library(tidyverse)
library(skimr)# From TidyTuesday: https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md
hotels <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv")Warm up! Take a look at an overview of the data with the skim() function.
Note: I already gave you the answer to this exercise. You just need to knit the document and view the output. A definition of all variables is given in the Data dictionary section at the end, though you don’t need to familiarize yourself with all variables in order to work through these exercises.
skim(hotels)| Name | hotels |
| Number of rows | 119390 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| character | 13 |
| Date | 1 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| hotel | 0 | 1 | 10 | 12 | 0 | 2 | 0 |
| arrival_date_month | 0 | 1 | 3 | 9 | 0 | 12 | 0 |
| meal | 0 | 1 | 2 | 9 | 0 | 5 | 0 |
| country | 0 | 1 | 2 | 4 | 0 | 178 | 0 |
| market_segment | 0 | 1 | 6 | 13 | 0 | 8 | 0 |
| distribution_channel | 0 | 1 | 3 | 9 | 0 | 5 | 0 |
| reserved_room_type | 0 | 1 | 1 | 1 | 0 | 10 | 0 |
| assigned_room_type | 0 | 1 | 1 | 1 | 0 | 12 | 0 |
| deposit_type | 0 | 1 | 10 | 10 | 0 | 3 | 0 |
| agent | 0 | 1 | 1 | 4 | 0 | 334 | 0 |
| company | 0 | 1 | 1 | 4 | 0 | 353 | 0 |
| customer_type | 0 | 1 | 5 | 15 | 0 | 4 | 0 |
| reservation_status | 0 | 1 | 7 | 9 | 0 | 3 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| reservation_status_date | 0 | 1 | 2014-10-17 | 2017-09-14 | 2016-08-07 | 926 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| is_canceled | 0 | 1 | 0.37 | 0.48 | 0.00 | 0.00 | 0.00 | 1 | 1 | ▇▁▁▁▅ |
| lead_time | 0 | 1 | 104.01 | 106.86 | 0.00 | 18.00 | 69.00 | 160 | 737 | ▇▂▁▁▁ |
| arrival_date_year | 0 | 1 | 2016.16 | 0.71 | 2015.00 | 2016.00 | 2016.00 | 2017 | 2017 | ▃▁▇▁▆ |
| arrival_date_week_number | 0 | 1 | 27.17 | 13.61 | 1.00 | 16.00 | 28.00 | 38 | 53 | ▅▇▇▇▅ |
| arrival_date_day_of_month | 0 | 1 | 15.80 | 8.78 | 1.00 | 8.00 | 16.00 | 23 | 31 | ▇▇▇▇▆ |
| stays_in_weekend_nights | 0 | 1 | 0.93 | 1.00 | 0.00 | 0.00 | 1.00 | 2 | 19 | ▇▁▁▁▁ |
| stays_in_week_nights | 0 | 1 | 2.50 | 1.91 | 0.00 | 1.00 | 2.00 | 3 | 50 | ▇▁▁▁▁ |
| adults | 0 | 1 | 1.86 | 0.58 | 0.00 | 2.00 | 2.00 | 2 | 55 | ▇▁▁▁▁ |
| children | 4 | 1 | 0.10 | 0.40 | 0.00 | 0.00 | 0.00 | 0 | 10 | ▇▁▁▁▁ |
| babies | 0 | 1 | 0.01 | 0.10 | 0.00 | 0.00 | 0.00 | 0 | 10 | ▇▁▁▁▁ |
| is_repeated_guest | 0 | 1 | 0.03 | 0.18 | 0.00 | 0.00 | 0.00 | 0 | 1 | ▇▁▁▁▁ |
| previous_cancellations | 0 | 1 | 0.09 | 0.84 | 0.00 | 0.00 | 0.00 | 0 | 26 | ▇▁▁▁▁ |
| previous_bookings_not_canceled | 0 | 1 | 0.14 | 1.50 | 0.00 | 0.00 | 0.00 | 0 | 72 | ▇▁▁▁▁ |
| booking_changes | 0 | 1 | 0.22 | 0.65 | 0.00 | 0.00 | 0.00 | 0 | 21 | ▇▁▁▁▁ |
| days_in_waiting_list | 0 | 1 | 2.32 | 17.59 | 0.00 | 0.00 | 0.00 | 0 | 391 | ▇▁▁▁▁ |
| adr | 0 | 1 | 101.83 | 50.54 | -6.38 | 69.29 | 94.58 | 126 | 5400 | ▇▁▁▁▁ |
| required_car_parking_spaces | 0 | 1 | 0.06 | 0.25 | 0.00 | 0.00 | 0.00 | 0 | 8 | ▇▁▁▁▁ |
| total_of_special_requests | 0 | 1 | 0.57 | 0.79 | 0.00 | 0.00 | 0.00 | 1 | 5 | ▇▁▁▁▁ |
Look at the documentation of the skimr package. And answer the following questions.
