Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 2
challenge_2
railroads
faostat
hotel_bookings
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_bookings <- read_csv('_data/hotel_bookings.csv')
hotel_bookings# A tibble: 119,390 × 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 Resor… 0 342 2015 July 27 1 0 0 2
2 Resor… 0 737 2015 July 27 1 0 0 2
3 Resor… 0 7 2015 July 27 1 0 1 1
4 Resor… 0 13 2015 July 27 1 0 1 1
5 Resor… 0 14 2015 July 27 1 0 2 2
6 Resor… 0 14 2015 July 27 1 0 2 2
7 Resor… 0 0 2015 July 27 1 0 2 2
8 Resor… 0 9 2015 July 27 1 0 2 2
9 Resor… 1 85 2015 July 27 1 0 3 2
10 Resor… 1 75 2015 July 27 1 0 3 2
# … with 119,380 more rows, 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>, …This dataset is a summarising details of hotel bookings which includes data like arrival time, month, year, number of nights, number of adults, children and babies staying. Other interesting details includes whether the guest has previously booked or cancelled bookings at the hotel, hotel room type etc.
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).
This dataset summarises various details about a hotel booking and contains 120k records dating from year 2015 to 2017. It can be seen by the summary command and other analysis of data done below that there are two types of hotels in this dataset - Resort Hotel and City Hotel. Customers from around the world; around 160-170 countries, book these hotels. It can also be seen that on an average, around 37% of the bookings are cancelled and around 3% of the guests are repeated. On an average, customers may need to wait for 2.3 days in the waitlist to confirm a booking and around 57% of these bookings include some special requests. The hotels provide 4 different types of meals to their customers. This dataset is likely gathered from various online and offline channels - from where the booking was made.
Code
summary(hotel_bookings) 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
NA's :4
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. :2017-09-14
Code
as_tibble(hotel_bookings)# A tibble: 119,390 × 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 Resor… 0 342 2015 July 27 1 0 0 2
2 Resor… 0 737 2015 July 27 1 0 0 2
3 Resor… 0 7 2015 July 27 1 0 1 1
4 Resor… 0 13 2015 July 27 1 0 1 1
5 Resor… 0 14 2015 July 27 1 0 2 2
6 Resor… 0 14 2015 July 27 1 0 2 2
7 Resor… 0 0 2015 July 27 1 0 2 2
8 Resor… 0 9 2015 July 27 1 0 2 2
9 Resor… 1 85 2015 July 27 1 0 3 2
10 Resor… 1 75 2015 July 27 1 0 3 2
# … with 119,380 more rows, 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>, …Code
colnames(hotel_bookings)[1] <- c("hotel")
unique_hotel <- unique(bird$hotel)Error in unique(bird$hotel): object 'bird' not foundCode
unique_hotelError in eval(expr, envir, enclos): object 'unique_hotel' not foundCode
colnames(hotel_bookings)[14] <- c("country")
unique_country <- unique(bird$country)Error in unique(bird$country): object 'bird' not foundCode
unique_countryError in eval(expr, envir, enclos): object 'unique_country' not foundCode
nrow(hotel_bookings)[1] 119390Code
colnames(hotel_bookings)[13] <- c("meal")
unique_meals <- unique(bird$meal)Error in unique(bird$meal): object 'bird' not foundCode
unique_mealsError in eval(expr, envir, enclos): object 'unique_meals' not foundCode
colnames(hotel_bookings)[15] <- c("market_segment")
unique_m <- unique(bird$market_segment)Error in unique(bird$market_segment): object 'bird' not foundCode
unique_mError in eval(expr, envir, enclos): object 'unique_m' not foundProvide 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.
Code
grouped_by_customer_type <- hotel_bookings %>%
group_by(customer_type)
grouped_by_customer_type %>%
summarise(
avg_stays_in_week_nights = mean(stays_in_week_nights, nr.rm = TRUE),
avg_stays_in_weekend_nights = mean(stays_in_weekend_nights, nr.rm = TRUE),
avg_days_in_waiting_list = mean(days_in_waiting_list, nr.rm=TRUE),
avg_total_of_special_requests = mean(total_of_special_requests, nr.rm=TRUE)
)# A tibble: 4 × 5
customer_type avg_stays_in_week_nights avg_stays_in_weeken…¹ avg_d…² avg_t…³
<chr> <dbl> <dbl> <dbl> <dbl>
1 Contract 3.85 1.47 0.0395 0.729
2 Group 2.06 0.825 0.369 0.645
3 Transient 2.51 0.939 1.32 0.632
4 Transient-Party 2.26 0.802 6.32 0.329
# … with abbreviated variable names ¹avg_stays_in_weekend_nights,
# ²avg_days_in_waiting_list, ³avg_total_of_special_requestsExplain and Interpret
I chose to group by the type of customers booking the two hotels and found the average days these customers stayed in week and weekend nights, the number of days they had to wait for and their special requests. I chose this group and these values because I think this shows important insights of the trends and patterns of different groups of customers. This type of information can be useful for the hotels to provide better service and maybe tier based prices based on the type of customer. For example, the “Transient-Party” customer type typically stay the longest on the waiting list while they stay on weekend nights the least. But we can see that “Transient” customers stay more in weekend nights and still have the second highest average days in waiting list. The hotels could try to make a priority queue from their waiting list and prioritise “Transient” customers on weekend nights over “Transient-Party”. This might lead to better customer service and satisfaction.