Code
library(tidyverse)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 4 Instructions
challenge_4
abc_poll
eggs
fed_rates
hotel_bookings
debt
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
#read the data
hotel_booking_data <- read_csv("_data/hotel_bookings.csv")
hotel_booking_data# 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
# find the unique values in all the columns
rapply(hotel_booking_data,function(x)length(unique(x))) hotel is_canceled
2 2
lead_time arrival_date_year
479 3
arrival_date_month arrival_date_week_number
12 53
arrival_date_day_of_month stays_in_weekend_nights
31 17
stays_in_week_nights adults
35 14
children babies
6 5
meal country
5 178
market_segment distribution_channel
8 5
is_repeated_guest previous_cancellations
2 15
previous_bookings_not_canceled reserved_room_type
73 10
assigned_room_type booking_changes
12 21
deposit_type agent
3 334
company days_in_waiting_list
353 128
customer_type adr
4 8879
required_car_parking_spaces total_of_special_requests
5 6
reservation_status reservation_status_date
3 926 Briefly describe the data
The following dataset comprises 32 distinct fields and 119390 observations. Each record relates to a reservation that a client has made. With each booking, numerous pieces of information are recorded. It contains information such as arrival date, length of stay, meals, kind of reserved accommodation, client type, reservation status, number of adults/children, and many other things. Entries in the data come from many countries throughout the world.
Tidy Data (as needed)
Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.
Code
# in order to check whether there are NULL fields
table(hotel_booking_data$country)
ABW AGO AIA ALB AND ARE ARG ARM ASM ATA ATF AUS AUT
2 362 1 12 7 51 214 8 1 2 1 426 1263
AZE BDI BEL BEN BFA BGD BGR BHR BHS BIH BLR BOL BRA
17 1 2342 3 1 12 75 5 1 13 26 10 2224
BRB BWA CAF CHE CHL CHN CIV CMR CN COL COM CPV CRI
4 1 5 1730 65 999 6 10 1279 71 2 24 19
CUB CYM CYP CZE DEU DJI DMA DNK DOM DZA ECU EGY ESP
8 1 51 171 7287 1 1 435 14 103 27 32 8568
EST ETH FIN FJI FRA FRO GAB GBR GEO GGY GHA GIB GLP
83 3 447 1 10415 5 4 12129 22 3 4 18 2
GNB GRC GTM GUY HKG HND HRV HUN IDN IMN IND IRL IRN
9 128 4 1 29 1 100 230 35 2 152 3375 83
IRQ ISL ISR ITA JAM JEY JOR JPN KAZ KEN KHM KIR KNA
14 57 669 3766 6 8 21 197 19 6 2 1 2
KOR KWT LAO LBN LBY LCA LIE LKA LTU LUX LVA MAC MAR
133 16 2 31 8 1 3 7 81 287 55 16 259
MCO MDG MDV MEX MKD MLI MLT MMR MNE MOZ MRT MUS MWI
4 1 12 85 10 1 18 1 5 67 1 7 2
MYS MYT NAM NCL NGA NIC NLD NOR NPL NULL NZL OMN PAK
28 2 1 1 34 1 2104 607 1 488 74 18 14
PAN PER PHL PLW POL PRI PRT PRY PYF QAT ROU RUS RWA
9 29 40 1 919 12 48590 4 1 15 500 632 2
SAU SDN SEN SGP SLE SLV SMR SRB STP SUR SVK SVN SWE
48 1 11 39 1 2 1 101 2 5 65 57 1024
SYC SYR TGO THA TJK TMP TUN TUR TWN TZA UGA UKR UMI
2 3 2 59 9 3 39 248 51 5 2 68 1
URY USA UZB VEN VGB VNM ZAF ZMB ZWE
32 2097 4 26 1 8 80 2 4 Code
#removing the null entries found above
hotel_booking_data <- hotel_booking_data %>%
filter(!(country == "NULL"))Code
#in order to know the data type of each field
sapply(hotel_booking_data, class) hotel is_canceled
"character" "numeric"
lead_time arrival_date_year
"numeric" "numeric"
arrival_date_month arrival_date_week_number
"character" "numeric"
arrival_date_day_of_month stays_in_weekend_nights
"numeric" "numeric"
stays_in_week_nights adults
"numeric" "numeric"
children babies
"numeric" "numeric"
meal country
"character" "character"
market_segment distribution_channel
"character" "character"
is_repeated_guest previous_cancellations
"numeric" "numeric"
previous_bookings_not_canceled reserved_room_type
"numeric" "character"
assigned_room_type booking_changes
"character" "numeric"
deposit_type agent
"character" "character"
company days_in_waiting_list
"character" "numeric"
customer_type adr
"character" "numeric"
required_car_parking_spaces total_of_special_requests
"numeric" "numeric"
reservation_status reservation_status_date
"character" "Date" We can observe from the analysis above that two fields, Agents and Company, both have numerical values but are tagged as character datatypes. This is because it has NULL values in it. We can remove the NULL values converting it NA and alter the datatype to numeric. This change will help us for easier analysis. A single field named arrival date can also be used to combine the arrival date in year, month, and date formats. ## Identify variables that need to be mutated
Are there any variables that require mutation to be usable in your analysis stream? For example, are all time variables correctly coded as dates? Are all string variables reduced and cleaned to sensible categories? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?