Can skimr functions be used in a pipeline? Yes, skimr functions can be used in a pipeline. According to the package documentation, “…by design, the main focus of skimr is on data frames. It is intended to fit well within a data pipeline and relies extensively on tidyverse vocabulary, which focuses on data frames.”
How is skimr used in this project? skim() is used in this project to obtain a larger set of statistics than the base summary() function in R. It is used to get a sense of statistical information that may be important for later analysis.
Do you think it might be useful for you? Yes, I think it can be most useful in cases with grouped data frames. The core function of skimr is skim(), and according to the documentation it is designed to work with grouped data frames and will try to coerce other objects to data frames whenever possible.
Are people traveling on a whim? Roughly 6% or 6174 out of 119390 people traveled with lead_time < 1 which is not an insignificant value. If the number of people traveling with lead_time < 1 was less than 1 or near 0 percent I may assume that the value is insignificant and people are not traveling on a whim but that is not the case here.
Let’s see…
Fill in the blanks for filtering for hotel bookings where the guest is not from the US (country code "USA") and the lead_time is less than 1 day.
Note: You will need to set eval=TRUE when you have an answer you want to try out.
hotels %>%
filter(
country != "USA",
lead_time < 1
)## # A tibble: 6,174 x 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo…
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Reso… 0 0 2015 July
## 2 Reso… 0 0 2015 July
## 3 Reso… 0 0 2015 July
## 4 Reso… 0 0 2015 July
## 5 Reso… 0 0 2015 July
## 6 Reso… 0 0 2015 July
## 7 Reso… 0 0 2015 July
## 8 Reso… 0 0 2015 July
## 9 Reso… 0 0 2015 July
## 10 Reso… 0 0 2015 July
## # … with 6,164 more rows, and 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>, 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>,
## # reservation_status_date <date>hotels %>%
count(hotel)## # A tibble: 2 x 2
## hotel n
## * <chr> <int>
## 1 City Hotel 79330
## 2 Resort Hotel 40060How many bookings involve at least 1 child or baby? (Note the logical op, PC) 9332 bookings involve at least 1 child or a baby.
In the following chunk, replace
[AT LEAST] with the logical operator for “at least” (in two places)[OR] with the logical operator for “or”Note: You will need to set eval=TRUE when you have an answer you want to try out.
hotels %>%
filter(
children >= 1 | babies >= 1
)## # A tibble: 9,332 x 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo…
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Reso… 0 18 2015 July
## 2 Reso… 1 47 2015 July
## 3 Reso… 0 1 2015 July
## 4 Reso… 0 10 2015 July
## 5 Reso… 1 79 2015 July
## 6 Reso… 0 101 2015 July
## 7 Reso… 0 92 2015 July
## 8 Reso… 1 26 2015 July
## 9 Reso… 0 102 2015 July
## 10 Reso… 0 78 2015 July
## # … with 9,322 more rows, and 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>, 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>,
## # reservation_status_date <date>Do you think it’s more likely to find bookings with children or babies in city hotels or resort hotels? I think it’s more likely to find booking with children or babies in resort hotel as compared to city hotels.
