Analyzing Hotel Booking Patterns in R Programming Langauge involves examining a dataset related to hotel bookings to derive insights into booking trends, customer behavior, and other relevant factors. In this case, the dataset contains columns such as hotel type, cancellation status, lead time, arrival date details, and other booking-related information.
Dataset Link:- Hotel Booking Demand
Load Packages and Data
- First, install and load the required packages.
- Next, read the Dataset and check the first few rows.
R
# Install and load necessary libraries
#install.packages(c("tidyverse", "ggplot2", "forecast"))
library(tidyverse)
library(ggplot2)
library(forecast)
# Load the hotel bookings dataset
hotel_data <- read.csv("Your//path")
head(hotel_data)
Output:
hotel is_canceled lead_time arrival_date_year arrival_date_month
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
arrival_date_week_number arrival_date_day_of_month stays_in_weekend_nights
1 27 1 0
2 27 1 0
3 27 1 0
4 27 1 0
5 27 1 0
6 27 1 0
stays_in_week_nights adults children babies meal country market_segment
1 0 2 0 0 BB PRT Direct
2 0 2 0 0 BB PRT Direct
3 1 1 0 0 BB GBR Direct
4 1 1 0 0 BB GBR Corporate
5 2 2 0 0 BB GBR Online TA
6 2 2 0 0 BB GBR Online TA
distribution_channel is_repeated_guest previous_cancellations
1 Direct 0 0
2 Direct 0 0
3 Direct 0 0
4 Corporate 0 0
5 TA/TO 0 0
6 TA/TO 0 0
previous_bookings_not_canceled reserved_room_type assigned_room_type
1 0 C C
2 0 C C
3 0 A C
4 0 A A
5 0 A A
6 0 A A
booking_changes deposit_type agent company days_in_waiting_list customer_type
1 3 No Deposit NULL NULL 0 Transient
2 4 No Deposit NULL NULL 0 Transient
3 0 No Deposit NULL NULL 0 Transient
4 0 No Deposit 304 NULL 0 Transient
5 0 No Deposit 240 NULL 0 Transient
6 0 No Deposit 240 NULL 0 Transient
adr required_car_parking_spaces total_of_special_requests reservation_status
1 0 0 0 Check-Out
2 0 0 0 Check-Out
3 75 0 0 Check-Out
4 75 0 0 Check-Out
5 98 0 1 Check-Out
6 98 0 1 Check-Out
reservation_status_date
1 2015-07-01
2 2015-07-01
3 2015-07-02
4 2015-07-02
5 2015-07-03
6 2015-07-03
Check the Structure
Let’s check the structure of the dataset .
R
Output:
'data.frame': 119390 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" ...
Check the Summary
Checking the summary of the dataset it provides various information like( min , meadian , mode ,etc)
R
Output:
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 children
Min. : 0.0000 Min. : 0.0 Min. : 0.000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.: 1.0 1st Qu.: 2.000 1st Qu.: 0.0000
Median : 1.0000 Median : 2.0 Median : 2.000 Median : 0.0000
Mean : 0.9276 Mean : 2.5 Mean : 1.856 Mean : 0.1039
3rd Qu.: 2.0000 3rd Qu.: 3.0 3rd Qu.: 2.000 3rd Qu.: 0.0000
Max. :19.0000 Max. :50.0 Max. :55.000 Max. :10.0000
NA's :4
babies meal country market_segment
Min. : 0.000000 Length:119390 Length:119390 Length:119390
1st Qu.: 0.000000 Class :character Class :character Class :character
Median : 0.000000 Mode :character Mode :character Mode :character
Mean : 0.007949
3rd Qu.: 0.000000
Max. :10.000000
distribution_channel is_repeated_guest previous_cancellations
Length:119390 Min. :0.00000 Min. : 0.00000
Class :character 1st Qu.:0.00000 1st Qu.: 0.00000
Mode :character Median :0.00000 Median : 0.00000
Mean :0.03191 Mean : 0.08712
3rd Qu.:0.00000 3rd Qu.: 0.00000
Max. :1.00000 Max. :26.00000
previous_bookings_not_canceled reserved_room_type assigned_room_type
Min. : 0.0000 Length:119390 Length:119390
1st Qu.: 0.0000 Class :character Class :character
Median : 0.0000 Mode :character Mode :character
Mean : 0.1371
3rd Qu.: 0.0000
Max. :72.0000
booking_changes deposit_type agent company
Min. : 0.0000 Length:119390 Length:119390 Length:119390
1st Qu.: 0.0000 Class :character Class :character Class :character
Median : 0.0000 Mode :character Mode :character Mode :character
Mean : 0.2211
3rd Qu.: 0.0000
Max. :21.0000
days_in_waiting_list customer_type adr
Min. : 0.000 Length:119390 Min. : -6.38
1st Qu.: 0.000 Class :character 1st Qu.: 69.29
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
Count NA Values
Checking the null values from the dataset.
