Challenge 7
Shaurya Rawat
2024-01-13
Introduction
This challenge will focus on publication ready plots that include properties such as axis labels and facets.
Loading the required libraries.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(readr)
library(here)## here() starts at C:/Users/SHAURYA/Desktop/Studies/Winter 2024 601/Challenges/challenge 7library(dplyr)Reading the Data
We load the dataset that is a CSV file.
hotel_csv <- read_csv("hotel_bookings.csv", show_col_types = FALSE)We can have a look at the data.
head(hotel_csv)## # 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>, …The following provides the description of the columns-
hotel- The type of hotel. For example, Resort Hotel.
is_canceled - Binary value that indicates if booking was cancelled or not.
lead_time: Number of days between the booking date and the arrival date.
arrival_date_year: The year of arrival.
arrival_date_month: The month of arrival.
arrival_date_week_number: The week number of the year for the arrival date.
arrival_date_day_of_month: The day of the month for the arrival date.
stays_in_weekend_nights: Number of weekend nights booked to stay.
stays_in_week_nights: Number of weekday nights booked to stay.
adults: Number of adults included in the reservation.
children: Number of children included in the reservation.
babies: Number of babies included in the reservation.
meal: Type of meal booked.
country: Country of origin.
market_segment: Market segment designation like Online Travel Agencies.
distribution_channel: Booking distribution channel.
is_repeated_guest: Binary value indicating if the guest is a repeated guest or not.
previous_cancellations: Number of previous bookings that were canceled by the guest.
previous_bookings_not_canceled: Number of previous bookings not canceled by the guest.
reserved_room_type: Code of room type reserved.
assigned_room_type: Code of room type assigned.
booking_changes: Number of changes made to the booking.
deposit_type: Type of deposit made by the guest.
agent: ID of the travel agency making the booking.
company: ID of the company making the booking.
days_in_waiting_list: Number of days the booking was in the waiting list before it was confirmed.
customer_type: Type of booking.
adr: Average Daily Rate (the sum of transactional value divided by the number of nights).
required_car_parking_spaces: Number of car parking spaces required by the guest.
total_of_special_requests: Number of special requests made by the guest.
reservation_status: Reservation last status like Checked Out or Cancelled.
reservation_status_date: Date when the last status was set.
We get the dimensions of the data
dim(hotel_csv)## [1] 119390 32We see that there are 119390 hotel bookings across 32 parameters.
We can have a look at the columns.
colnames(hotel_csv)## [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"We can just look at a specific column in the data. For instance, we can pick the country.
select(hotel_csv, "country")## # A tibble: 119,390 × 1
## country
## <chr>
## 1 PRT
## 2 PRT
## 3 GBR
## 4 GBR
## 5 GBR
## 6 GBR
## 7 PRT
## 8 PRT
## 9 PRT
## 10 PRT
## # ℹ 119,380 more rowsWe can find the distribution of values under a specific column. For instance, if we want to see it in distribution channel.
dc <- select(hotel_csv, distribution_channel)
head(dc)## # A tibble: 6 × 1
## distribution_channel
## <chr>
## 1 Direct
## 2 Direct
## 3 Direct
## 4 Corporate
## 5 TA/TO
## 6 TA/TOtable(dc)## distribution_channel
## Corporate Direct GDS TA/TO Undefined
## 6677 14645 193 97870 5We see that the frequencies vary a lot. From above we see that TA/TO has the highest occurence among all channels.
We can use a proportion table to get a better insight.
prop.table(table(dc))## distribution_channel
## Corporate Direct GDS TA/TO Undefined
## 5.592596e-02 1.226652e-01 1.616551e-03 8.197504e-01 4.187955e-05Tidying the data
First we check for any missing values.
colSums(is.na(hotel_csv))## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_week_number
## 0 0
## arrival_date_day_of_month stays_in_weekend_nights
## 0 0
## stays_in_week_nights adults
## 0 0
## children babies
## 4 0
## meal country
## 0 0
## market_segment distribution_channel
## 0 0
## is_repeated_guest previous_cancellations
## 0 0
## previous_bookings_not_canceled reserved_room_type
## 0 0
## assigned_room_type booking_changes
## 0 0
## deposit_type agent
## 0 0
## company days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0We can fix it by replacing the missing children values by 0.