Test your intuition. Using filter() determine the number of bookings in resort hotels that have more than 1 child or baby in the room? Then, do the same for city hotels, and compare the numbers of rows in the resulting filtered data frames.
hotels %>%
filter(
hotel == 'Resort Hotel',
children > 1 | babies >= 1
)## # A tibble: 2,182 x 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo…
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Reso… 1 47 2015 July
## 2 Reso… 0 1 2015 July
## 3 Reso… 0 10 2015 July
## 4 Reso… 0 92 2015 July
## 5 Reso… 1 26 2015 July
## 6 Reso… 0 102 2015 July
## 7 Reso… 0 78 2015 July
## 8 Reso… 0 2 2015 July
## 9 Reso… 1 34 2015 July
## 10 Reso… 0 97 2015 July
## # … with 2,172 more rows, and 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>, 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>,
## # reservation_status_date <date>hotels %>%
filter(
hotel != 'Resort Hotel',
children > 1 | babies >= 1
)## # A tibble: 2,432 x 32
## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo…
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 City… 1 100 2015 July
## 2 City… 0 67 2015 July
## 3 City… 0 122 2015 July
## 4 City… 0 6 2015 August
## 5 City… 0 7 2015 August
## 6 City… 0 13 2015 August
## 7 City… 0 13 2015 August
## 8 City… 0 0 2015 August
## 9 City… 0 0 2015 August
## 10 City… 0 1 2015 August
## # … with 2,422 more rows, and 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>, 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>,
## # reservation_status_date <date>Create a frequency table of the number of adults in a booking. Display the results in descending order so the most common observation is on top. What is the most common number of adults in bookings in this dataset? The most common number of adults in bookings in this dataset is 2.
Are there any surprising results? I found it surprising that there were 403 bookings for 0 adults. Perhaps these were bookings that were canceled or a no-show
Note: Don’t forget to label your R chunk as well (where it says label-me-1). Your label should be short, informative, and shouldn’t include spaces. It also shouldn’t repeat a previous label, otherwise R Markdown will give you an error about repeated R chunk labels.
hotels %>%
count(adults, sort = TRUE)## # A tibble: 14 x 2
## adults n
## <dbl> <int>
## 1 2 89680
## 2 1 23027
## 3 3 6202
## 4 0 403
## 5 4 62
## 6 26 5
## 7 5 2
## 8 20 2
## 9 27 2
## 10 6 1
## 11 10 1
## 12 40 1
## 13 50 1
## 14 55 1Repeat Exercise 5, once for canceled bookings (is_canceled coded as 1) and once for not canceled bookings (is_canceled coded as 0). What does this reveal about the surprising results you spotted in the previous exercise? The number of canceled bookings is 44224 which reveals that perhaps the results spotted in the previous exercise were not canceled bookings because at 403 that number was significantly smaller than the results in this exercise.
Therefore, I would guess that the 403 bookings with 0 adults in the previous exercise perhaps correspond to the number of children traveling alone that have bi-coastal families.
Note: Don’t forget to label your R chunk as well (where it says label-me-2).
hotels %>%
count(is_canceled == 1, sort = TRUE)## # A tibble: 2 x 2
## `is_canceled == 1` n
## <lgl> <int>
## 1 FALSE 75166
## 2 TRUE 44224hotels %>%
count(is_canceled == 0, sort = TRUE)## # A tibble: 2 x 2
## `is_canceled == 0` n
## <lgl> <int>
## 1 TRUE 75166
## 2 FALSE 44224Calculate minimum, mean, median, and maximum average daily rate (adr) grouped by hotel type so that you can get these statistics separately for resort and city hotels. Which type of hotel is higher, on average? City hotels are higher, on average.
hotels %>%
group_by(hotel) %>%
summarize(minimum = min(adr),
mean = mean(adr, na.rm = TRUE),
median = median(adr, na.rm = TRUE),
max_average_daily_rate = max(adr, na.rm = TRUE)
)## # A tibble: 2 x 5
## hotel minimum mean median max_average_daily_rate
## * <chr> <dbl> <dbl> <dbl> <dbl>
## 1 City Hotel 0 105. 99.9 5400
## 2 Resort Hotel -6.38 95.0 75 508We observe two unusual values in the summary statistics above – a negative minimum, and a very high maximum). What types of hotels are these? I would assume that high-end luxury hotels are a factor that contribute to a high maximum average daily rate of 5400 for city hotels while the negative minimum for resort hotels could be an input error.