R
# Count NA values in the entire data frame
total_na_count <- sum(is.na(hotel_data))
# Count NA values in each column
na_counts_per_column <- colSums(is.na(hotel_data))
# Display the results
cat("Total NA values in the entire data frame:", total_na_count, "\n")
cat("NA values per column:\n")
print(na_counts_per_column)
Output:
Total NA values in the entire data frame: 4
NA values per column:
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 0
Removing NA Values
If any null values is present it will be removed by this command.
R
# Removing for null values
hotel_data <- na.omit(hotel_data)
sum(is.na(hotel_data))
Output:
[1] 0
Visualization
Barplot
The barplot shows the both type hotel reservations number .
R
ggplot(hotel_data, aes(x = reservation_status, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Reservation Status by Hotel Type", x = "Reservation Status",
y = "Count")
Output:
Analyzing Hotel Booking Patterns in R
Scatterplot between Lead Time vs. Average Daily Rate
This scatterplot aims to understand the relationship between lead time (the number of days between booking and arrival) and the Average Daily Rate (ADR). The points on the scatterplot represent individual bookings.
R
# Visualization: Scatter Plot - Lead Time vs. Average Daily Rate
ggplot(hotel_data, aes(x = lead_time, y = adr, color = hotel)) +
geom_point(alpha = 0.5) +
labs(title = "Lead Time vs. Average Daily Rate",
x = "Lead Time",
y = "Average Daily Rate",
color = "Hotel Type") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
A positive correlation between lead time and ADR suggests that longer lead times may be associated with lower ADR.
- Clusters or patterns in the scatterplot may indicate seasonality or specific booking trends.
Histogram with Distplot for Hotel Booking Pattern in R
We take sample portion of this large dataset , it can be modified according to the demand.
R
# Sample a subset of the dataset (adjust the number as needed)
sample_size <- 1000
hotel_data_sample <- hotel_data %>% sample_n(sample_size)
# Set a theme for better appearance
theme_set(theme_minimal())
# Create a histogram with distribution plot for lead time
ggplot(hotel_data_sample, aes(x = lead_time)) +
geom_histogram(aes(y = ..density..), binwidth = 10, fill = "red", color = "green",
alpha = 0.7) +
geom_density(color = "blue") +
labs(title = "Hotel Booking Lead Time Distribution with Distribution Plot",
x = "Lead Time (days)",
y = "Density") +
theme(panel.grid.major = element_line(color = "gray", linetype = "dashed",size = 0.2),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "white"),
legend.position = "none") +
scale_x_continuous(breaks = seq(0, max(hotel_data_sample$lead_time), by = 20))
Output:
Analyzing Hotel Booking Patterns in R
Skewed distributions may indicate certain booking behaviors, such as last-minute bookings or extended stays.
- Peaks and valleys in the distplot offer insights into preferred booking patterns within the dataset.
Outliers Detection forHotel Booking Pattern Data in R
Outliers can significantly impact analysis. This step involves identifying and examining potential outliers in numeric columns like lead time, ADR, or stays.
