Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 4 by Jinxia Niu
challenge_4
hotel_bookings
Jinxia Niu
More data wrangling: pivoting
Challenge Overview
Today’s challenge is to:
- read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
- tidy data (as needed, including sanity checks)
- identify variables that need to be mutated
- mutate variables and sanity check all mutations
Read in data
Read in one (or more) of the following datasets, using the correct R package and command.
- abc_poll.csv ⭐
- poultry_tidy.xlsx or organiceggpoultry.xls⭐⭐
- FedFundsRate.csv⭐⭐⭐
- hotel_bookings.csv⭐⭐⭐⭐
- debt_in_trillions.xlsx ⭐⭐⭐⭐⭐
Code
library(tidyverse)
hotel_bookings<- read.csv("_data/hotel_bookings.csv")
dim(hotel_bookings)[1] 119390 32Code
head(hotel_bookings,10) 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
7 Resort Hotel 0 0 2015 July
8 Resort Hotel 0 9 2015 July
9 Resort Hotel 1 85 2015 July
10 Resort Hotel 1 75 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
7 27 1 0
8 27 1 0
9 27 1 0
10 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
7 2 2 0 0 BB PRT Direct
8 2 2 0 0 FB PRT Direct
9 3 2 0 0 BB PRT Online TA
10 3 2 0 0 HB PRT Offline TA/TO
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
7 Direct 0 0
8 Direct 0 0
9 TA/TO 0 0
10 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
7 0 C C
8 0 C C
9 0 A A
10 0 D D
booking_changes deposit_type agent company days_in_waiting_list
1 3 No Deposit NULL NULL 0
2 4 No Deposit NULL NULL 0
3 0 No Deposit NULL NULL 0
4 0 No Deposit 304 NULL 0
5 0 No Deposit 240 NULL 0
6 0 No Deposit 240 NULL 0
7 0 No Deposit NULL NULL 0
8 0 No Deposit 303 NULL 0
9 0 No Deposit 240 NULL 0
10 0 No Deposit 15 NULL 0
customer_type adr required_car_parking_spaces total_of_special_requests
1 Transient 0.0 0 0
2 Transient 0.0 0 0
3 Transient 75.0 0 0
4 Transient 75.0 0 0
5 Transient 98.0 0 1
6 Transient 98.0 0 1
7 Transient 107.0 0 0
8 Transient 103.0 0 1
9 Transient 82.0 0 1
10 Transient 105.5 0 0
reservation_status reservation_status_date
1 Check-Out 2015-07-01
2 Check-Out 2015-07-01
3 Check-Out 2015-07-02
4 Check-Out 2015-07-02
5 Check-Out 2015-07-03
6 Check-Out 2015-07-03
7 Check-Out 2015-07-03
8 Check-Out 2015-07-03
9 Canceled 2015-05-06
10 Canceled 2015-04-22Briefly describe the data
It’s a hotel booking data set of 32 variables,119390 observations.There are several redundant variables, especially the arrival date_year,arrival_date_month and arrival_date_day_of_the_month ones.
Identify variables that need to be mutated
We need to use mutate() and make_date() from the lubridate package to create just one date format. In addition, because the arrival-month is currently shown as characters(eg.July, August) instead of numbers, we also need to use case_when() from the dplyr package to convert them first into numbers.
##Note:The case_when() function from the dplyr package in R can be used to create new variables from existing variables.
This function uses the following basic syntax:
library(dplyr)
df %>% mutate(new_var = case_when(var1 < 15 ~ ‘low’, var2 < 25 ~ ‘med’, TRUE ~ ‘high’)) Note that TRUE is equivalent to an “else” statement.
Code
library(dplyr)
library(lubridate)
hotel_bookings <- hotel_bookings %>%
mutate(month = case_when(
arrival_date_month == "January" ~ 1,
arrival_date_month == "February" ~ 2,
arrival_date_month == "March" ~ 3,
arrival_date_month == "April" ~ 4,
arrival_date_month == "May" ~ 5,
arrival_date_month == "June" ~ 6,
arrival_date_month == "July" ~ 7,
arrival_date_month == "August" ~ 8,
arrival_date_month == "September" ~ 9,
arrival_date_month == "October" ~ 10,
arrival_date_month == "November" ~ 11,
arrival_date_month == "December" ~ 12,
)) %>%
mutate(arrival_date = make_date(arrival_date_year, month, arrival_date_day_of_month)) %>%
select(-c(arrival_date_year, arrival_date_month, arrival_date_day_of_month, month, arrival_date_week_number)) %>%
select(hotel, arrival_date, everything())
dim(hotel_bookings)[1] 119390 29