Annotations Using ggplot2 - Hotel Booking Dataset
Keman Xiang
Background
In this notebook, we’ll review a scenario, and add annotations to a data visualization with ggplot2. We will also save images from ggplot2 visualizations so that we can add them directly to presentations.
The Scenario
As junior data analysts for a hotel booking company, we have been
creating visualizations in R with the ggplot2
package to share insights about our data with stakeholders. After
creating a series of visualizations using ggplot(),
ggplot2 aesthetics, and filters, our stakeholder asks we to
add annotations to our visualizations to help explain our findings in a
presentation. Luckily, ggplot2 has annotation functions
built in.
Step 1: Import our data
The data in this example is originally from the article Hotel Booking Demand Datasets (https://www.sciencedirect.com/science/article/pii/S2352340918315191), written by Nuno Antonio, Ana Almeida, and Luis Nunes for Data in Brief, Volume 22, February 2019.
The data was downloaded and cleaned by Thomas Mock and Antoine Bichat for #TidyTuesday during the week of February 11th, 2020 (https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md).
We can learn more about the dataset here: https://www.kaggle.com/jessemostipak/hotel-booking-demand
Fead in the file ‘hotel_bookings.csv’ into a data frame:
hotel_bookings <- read.csv("hotel_bookings.csv")Step 2: Refresh Our Memory
By now, we are pretty familiar with this data set. But we can refresh
our memory with the head() and colnames()
functions.
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 2015-07-03colnames(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"Step 3: Install and load the ‘ggplot2’ package
Step 4: Annotating our chart
As a refresher, here is the chart we created before:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = market_segment)) +
facet_wrap(~hotel)
The first step will be adding a title; that is often the first thing
people will pay attention to when they encounter a data visualization
for the first time. To add a title, we will add labs() at
the end of our ggplot() command and then input a title
there:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = market_segment)) +
facet_wrap(~hotel) +
labs(title="Comparison of market segments by hotel type for hotel bookings")
We also want to add another detail about what time period this data covers. To do this, we need to find out when the data is from.
We realize we can use the min() function on the year
column in the data:
min(hotel_bookings$arrival_date_year)## [1] 2015And the max() function:
max(hotel_bookings$arrival_date_year)## [1] 2017But we will need to save them as variables in order to easily use them in our labeling:
mindate <- min(hotel_bookings$arrival_date_year)
maxdate <- max(hotel_bookings$arrival_date_year)Now, we will add in a subtitle using subtitle= in the
labs() function. Then, we can use the paste0()
function to use our newly-created variables in our labels. This is
really handy, because if the data gets updated and there is more recent
data added, we don’t have to change the code below because the variables
are dynamic:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = market_segment)) +
facet_wrap(~hotel) +
labs(title="Comparison of market segments by hotel type for hotel bookings",
subtitle=paste0("Data from: ", mindate, " to ", maxdate))
We decide to switch the subtitle to a
caption which will appear in the bottom right corner
instead.
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = market_segment)) +
facet_wrap(~hotel) +
labs(title="Comparison of market segments by hotel type for hotel bookings",
caption=paste0("Data from: ", mindate, " to ", maxdate))
Now we want to clean up the x and y axis labels to make sure they are
really clear. To do that, we can add to the labs() function
and use x= and y=.
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = market_segment)) +
facet_wrap(~hotel) +
labs(title="Comparison of market segments by hotel type for hotel bookings",
caption=paste0("Data from: ", mindate, " to ", maxdate),
x="Market Segment",
y="Number of Bookings")
Step 5: Saving our chart
The ggsave() function was used to save the last plot
that was generated, so if we have run something after running the code
chunk above, then run that code chunk again.
Then we save that plot as a .png file named
city_payment_chart, which makes it clear to our
stakeholders what the .png file contains.
ggsave('hotel_booking_chart.png')## Saving 7 x 5 in imageTip
Tthe default dimensions that ggsave() saved our image as
are 7x7.
We can see these dimensions listed after we run the code chunk.
If we wanted to make our chart bigger and more rectangular to fit the
slide show presentation, we could specify the height and width of our
.png in the ggsave() command. Let’s create a 16x8 .png
image:
ggsave('hotel_booking_chart.png',
width=16,
height=8)Wrap Up
Now we have finished creating and exporting a data visualization with
annotations in ggplot2, we can share what we created with
key stakeholders to give them insights into our data findings.