library(tidyverse)
library(ggplot2)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 7
challenge_7
hotel_bookings
australian_marriage
air_bnb
eggs
abc_poll
faostat
usa_households
Visualizing Multiple Dimensions
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)
- Recreate at least two graphs from previous exercises, but introduce at least one additional dimension that you omitted before using ggplot functionality (color, shape, line, facet, etc) The goal is not to create unneeded chart ink (Tufte), but to concisely capture variation in additional dimensions that were collapsed in your earlier 2 or 3 dimensional graphs.
- Explain why you choose the specific graph type
- If you haven’t tried in previous weeks, work this week to make your graphs “publication” ready with titles, captions, and pretty axis labels and other viewer-friendly features
R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code. And anyone not familiar with Edward Tufte should check out his fantastic books and courses on data visualizaton.
(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.
- eggs ⭐
- abc_poll ⭐⭐
- australian_marriage ⭐⭐
- hotel_bookings ⭐⭐⭐
- air_bnb ⭐⭐⭐
- us_hh ⭐⭐⭐⭐
- faostat ⭐⭐⭐⭐⭐
hotel_bookings <- read.csv("_data/hotel_bookings.csv")
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 2015-07-03dim(hotel_bookings)[1] 119390 32Briefly describe the data
The data has 119390 rows and 32 columns. It has hotel booking data statistics.
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.
sum(is.na(hotel_bookings))[1] 4sum(is.na(hotel_bookings $ previous_cancellations))[1] 0sum(is.na(hotel_bookings $ children))[1] 4hotel_bookings <- hotel_bookings %>%
replace_na(list(children = 0))
sum(is.na(hotel_bookings $ children))[1] 0Since NA was only 4, I tried to find the column which would be NA. I found children is that column and decided to make it 0.
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_grouped <- hotel_bookings %>%
group_by(customer_type) %>%
summarise(AvgBooking = mean(booking_changes))Visualization with Multiple Dimensions
ggplot(hotel_bookings,aes(customer_type))+ geom_bar() + ggtitle("Count of Customer grouped by Customer Type")
ggplot(hotel_bookings_grouped,aes(x=customer_type, y=AvgBooking)) + geom_point() + ggtitle("Average Booking as per the customer type")
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(month=floor_date(date,unit="month"),
across(c(hotel, assigned_room_type),as.factor)) %>%
count(month, hotel, assigned_room_type,.drop=F)
ggplot(hotel_bookings, aes(month, n, col=assigned_room_type))+
geom_line()