Document your work here.
Code
h_book_mut <- hotel_booking_data %>%
mutate(arrival_date = str_c(arrival_date_day_of_month,
arrival_date_month,
arrival_date_year, sep="/"),
arrival_date = dmy(arrival_date),
total_guests = adults + children + babies) %>%
select(-c(arrival_date_day_of_month,arrival_date_month,arrival_date_year))
h_book_mut# A tibble: 118,902 × 31
hotel is_ca…¹ lead_…² arriv…³ stays…⁴ stays…⁵ adults child…⁶ babies meal
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 Resort H… 0 342 27 0 0 2 0 0 BB
2 Resort H… 0 737 27 0 0 2 0 0 BB
3 Resort H… 0 7 27 0 1 1 0 0 BB
4 Resort H… 0 13 27 0 1 1 0 0 BB
5 Resort H… 0 14 27 0 2 2 0 0 BB
6 Resort H… 0 14 27 0 2 2 0 0 BB
7 Resort H… 0 0 27 0 2 2 0 0 BB
8 Resort H… 0 9 27 0 2 2 0 0 FB
9 Resort H… 1 85 27 0 3 2 0 0 BB
10 Resort H… 1 75 27 0 3 2 0 0 HB
# … with 118,892 more rows, 21 more variables: 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>, …Code
# to find the range of the arrival date
summary(h_book_mut$arrival_date) Min. 1st Qu. Median Mean 3rd Qu. Max.
"2015-07-01" "2016-03-14" "2016-09-07" "2016-08-29" "2017-03-19" "2017-08-31" The dates of arrival, as seen from above, range from July 2015 to August 2017.
The lead time indicates the beginning of the entire hotel reservation. By deducting the lead time from the arrival date, one may determine the booking date.
Code
h_book_mut <- h_book_mut %>%
mutate(booking_date = arrival_date - lead_time)
h_book_mut# A tibble: 118,902 × 32
hotel is_ca…¹ lead_…² arriv…³ stays…⁴ stays…⁵ adults child…⁶ babies meal
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 Resort H… 0 342 27 0 0 2 0 0 BB
2 Resort H… 0 737 27 0 0 2 0 0 BB
3 Resort H… 0 7 27 0 1 1 0 0 BB
4 Resort H… 0 13 27 0 1 1 0 0 BB
5 Resort H… 0 14 27 0 2 2 0 0 BB
6 Resort H… 0 14 27 0 2 2 0 0 BB
7 Resort H… 0 0 27 0 2 2 0 0 BB
8 Resort H… 0 9 27 0 2 2 0 0 FB
9 Resort H… 1 85 27 0 3 2 0 0 BB
10 Resort H… 1 75 27 0 3 2 0 0 HB
# … with 118,892 more rows, 22 more variables: country <chr>,
# market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
# deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>,
# customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>,
# total_of_special_requests <dbl>, reservation_status <chr>, …Code
unique(hotel_booking_data$reservation_status)[1] "Check-Out" "Canceled" "No-Show" Code
h_book_mut <- h_book_mut %>%
mutate(across(c(agent, company),~ replace(.,str_detect(., "NULL"), NA))) %>% mutate_at(vars(agent, company),as.numeric)
is.numeric(h_book_mut$agent)[1] TRUECode
is.numeric(h_book_mut$company)[1] TRUE