library(tidyverse)
library(ggplot2)
library(dplyr)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 6_PriyankaThatikonda
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 ⭐⭐⭐⭐⭐
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 consists of 119,390 entries related to hotel bookings. It includes diverse information such as arrival date (year, month, day), hotel type, cancellation status, lead time, duration of stay, number of adults, children, and babies, meal preferences, country of origin, market segment, distribution channel, previous cancellations, reserved and assigned room types, booking changes, deposit type, days on the waiting list, customer type, average daily rate, required car parking spaces, and total number of special requests.
dim(df)[1] 119390 32colnames(df) [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" 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.
To visualize the number of bookings that occurred in 2015 for five countries (USA, BEL, GBR, DEU, FRA) across different months, a new column can be created to represent the total number of bookings combining all age group bookings. This allows us to observe the variation in booking numbers based on seasons or specific months in each country. To achieve this, the month and year can be aggregated to form a new date column.
df <- df %>%
mutate(date_year = paste0(arrival_date_month," ",arrival_date_day_of_month," ",arrival_date_year))
df$date_year %>% head()[1] "July 1 2015" "July 1 2015" "July 1 2015" "July 1 2015" "July 1 2015"
[6] "July 1 2015"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.
df1<-dataTime Dependent Visualization
df %>%
filter(arrival_date_year == 2015) %>%
count(arrival_date_month, hotel) %>%
ggplot(aes(x = arrival_date_month, y = n, fill = hotel)) +
geom_col(position = "dodge") +
labs(x = "Month", y = "Total Bookings", title = "Monthly bookings by hotel") +
scale_fill_manual(values = c("#4f79a7", "#f28d2c"),
name = "Hotel",
labels = c("City Hotel", "Resort Hotel"))
Visualizing Part-Whole Relationships
df %>%
select(arrival_date_month, days_in_waiting_list) %>%
group_by(arrival_date_month) %>%
summarise(max_days_in_waiting_list = max(days_in_waiting_list)) %>%
mutate(month = make_date(year = 2021, month = match(arrival_date_month, month.name))) %>%
ggplot(aes(month, max_days_in_waiting_list)) +
geom_line() +
scale_x_date(date_labels = "%B", date_breaks = "1 month") +
labs(title = "Days in WL per month")