stays_in_week_nights)
hotel_wise_ct %>%
group_by(hotel) %>%
summarize(stays_in_week_nights=sum(stays_in_week_nights))# A tibble: 2 × 2
hotel stays_in_week_nights
<chr> <dbl>
1 City Hotel 173174
2 Resort Hotel 125337Code
hotel_wise_ct# A tibble: 119,390 × 2
hotel stays_in_week_nights
<chr> <dbl>
1 Resort Hotel 0
2 Resort Hotel 0
3 Resort Hotel 1
4 Resort Hotel 1
5 Resort Hotel 2
6 Resort Hotel 2
7 Resort Hotel 2
8 Resort Hotel 2
9 Resort Hotel 3
10 Resort Hotel 3
# ℹ 119,380 more rowsMedian, Mean and standard deviation of stays_in_week_nights in every hotel:
Code
hotel_wise_ct = select(hotel_data, hotel, stays_in_week_nights)
hotel_wise_ct %>%
group_by(hotel) %>%
summarize(meanDays=mean(stays_in_week_nights),medianDays=median(stays_in_week_nights),standardDeviation = sd(stays_in_week_nights))# A tibble: 2 × 4
hotel meanDays medianDays standardDeviation
<chr> <dbl> <dbl> <dbl>
1 City Hotel 2.18 2 1.46
2 Resort Hotel 3.13 3 2.46Hotel wise number of days busy count displayed in descending order :
Code
hotel_wise_grouped_cts <- hotel_wise_ct %>%
group_by(hotel) %>%
summarize(Sum = sum(stays_in_week_nights))
count_sorted <- hotel_wise_grouped_cts %>%
arrange(desc(Sum))
count_sorted# A tibble: 2 × 2
hotel Sum
<chr> <dbl>
1 City Hotel 173174
2 Resort Hotel 125337Explain and Interpret
Be sure to explain why you choose a specific group. Comment on the interpretation of any interesting differences between groups that you uncover. This section can be integrated with the exploratory data analysis, just be sure it is included.
I attempted to see which hotel is the busiest from among all the hotels in the given data set. If I go by number of bookings, then I would not get an appropriate answer. The true measure is the availability for a booking, which in turn is the number of days.I tried to group the data by hotel and then attempted to see the total number of days that were busy at every hotel. Additionally, I have also observed some metrics relate to the data in terms of the days spent like mean, median, standard deviation.
Overall, City Hotel looks more busy compared to Resort Hotel. We can draw an assumed conclusion from the name by assuming that city hotel is more accessible than resort hotel where people only go on holidays.