library(tidyverse)
library(ggplot2)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 6
challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_households
abc_poll
Visualizing Time and Relationships
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)
- mutate variables as needed (including sanity checks)
- create at least one graph including time (evolution)
- try to make them “publication” ready (optional)
- Explain why you choose the specific graph type
- Create at least one graph depicting part-whole or flow relationships
- try to make them “publication” ready (optional)
- Explain why you choose the specific graph type
R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.
(be sure to only include the category tags for the data you use!)
Read in data
Read in one (or more) of the following datasets, using the correct R package and command.
- debt ⭐
- fed_rate ⭐⭐
- abc_poll ⭐⭐⭐
- usa_hh ⭐⭐⭐
- hotel_bookings ⭐⭐⭐⭐
- AB_NYC ⭐⭐⭐⭐⭐
We will read the hotel bookings data.
hotel_bookings <- read.csv("_data/hotel_bookings.csv")
# The Dimensions
dim(hotel_bookings)[1] 119390 32 # The Column Names
colnames(hotel_bookings) [1] "hotel" "is_canceled"
[3] "lead_time" "arrival_date_year"
[5] "arrival_date_month" "arrival_date_week_number"
[7] "arrival_date_day_of_month" "stays_in_weekend_nights"
[9] "stays_in_week_nights" "adults"
[11] "children" "babies"
[13] "meal" "country"
[15] "market_segment" "distribution_channel"
[17] "is_repeated_guest" "previous_cancellations"
[19] "previous_bookings_not_canceled" "reserved_room_type"
[21] "assigned_room_type" "booking_changes"
[23] "deposit_type" "agent"
[25] "company" "days_in_waiting_list"
[27] "customer_type" "adr"
[29] "required_car_parking_spaces" "total_of_special_requests"
[31] "reservation_status" "reservation_status_date" head(hotel_bookings) 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 2015-07-03 tail(hotel_bookings) hotel is_canceled lead_time arrival_date_year arrival_date_month
119385 City Hotel 0 21 2017 August
119386 City Hotel 0 23 2017 August
119387 City Hotel 0 102 2017 August
119388 City Hotel 0 34 2017 August
119389 City Hotel 0 109 2017 August
119390 City Hotel 0 205 2017 August
arrival_date_week_number arrival_date_day_of_month
119385 35 30
119386 35 30
119387 35 31
119388 35 31
119389 35 31
119390 35 29
stays_in_weekend_nights stays_in_week_nights adults children babies meal
119385 2 5 2 0 0 BB
119386 2 5 2 0 0 BB
119387 2 5 3 0 0 BB
119388 2 5 2 0 0 BB
119389 2 5 2 0 0 BB
119390 2 7 2 0 0 HB
country market_segment distribution_channel is_repeated_guest
119385 BEL Offline TA/TO TA/TO 0
119386 BEL Offline TA/TO TA/TO 0
119387 FRA Online TA TA/TO 0
119388 DEU Online TA TA/TO 0
119389 GBR Online TA TA/TO 0
119390 DEU Online TA TA/TO 0
previous_cancellations previous_bookings_not_canceled reserved_room_type
119385 0 0 A
119386 0 0 A
119387 0 0 E
119388 0 0 D
119389 0 0 A
119390 0 0 A
assigned_room_type booking_changes deposit_type agent company
119385 A 0 No Deposit 394 NULL
119386 A 0 No Deposit 394 NULL
119387 E 0 No Deposit 9 NULL
119388 D 0 No Deposit 9 NULL
119389 A 0 No Deposit 89 NULL
119390 A 0 No Deposit 9 NULL
days_in_waiting_list customer_type adr required_car_parking_spaces
119385 0 Transient 96.14 0
119386 0 Transient 96.14 0
119387 0 Transient 225.43 0
119388 0 Transient 157.71 0
119389 0 Transient 104.40 0
