Your task is to: Choose any three of the “wide” datasets identified in the course Slack channel. (You may use your own dataset; please don’t use my Sample Post dataset, since that was used in your previous assignment!) For each of the three chosen datasets: • Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below. • Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. [Most of your grade will be based on this step!] • Perform the analysis requested in the discussion item. • Your code should be in an R Markdown file, posted to rpubs.com, and should include narrative descriptions of your data cleanup work, analysis, and conclusions.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── 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(dplyr)
library(lubridate)
library(anytime)This dataset contains information which compares booking information between a city hotel and a resort hotel.
rawlink_hotel <- 'https://raw.githubusercontent.com/rkasa01/DATA607_Project2_Data/main/hotel_bookings.csv'
hotel_data <- read.csv(rawlink_hotel)
head(hotel_data)## 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 2015-07-03When I was looking at this dataset, a question popped into my mind! I wanted to know what was the highest and lowest average daily rate.
To answer this question, I removed any cancelled reservations and I first tidied the data a bit like so:
adr_hotel_data <- hotel_data %>%
filter(is_canceled > 0) %>%
select(country, hotel, babies, children, adults, adr)
head(adr_hotel_data)## country hotel babies children adults adr
## 1 PRT Resort Hotel 0 0 2 82.0
## 2 PRT Resort Hotel 0 0 2 105.5
## 3 PRT Resort Hotel 0 0 2 123.0
## 4 PRT Resort Hotel 0 0 2 107.0
## 5 PRT Resort Hotel 0 0 2 108.3
## 6 PRT Resort Hotel 0 0 3 108.8highest_adr <- adr_hotel_data %>%
summarize(highest_adr = max(adr, na.rm = TRUE))
lowest_adr <- adr_hotel_data %>%
summarize(lowest_adr = min(adr, na.rm = TRUE))
print(highest_adr)## highest_adr
## 1 5400print(lowest_adr)## lowest_adr
## 1 0It looks like the highest average daily rate was $5400 and the lowest was $0 – both of which are drastically different rates!