library(tidyverse)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 6
challenge_6
Harsha Kanaka Eswar Gudipudi
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
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 ⭐⭐⭐⭐⭐
df <- read.csv("_data/hotel_bookings.csv")
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
1 2015-07-01
2 2015-07-01
3 2015-07-02
4 2015-07-02
5 2015-07-03
6 2015-07-03Briefly describe the data
The dataset contains information about hotels including whether a booking was canceled, lead time, arrival date, stays in weekend nights, stays in week nights, number of adults, children, and babies, market segment, distribution channel, whether the guest was a repeat visitor, previous cancellations and bookings, reserved and assigned room types, booking changes, deposit type, agent and company IDs, days in waiting list, customer type, average daily rate, required parking spaces, total special requests, and reservation status. The data covers the years 2015 to 2017.
summary(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
# Get unique values for the market_segment column
unique_market_segment <- unique(df$market_segment)
cat(paste(unique_market_segment, collapse = ", "))Direct, Corporate, Online TA, Offline TA/TO, Complementary, Groups, Undefined, AviationTidy Data (as needed)
I would like to create a plot that displays the trend of reservations whether it is online,direct,etc for each month in a pirticular year. To achieve this, we must first create a new column in the dataset called “arrival-year-month” and aggregate all the bookings into single column.
df_tidy <- df %>%
mutate(date = paste(arrival_date_year, arrival_date_month, arrival_date_day_of_month, sep = "-"),
date = as.Date(date, format = "%Y-%B-%d"))
df_tidy$bookings<-rowSums(df_tidy[, c("adults", "children","babies")], na.rm = TRUE)
head(df_tidy[, c("date", "bookings")], n = 5) date bookings
1 2015-07-01 2
2 2015-07-01 2
3 2015-07-01 1
4 2015-07-01 1
5 2015-07-01 2Time Dependent Visualization
year<-2016
bookings_filtered <- df_tidy %>% filter(format(date, "%Y") == 2016)
# Group the data by month and market segment and calculate the total number of bookings in each group
bookings_grouped <- bookings_filtered %>% group_by(format(date, "%m"), market_segment) %>% summarise(total_bookings = sum(bookings))
print(sum(is.na(bookings_grouped$bookings_filtered)))[1] 0b1<-bookings_grouped
bookings_grouped<-bookings_grouped%>%
filter(market_segment %in% c("Direct", "Online TA"))
bookings_grouped# A tibble: 24 × 3
# Groups: format(date, "%m") [12]
`format(date, "%m")` market_segment total_bookings
<chr> <chr> <dbl>
1 01 Direct 655
2 01 Online TA 1778
3 02 Direct 977
4 02 Online TA 3043
5 03 Direct 933
6 03 Online TA 4761
7 04 Direct 903
8 04 Online TA 5125
9 05 Direct 864
10 05 Online TA 4771
# ℹ 14 more rowsggplot(bookings_grouped, aes(x = `format(date, "%m")`, y = total_bookings, fill = market_segment)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "Month of Booking", y = "Number of Bookings", title = paste("Total Bookings by Market Segment in", year)) +
theme_bw()
Visualizing Part-Whole Relationships
ggplot(b1, aes(x = `format(date, "%m")`, y = total_bookings, fill = market_segment)) +
geom_col() +
labs(title = "Part-Whole Relationships for Bookings by Market Segment and Month",
y = "Total Bookings",
x = "Month") +
theme(legend.position = "bottom")