hotel_csv$children[is.na(hotel_csv$children)] <- 0We can check again for missing values to see if it worked.
colSums(is.na(hotel_csv))## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_week_number
## 0 0
## arrival_date_day_of_month stays_in_weekend_nights
## 0 0
## stays_in_week_nights adults
## 0 0
## children babies
## 0 0
## meal country
## 0 0
## market_segment distribution_channel
## 0 0
## is_repeated_guest previous_cancellations
## 0 0
## previous_bookings_not_canceled reserved_room_type
## 0 0
## assigned_room_type booking_changes
## 0 0
## deposit_type agent
## 0 0
## company days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0The above operation eliminated all missing values.
We can conduct a sanity check to ensure that the data appears to be normal.
summary(hotel_csv)## 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
## 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
## Min. :2014-10-17
## 1st Qu.:2016-02-01
## Median :2016-08-07
## Mean :2016-07-30
## 3rd Qu.:2017-02-08
## Max. :2017-09-14We see that all the columns are in reasonable ranges. That means that tidying the data was successful.
Mutating the data
We see that there are separate columns for year, month, and day. We can merge them into one as a date column.
hotel_csv <- hotel_csv %>%
mutate(arrival_date = as.Date(paste(arrival_date_year, arrival_date_month, arrival_date_day_of_month, sep = "-"), format = "%Y-%B-%d"))We can remove the columns that we merged.
hotel_csv <- hotel_csv %>%
select(-c(arrival_date_year, arrival_date_month, arrival_date_day_of_month))We see that we can merge adults, children and babies into a single column as people.
hotel_csv <- hotel_csv %>%
mutate(guests = adults + children + babies)We can now take off the original columns.
hotel_csv <- hotel_csv %>%
select(-c(adults, children, babies))We see that there are separate columns for weeknights and weekend nights. We can merge these to get total nights.
hotel_csv <- hotel_csv %>%
mutate(total_nights = stays_in_weekend_nights + stays_in_week_nights)We then remove the original columns.
hotel_csv <- hotel_csv %>%
select(-c(stays_in_weekend_nights, stays_in_week_nights))Once all is done, we can have a look at the data again to see if the changes were successfully made.
head(hotel_csv)## # A tibble: 6 × 27
## hotel is_canceled lead_time arrival_date_week_number meal country
## <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 Resort Hotel 0 342 27 BB PRT
## 2 Resort Hotel 0 737 27 BB PRT
## 3 Resort Hotel 0 7 27 BB GBR
## 4 Resort Hotel 0 13 27 BB GBR
## 5 Resort Hotel 0 14 27 BB GBR
## 6 Resort Hotel 0 14 27 BB GBR
## # ℹ 21 more variables: 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>, booking_changes <dbl>, deposit_type <chr>,
## # agent <chr>, company <chr>, days_in_waiting_list <dbl>,
## # customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>,
## # total_of_special_requests <dbl>, reservation_status <chr>, …We can conduct sanity checks on our mutations.
str(hotel_csv)## tibble [119,390 × 27] (S3: 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_week_number : num [1:119390] 27 27 27 27 27 27 27 27 27 27 ...