119390 0 Transient 151.20 0
total_of_special_requests reservation_status reservation_status_date
119385 2 Check-Out 2017-09-06
119386 0 Check-Out 2017-09-06
119387 2 Check-Out 2017-09-07
119388 4 Check-Out 2017-09-07
119389 0 Check-Out 2017-09-07
119390 2 Check-Out 2017-09-07 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
Length:119390
Class :character
Mode :character
unique(hotel_bookings$hotel)[1] "Resort Hotel" "City Hotel" unique(hotel_bookings$reservation_status)[1] "Check-Out" "Canceled" "No-Show" unique(hotel_bookings$country) [1] "PRT" "GBR" "USA" "ESP" "IRL" "FRA" "NULL" "ROU" "NOR" "OMN"
[11] "ARG" "POL" "DEU" "BEL" "CHE" "CN" "GRC" "ITA" "NLD" "DNK"
[21] "RUS" "SWE" "AUS" "EST" "CZE" "BRA" "FIN" "MOZ" "BWA" "LUX"
[31] "SVN" "ALB" "IND" "CHN" "MEX" "MAR" "UKR" "SMR" "LVA" "PRI"
[41] "SRB" "CHL" "AUT" "BLR" "LTU" "TUR" "ZAF" "AGO" "ISR" "CYM"
[51] "ZMB" "CPV" "ZWE" "DZA" "KOR" "CRI" "HUN" "ARE" "TUN" "JAM"
[61] "HRV" "HKG" "IRN" "GEO" "AND" "GIB" "URY" "JEY" "CAF" "CYP"
[71] "COL" "GGY" "KWT" "NGA" "MDV" "VEN" "SVK" "FJI" "KAZ" "PAK"
[81] "IDN" "LBN" "PHL" "SEN" "SYC" "AZE" "BHR" "NZL" "THA" "DOM"
[91] "MKD" "MYS" "ARM" "JPN" "LKA" "CUB" "CMR" "BIH" "MUS" "COM"
[101] "SUR" "UGA" "BGR" "CIV" "JOR" "SYR" "SGP" "BDI" "SAU" "VNM"
[111] "PLW" "QAT" "EGY" "PER" "MLT" "MWI" "ECU" "MDG" "ISL" "UZB"
[121] "NPL" "BHS" "MAC" "TGO" "TWN" "DJI" "STP" "KNA" "ETH" "IRQ"
[131] "HND" "RWA" "KHM" "MCO" "BGD" "IMN" "TJK" "NIC" "BEN" "VGB"
[141] "TZA" "GAB" "GHA" "TMP" "GLP" "KEN" "LIE" "GNB" "MNE" "UMI"
[151] "MYT" "FRO" "MMR" "PAN" "BFA" "LBY" "MLI" "NAM" "BOL" "PRY"
[161] "BRB" "ABW" "AIA" "SLV" "DMA" "PYF" "GUY" "LCA" "ATA" "GTM"
[171] "ASM" "MRT" "NCL" "KIR" "SDN" "ATF" "SLE" "LAO" max(hotel_bookings$stays_in_week_nights)[1] 50 max(hotel_bookings$stays_in_weekend_nights)[1] 19 max(hotel_bookings$adults)[1] 55 max(hotel_bookings$children, na.rm = TRUE)[1] 10 max(hotel_bookings$babies)[1] 10Briefly describe the data
This contains the hotel bookings data from 2015 to 2017 from across 178 countries for 2 hotels - “Resort Hotel”, “City Hotel”. There are a total of 32 columns.
Tidy Data (as needed)
Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.
hotel_bookings<- na.omit(hotel_bookings)Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?
Document your work here.
hotel_bookings$date <- ymd(paste(hotel_bookings$arrival_date_year, hotel_bookings$arrival_date_month, hotel_bookings$arrival_date_day_of_month, sep = "-"))
hotel_bookings <- hotel_bookings%>%
mutate(total_guests_number = rowSums(select(.,adults,children,babies),na.rm = TRUE))Time Dependent Visualization
Lets visualize the date vs total guest count to see how many guests came in that date.
hotel_bookings <- hotel_bookings%>%
group_by(date) %>%
mutate(month_total = sum(total_guests_number)) %>%
ungroup()
ggplot(hotel_bookings, aes(x=date,y=month_total)) +
geom_line() +
xlab("Date") +
ylab("Count of reservations") +
labs(title="Date vs Reservations")
Visualizing Part-Whole Relationships
Lets visualize the month vs number of reservations made in that month.
hotel_bookings <- hotel_bookings %>%
mutate(month = floor_date(date,unit="month")) %>%
group_by(month) %>%
summarise(n=n()) %>%
ungroup()
hotel_bookings %>%
ggplot(aes(month, n)) +
geom_line() +
scale_x_date("Date", breaks="3 months")+
scale_y_continuous() +
xlab("Month") +
ylab("Number of reservations")