library(tidyverse)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 6
challenge_6
Priyanka Perumalla
hotel_bookings
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
Showing first 5 columns in the data
df <- read.csv("_data/hotel_bookings.csv")
head(df, n = 5)[,1:5] 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 Julysummary(df) 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
Briefly describe the data
The data provided has information about hotel bookings. There are a total of 119,390 entries in the dataset. The dataset includes information such as the type of hotel, whether the booking was canceled, lead time, arrival year, month and day, number of nights stayed, the number of adults, children and babies, meal type, country of origin, market segment, distribution channel, previous cancellations, reserved and assigned room type, booking changes, deposit type, days on the waiting list, customer type, average daily rate, required car parking spaces, and total number of special requests.
No of countries present in the data:
unique_country <- unique(df$country)
cat(paste(unique_country, collapse = ", "))PRT, GBR, USA, ESP, IRL, FRA, NULL, ROU, NOR, OMN, ARG, POL, DEU, BEL, CHE, CN, GRC, ITA, NLD, DNK, RUS, SWE, AUS, EST, CZE, BRA, FIN, MOZ, BWA, LUX, SVN, ALB, IND, CHN, MEX, MAR, UKR, SMR, LVA, PRI, SRB, CHL, AUT, BLR, LTU, TUR, ZAF, AGO, ISR, CYM, ZMB, CPV, ZWE, DZA, KOR, CRI, HUN, ARE, TUN, JAM, HRV, HKG, IRN, GEO, AND, GIB, URY, JEY, CAF, CYP, COL, GGY, KWT, NGA, MDV, VEN, SVK, FJI, KAZ, PAK, IDN, LBN, PHL, SEN, SYC, AZE, BHR, NZL, THA, DOM, MKD, MYS, ARM, JPN, LKA, CUB, CMR, BIH, MUS, COM, SUR, UGA, BGR, CIV, JOR, SYR, SGP, BDI, SAU, VNM, PLW, QAT, EGY, PER, MLT, MWI, ECU, MDG, ISL, UZB, NPL, BHS, MAC, TGO, TWN, DJI, STP, KNA, ETH, IRQ, HND, RWA, KHM, MCO, BGD, IMN, TJK, NIC, BEN, VGB, TZA, GAB, GHA, TMP, GLP, KEN, LIE, GNB, MNE, UMI, MYT, FRO, MMR, PAN, BFA, LBY, MLI, NAM, BOL, PRY, BRB, ABW, AIA, SLV, DMA, PYF, GUY, LCA, ATA, GTM, ASM, MRT, NCL, KIR, SDN, ATF, SLE, LAOTidy Data (as needed)
Creating a new column for total no of bookings combining all the age group booking to visualize no of bookings happened in 2015 for 5 countries: USA, BEL, GBR, DEU, FRA over the span of every month bookings. It helps to see on which seasons or depending on month the no of bookings are high in a country. Aggregating month and year to form the new date.
# create new date column adding all the bookings
df$total_bookings<-rowSums(df[, c("adults", "children","babies")], na.rm = TRUE)
df$date <- as.Date(paste(df$arrival_date_year, df$arrival_date_month, df$arrival_date_day_of_month, sep = "-"), format = "%Y-%B-%d")
head(df) 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 total_bookings date
1 2015-07-01 2 2015-07-01
2 2015-07-01 2 2015-07-01
3 2015-07-02 1 2015-07-01
4 2015-07-02 1 2015-07-01
5 2015-07-03 2 2015-07-01
6 2015-07-03 2 2015-07-01df1<-dfTime Dependent Visualization
#summing all the bookings of a month w.r.t country
df <- df %>% filter(arrival_date_year == 2015)%>% group_by(arrival_date_month, country) %>% summarise(monthly_bookings =sum(total_bookings))
df<-df%>% filter(country %in% c("USA", "BEL", "GBR", "DEU", "FRA"))
ggplot(df, aes(x = arrival_date_month, y = monthly_bookings, fill = country)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "Month of Booking", y = "Total Bookings", title = paste("Total Bookings in a country in 2015")) +
theme_bw()+
scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2"))
Visualizing Part-Whole Relationships
ggplot(df, aes(x = arrival_date_month, y = monthly_bookings, fill = country)) +
geom_col() +
labs(title = "Visualizing Part-Whole Relationships Country and Month",
y = "Monthly Bookings",
x = "Month") +
theme_bw()+
scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2"))