R
# Assuming 'lead_time' is the column of interest
# Create a boxplot to visualize outliers
ggplot(hotel_data, aes(x = 1, y = lead_time)) +
geom_boxplot(fill = "skyblue", color = "black") +
labs(title = "Boxplot of Lead Time (Outlier Detection)",
x = "",
y = "Lead Time") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
Outliers may highlight exceptional cases, such as extremely long lead times or unusually high ADRs.
- Understanding outliers helps in assessing the robustness of general trends and patterns in the dataset.
Check Monthly Booking Trends
Analyzing booking trends on a monthly basis helps identify seasonality and understand when hotels experience peak or off-peak periods.
R
# Visualization Monthly Booking Trends
hotel_data %>%
mutate(arrival_date_month = factor(arrival_date_month, levels = month.name)) %>%
ggplot(aes(x = arrival_date_month, fill = hotel)) +
geom_bar(position = "dodge", stat = "count") +
labs(title = "Monthly Booking Trends",
x = "Month",
y = "Number of Bookings",
fill = "Hotel Type") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
Peaks in certain months may be attributed to holidays, events, or specific seasons, influencing booking patterns.
- Understanding monthly trends aids in optimizing resource allocation and marketing strategies.
Check the Average Daily Rate Distribution
Examining the distribution of Average Daily Rates provides insights into the pricing strategy of hotels.
R
# Visualization Average Daily Rate (ADR) Distribution
ggplot(hotel_data, aes(x = adr, fill = hotel)) +
geom_density(alpha = 0.7) +
labs(title = "ADR Distribution by Hotel Type",
x = "ADR",
y = "Density",
fill = "Hotel Type") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
Peaks or clusters in the ADR distribution may indicate different pricing tiers or packages.
- Understanding ADR distribution helps in setting competitive and profitable pricing strategies.
Check Market Segment Distribution
Analyzing the distribution of market segments provides insights into the customer base and booking sources.
R
# Visualization Market Segment Distribution
hotel_data %>%
ggplot(aes(x = market_segment, fill = hotel)) +
geom_bar(position = "dodge", stat = "count") +
labs(title = "Distribution of Bookings by Market Segment",
x = "Market Segment",
y = "Number of Bookings",
fill = "Hotel Type") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
Dominant market segments indicate the primary sources of bookings, influencing marketing and communication strategies.
- Varied market segment distribution helps in understanding the diverse customer base.
Check Most Preffered hotel
Identifying the most preferred hotel provides insights into customer preferences and popularity.
R
# Visualization: Most Preferred Hotel
library(ggplot2)
library(ggthemes) # For additional themes
# Assuming hotel_data is your data frame
hotel_data %>%
ggplot(aes(x = hotel, fill = hotel)) +
geom_bar(stat = "count", color = "white", size = 0.7) +
labs(title = "Distribution of Bookings by Hotel Type",
x = "Hotel Type",
y = "Number of Bookings",
fill = "Hotel Type") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
legend.title = element_text(face = "italic"), # Make legend title italic
legend.position = "top", # Position legend at the top
panel.grid.major = element_line(colour = "lightgray", linetype = "dashed"))
Output:
Analyzing Hotel Booking Patterns in R
The preferred city hotel may have unique features or services that attract more bookings.
- Understanding customer preferences aids in targeted marketing and service improvements.
Visualize Cancellation Patterns
Visualizing cancellation patterns helps in understanding factors influencing cancellations and optimizing reservation management.
R
# Visualization: Cancellation Patterns
hotel_data %>%
ggplot(aes(x = hotel, fill = as.factor(is_canceled))) +
geom_bar(stat = "count", position = "stack") +
labs(title = "Cancellation Patterns by Hotel Type",
x = "Hotel Type",
y = "Number of Bookings",
fill = "Cancellation Status") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
Peaks in cancellation rates may be linked to specific seasons, events, or external factors.
- Identifying patterns in cancellations assists in implementing strategies to reduce cancellations.
Analyzing Booking Distribution by Month
Examining the booking distribution by month provides insights into booking patterns over the entire dataset.