## $ 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" ...
## $ arrival_date : Date[1:119390], format: "2015-07-01" "2015-07-01" ...
## $ guests : num [1:119390] 2 2 1 1 2 2 2 2 2 2 ...
## $ total_nights : num [1:119390] 0 0 1 1 2 2 2 2 3 3 ...summary(hotel_csv)## hotel is_canceled lead_time arrival_date_week_number
## Length:119390 Min. :0.0000 Min. : 0 Min. : 1.00
## Class :character 1st Qu.:0.0000 1st Qu.: 18 1st Qu.:16.00
## Mode :character Median :0.0000 Median : 69 Median :28.00
## Mean :0.3704 Mean :104 Mean :27.17
## 3rd Qu.:1.0000 3rd Qu.:160 3rd Qu.:38.00
## Max. :1.0000 Max. :737 Max. :53.00
## meal country market_segment distribution_channel
## Length:119390 Length:119390 Length:119390 Length:119390
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.00000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :0.00000 Median : 0.00000 Median : 0.0000
## Mean :0.03191 Mean : 0.08712 Mean : 0.1371
## 3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.00000 Max. :26.00000 Max. :72.0000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:119390 Length:119390 Min. : 0.0000 Length:119390
## Class :character Class :character 1st Qu.: 0.0000 Class :character
## Mode :character Mode :character Median : 0.0000 Mode :character
## Mean : 0.2211
## 3rd Qu.: 0.0000
## Max. :21.0000
## agent company days_in_waiting_list customer_type
## Length:119390 Length:119390 Min. : 0.000 Length:119390
## Class :character Class :character 1st Qu.: 0.000 Class :character
## Mode :character Mode :character Median : 0.000 Mode :character
## Mean : 2.321
## 3rd Qu.: 0.000
## Max. :391.000
## adr required_car_parking_spaces total_of_special_requests
## Min. : -6.38 Min. :0.00000 Min. :0.0000
## 1st Qu.: 69.29 1st Qu.:0.00000 1st Qu.:0.0000
## Median : 94.58 Median :0.00000 Median :0.0000
## Mean : 101.83 Mean :0.06252 Mean :0.5714
## 3rd Qu.: 126.00 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :5400.00 Max. :8.00000 Max. :5.0000
## reservation_status reservation_status_date arrival_date
## Length:119390 Min. :2014-10-17 Min. :2015-07-01
## Class :character 1st Qu.:2016-02-01 1st Qu.:2016-03-13
## Mode :character Median :2016-08-07 Median :2016-09-06
## Mean :2016-07-30 Mean :2016-08-28
## 3rd Qu.:2017-02-08 3rd Qu.:2017-03-18
## Max. :2017-09-14 Max. :2017-08-31
## guests total_nights
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 2.000
## Median : 2.000 Median : 3.000
## Mean : 1.968 Mean : 3.428
## 3rd Qu.: 2.000 3rd Qu.: 4.000
## Max. :55.000 Max. :69.000We see that both operations yield appropriate results. This shows that the mutations were successful.
Plots
We can compare the total nights spent across the confirmed bookings in both types of hotels - city and resort.
confirmed_bookings <- hotel_csv %>%
filter(reservation_status == "Check-Out")
nights <- ggplot(confirmed_bookings, aes(x = hotel, y = total_nights)) +
geom_boxplot(color = "skyblue", outlier.shape = 16, outlier.size = 3) +
labs(title = "Distribution of total nights across confirmed bookings",
subtitle = "Comparison with types of hotel",
x = "Hotel",
y = "Total Nights") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
plot.subtitle = element_text(hjust = 0.5, size = 14),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
nights
We use these parameters as they present an interesting case of data analysis. A box plot does a great job in visualizing the comparison between the two.
We see that for most part, the number of nights spent in both hotels are almost similar. We also see that the minimum total nights spent in resorts is higher and this makes sense because resort hotels tend to be close to tourist attractions that attracts a more expensive stay.
We also see that certain bookings in resorts are for well over a month which presents an opportunity to investigate deeper into such bookings.
The second graph compares the bookings where people got their reserved room type and the people who were assigned something else.
room_type <- hotel_csv %>%
group_by(final_room = ifelse(reserved_room_type == assigned_room_type, "Same Room Type", "Different Room Type")) %>%
summarize(count = n())
plot2 <- ggplot(room_type, aes(x = "", y = count, fill = final_room)) +
geom_bar(stat = "identity", color = "skyblue") +
labs(title = "Comparison of Reserved and Assigned Room Types",
x = NULL,
y = "Count") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
plot2
A stacked bar chart is used because it gives a clear visual representation of the gap between the room types. From the chart above, we see that most bookings were assigned the room type that they reserved which is about 100,000. Less than 25000 bookings out of 119390 bookings were assigned some other room type.
This comparison is essential as it gives information about the demand and supply of various room types. The hotel management can analyze this information and make changes to the quantity of room types.
Conclusion
We created a few more graphs compared to Challenge 6 and we made them publication ready by assigning appropriate legends, title, and caption.