Challenge 6

Visualizing Time and Relationships

library(tidyverse)
library(ggplot2)
library(dplyr)
options(dplyr.summarise.inform = FALSE)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Data Description

Reading the data

data <- read_csv("_data/hotel_bookings.csv", show_col_types = FALSE)
head(data)

# A tibble: 6 × 32
  hotel   is_ca…¹ lead_…² arriv…³ arriv…⁴ arriv…⁵ arriv…⁶ stays…⁷ stays…⁸ adults
  <chr>     <dbl>   <dbl>   <dbl> <chr>     <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
1 Resort…       0     342    2015 July         27       1       0       0      2
2 Resort…       0     737    2015 July         27       1       0       0      2
3 Resort…       0       7    2015 July         27       1       0       1      1
4 Resort…       0      13    2015 July         27       1       0       1      1
5 Resort…       0      14    2015 July         27       1       0       2      2
6 Resort…       0      14    2015 July         27       1       0       2      2
# … with 22 more variables: 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>, 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>, …

Columns in the dataset:

colnames(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"       

The dimensions of the dataset are as follows:

dim(data)

[1] 119390     32

There are 32 columns and 119390 rows in the dataset.

Tidying the Data

I plan on visualizing the number of people who stayed in a hotel during each year so the dataset can be assumed to be tidy and can be used for visualization.

Time Dependent Visualization

First I want to analyse at which of these years the hotels had a maximum number of people staying at the hotel. To do that first I will have to filter out the people who cancelled from the dataset, then we can group the data on hotel and arrival_date_year and this data could be summarised to obtain the number of bookings in each of the hotels during different years. The command is as follows:

yearly_data <- data%>%
  filter(is_canceled == 0)%>%
  group_by(hotel, arrival_date_year)%>%
  summarise(yearly_count = n())
head(yearly_data)

# A tibble: 6 × 3
# Groups:   hotel [2]
  hotel        arrival_date_year yearly_count
  <chr>                    <dbl>        <int>
1 City Hotel                2015         7678
2 City Hotel                2016        22733
3 City Hotel                2017        15817
4 Resort Hotel              2015         6176
5 Resort Hotel              2016        13637
6 Resort Hotel              2017         9125

This data can be efficiently depicted using a histogram as it involves frequencies.

ggplot(yearly_data)+
  geom_bar(aes(x = arrival_date_year, y = yearly_count, fill= hotel), stat = "identity", position = "dodge")

Therefore from the above bar plot both the hotels seem to perform well in the year 2016, hosting more number of guests than any other year.

Visualizing Part-Whole Relationships

Since there are some cancelled bookings in the dataset we can plot the total bookings while depicting a part with canceled bookings. To get such a visualization the following command can be run:

total_yearly_data <- data%>%
                      group_by(hotel, arrival_date_year) %>%
                      summarise(yearly_counts = n(),
                                canceled_bookings = sum(is_canceled))
head(total_yearly_data)

# A tibble: 6 × 4
# Groups:   hotel [2]
  hotel        arrival_date_year yearly_counts canceled_bookings
  <chr>                    <dbl>         <int>             <dbl>
1 City Hotel                2015         13682              6004
2 City Hotel                2016         38140             15407
3 City Hotel                2017         27508             11691
4 Resort Hotel              2015          8314              2138
5 Resort Hotel              2016         18567              4930
6 Resort Hotel              2017         13179              4054

Now the plot depicting the different parts of the data is as follows:

ggplot(total_yearly_data)+
  geom_col(aes(x = arrival_date_year, y = yearly_counts, fill = canceled_bookings))