R
# Visualization: Booking Distribution by Month
hotel_data %>%
ggplot(aes(x = arrival_date_month, fill = hotel)) +
geom_bar(stat = "count", position = "stack") +
labs(title = "Booking Distribution by Month",
x = "Month",
y = "Number of Bookings",
fill = "Hotel Type") +
theme_minimal() +
scale_x_discrete(labels = function(x) formatC(as.numeric(x), width = 2, flag = "0"))
Output:
Analyzing Hotel Booking Patterns in R
Seasonal variations may be observed, influencing marketing and pricing strategies.
- Identifying booking patterns helps in resource planning and optimizing hotel operations.
Check the Customer type
Analyze the distribution of bookings based on customer types (Transient, Contract, Group, etc.).
R
# Create a barplot for customer type distribution
ggplot(hotel_data, aes(x = customer_type, fill = customer_type)) +
geom_bar() +
labs(title = "Distribution of Bookings by Customer Type",
x = "Customer Type",
y = "Number of Bookings") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
The barplot visualizes the proportion of bookings attributed to different customer types, such as Transient, Contract, Group, etc.
- It helps identify which customer segments contribute significantly to the overall bookings, allowing for targeted marketing efforts or tailored services.
- Understanding the preferred customer types can guide strategies to attract and retain specific segments, enhancing customer satisfaction and loyalty.
Checking the Booking Channels
This barplot illustrates the distribution of bookings through different channels, providing insights into the popularity of channels such as direct bookings, online travel agencies (OTA), and corporate bookings.
R
# Create a barplot for booking distribution by channels
ggplot(hotel_data, aes(x = distribution_channel, fill = distribution_channel)) +
geom_bar() +
labs(title = "Distribution of Bookings by Booking Channels",
x = "Booking Channels",
y = "Number of Bookings") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
The visualization shows the distribution of bookings across various channels such as Direct, Online Travel Agencies (OTA), and Corporate.
- From the barplot, it appears that the “TA/TO” (Travel Agents/Tour Operators) channel has the highest number of bookings compared to other channels.
- The hotel management may infer that the TA/TO channel is crucial for bookings, and they might consider strengthening marketing efforts or partnerships with travel agencies to further enhance this channel.
Correlation Heatmap of Numeric Columns
The correlation heatmap illustrates the relationships between numeric variables, revealing potential dependencies.
R
# Load the reshape2 package
library(reshape2)
# Select numeric columns for correlation heatmap
numeric_columns <- select_if(hotel_data, is.numeric)
# Calculate the correlation matrix
correlation_matrix <- cor(numeric_columns)
# Plot the heatmap
ggplot(data = melt(correlation_matrix), aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
theme_minimal() +
labs(title = "Correlation Heatmap of Numeric Columns",
x = "Variables",
y = "Variables",
fill = "Correlation")
Output:
Analyzing Hotel Booking Patterns in R
Strong correlations may indicate interdependencies between variables, influencing decision-making.
- Understanding correlations helps in identifying key factors affecting booking patterns.
Analyze the Weekday Vs Weekend bookings
Visualize the distribution of bookings between weekdays and weekends.
R
# Create a barplot for weekday vs. weekend bookings
hotel_data %>%
mutate(booking_day_type = ifelse(stays_in_weekend_nights >0,"Weekend","Weekday"))%>%
ggplot(aes(x = booking_day_type, fill = booking_day_type)) +
geom_bar() +
labs(title = "Distribution of Bookings between Weekdays and Weekends",
x = "Day Type",
y = "Number of Bookings") +
theme_minimal()
Output:
Analyzing Hotel Booking Patterns in R
The number of bookings during weekdays is notably lower than weekends, indicating a higher preference for weekends stays.
Conclusion
This R analysis explores hotel booking patterns, covering data loading, structure check, and visualization. Various plots reveal trends, distributions, customer type , booking source and correlations, offering insights into customer behavior. The most preferred hotel and cancellation patterns are highlighted. Overall, the analysis provides valuable information for understanding and decision-making in the hospitality industry.
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