Locate these observations in the dataset and find out the arrival date (year and month) as well as how many people (adults, children, and babies) stayed in the room. You can investigate the data in the viewer to locate these values, but preferably you should identify them in a reproducible way with some code.
Hint: For example, you can filter for the given adr amounts and select the relevant columns.
hotels %>%
filter(
adr == 5400 | adr == -6.38
) %>%
select (
arrival_date_month, arrival_date_year, babies, adults, children
)## # A tibble: 2 x 5
## arrival_date_month arrival_date_year babies adults children
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 March 2017 0 2 0
## 2 March 2016 0 2 0Below is the full data dictionary. Note that it is long (there are lots of variables in the data), but we will be using a limited set of the variables for our analysis.
| variable | class | description |
|---|---|---|
| hotel | character | Hotel (H1 = Resort Hotel or H2 = City Hotel) |
| is_canceled | double | Value indicating if the booking was canceled (1) or not (0) |
| lead_time | double | Number of days that elapsed between the entering date of the booking into the PMS and the arrival date |
| arrival_date_year | double | Year of arrival date |
| arrival_date_month | character | Month of arrival date |
| arrival_date_week_number | double | Week number of year for arrival date |
| arrival_date_day_of_month | double | Day of arrival date |
| stays_in_weekend_nights | double | Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel |
| stays_in_week_nights | double | Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel |
| adults | double | Number of adults |
| children | double | Number of children |
| babies | double | Number of babies |
| meal | character | Type of meal booked. Categories are presented in standard hospitality meal packages: Undefined/SC – no meal package; BB – Bed & Breakfast; HB – Half board (breakfast and one other meal – usually dinner); FB – Full board (breakfast, lunch and dinner) |
| country | character | Country of origin. Categories are represented in the ISO 3155–3:2013 format |
| market_segment | character | Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators” |
| distribution_channel | character | Booking distribution channel. The term “TA” means “Travel Agents” and “TO” means “Tour Operators” |
| is_repeated_guest | double | Value indicating if the booking name was from a repeated guest (1) or not (0) |
| previous_cancellations | double | Number of previous bookings that were cancelled by the customer prior to the current booking |
| previous_bookings_not_canceled | double | Number of previous bookings not cancelled by the customer prior to the current booking |
| reserved_room_type | character | Code of room type reserved. Code is presented instead of designation for anonymity reasons |
| assigned_room_type | character | Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g. overbooking) or by customer request. Code is presented instead of designation for anonymity reasons |
| booking_changes | double | Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation |
| deposit_type | character | Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories: No Deposit – no deposit was made; Non Refund – a deposit was made in the value of the total stay cost; Refundable – a deposit was made with a value under the total cost of stay. |
| agent | character | ID of the travel agency that made the booking |
| company | character | ID of the company/entity that made the booking or responsible for paying the booking. ID is presented instead of designation for anonymity reasons |
| days_in_waiting_list | double | Number of days the booking was in the waiting list before it was confirmed to the customer |
| customer_type | character | Type of booking, assuming one of four categories: Contract - when the booking has an allotment or other type of contract associated to it; Group – when the booking is associated to a group; Transient – when the booking is not part of a group or contract, and is not associated to other transient booking; Transient-party – when the booking is transient, but is associated to at least other transient booking |
| adr | double | Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights |
| required_car_parking_spaces | double | Number of car parking spaces required by the customer |
| total_of_special_requests | double | Number of special requests made by the customer (e.g. twin bed or high floor) |
| reservation_status | character | Reservation last status, assuming one of three categories: Canceled – booking was canceled by the customer; Check-Out – customer has checked in but already departed; No-Show – customer did not check-in and did inform the hotel of the reason why |
| reservation_status_date | double | Date at which the last status was set. This variable can be used in conjunction with the ReservationStatus to understand when was the booking canceled or when did the customer checked-out of the hotel |