Lesson 3: Aesthetics and Visualizations
Background for this activity
In this activity, you’ll review a scenario, and continue to apply your knowledge of data visualization with ggplot2. You will learn more about the aesthetic features of visualizations and how to customize them by specific criteria.
Throughout this activity, you will also have the opportunity to practice writing your own code by making changes to the code chunks yourself. If you encounter an error or get stuck, you can always check the Lesson3_Aesthetics_Solutions .rmd file in the Solutions folder under Week 4 for the complete, correct code.
The Scenario
In this example, you are a junior data analyst working for the same
hotel booking company from earlier. Last time, you created some simple
visualizations with ggplot2 to give your stakeholders quick
insights into your data. Now, you are are interested in creating
visualizations that highlight different aspects of the data to present
to your stakeholder. You are going to expand on what you have already
learned about ggplot2 and create new kinds of
visualizations like bar charts.
Step 1: Import your data
If you haven’t exited out of RStudio since importing this data last time, you can skip these steps. Rerunning these code chunks won’t affect your console if you want to run them just in case, though.
Run the code below to read in the file ‘hotel_bookings.csv’ into a data frame:
If this line causes an error, copy in the line setwd(“projects/Course 7/Week 4”) before it.
hotel_bookings <- read.csv("../data/hotel_bookings.csv")Step 2: Refresh Your Memory
By now, you are pretty familiar with this data set. But you can
refresh your memory with the head() and
colnames() functions. Run two code chunks below to get at a
sample of the data and also preview all the column names:
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 (optional)
If you haven’t already installed and loaded the ggplot2
package, you will need to do that before you can use the
ggplot() function. You only have to do this once though,
not every time you call ggplot().
You can also skip this step if you haven’t closed your RStudio
account since doing the last activity. If you aren’t sure, you can run
the code chunk and hit ‘cancel’ if the warning message pops up telling
you that have already downloaded the ggplot2 package.
Run the code chunk below to install and load ggplot2.
This may take a few minutes!
Step 4: Making a Bar Chart
Your stakeholder is interested in developing promotions based on different booking distributions, but first they need to know how many of the transactions are occurring for each different distribution type.
You can tell ggplot() what type of chart you want to
create by using the geom_ argument.
Previously, you used geom_point to make a scatter plot
comparing lead time and number of children. Now, you will use
geom_bar to make a bar chart in this code chunk:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel))
Previously, you created scatter plots with ggplot2. This code chunk
creates a bar chart with ‘distribution_channel’ on the x axis and
‘count’ on the y axis. There is data for corporate, direct, GDS, TA/TO,
and undefined distribution channels.
Practice quiz
Use the bar chart you created to answer this question: what distribution type has the most number of bookings? Note your answer for the practice quiz question in Coursera afterwards.
A: TA/TO
B: Direct
C: GDS
D: Corporate
Step 5: Diving deeper into bar charts
After exploring your bar chart, your stakeholder has more questions. Now they want to know if the number of bookings for each distribution type is different depending on whether or not there was a deposit or what market segment they represent.
Try modifying the code below to answer the question about deposits by adding ‘fill=deposit_type’ after ‘x = distribution_channel’:
This code chunk also creates a bar chart with ‘distribution_channel’ on
the x-axis and ‘count’ on the y axis. But it also includes data from
‘deposit_type’ column as color-coded sections of each bar. There is a
legend explaining what each color represents on the right side of the
visualization.
Now try adding ‘fill=market_segment’ to this code chunk instead of ‘fill=deposit_type’:
This bar chart is similar to the previous chart, except that
‘market_segment’ data is being recorded in the color-coded sections of
each bar.
Step 6: Facets galore
After reviewing the new charts, your stakeholder asks you to create separate charts for each deposit type and market segment to help them understand the differences more clearly.
You know that the facet_ function can do this very
quickly.
Add ‘deposit_type’ after the ‘~’ symbol in the code chunk below to create a different chart for each deposit type:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type)
This code chunk creates three bar charts for ‘no_deposit’, non_refund’, and ‘refundable’ deposit types. You notice that it’s hard to read the x-axis labels here, so you add one piece of code at the end that rotates the text to 45 degrees to make it easier to read.
Try it out below:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type) +
theme(axis.text.x = element_text(angle = 45))
This code chunk creates a similar bar chart to the previous chunk, but
now the labels on the x axis with the different distribution channels
are clearer.
You can use the same syntax to create a different chart for each market segment:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~market_segment) +
theme(axis.text.x = element_text(angle = 45))
The facet_grid function does something similar. The main
difference is that facet_grid will include plots even if
they are empty. Run the code chunk below to check it out:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_grid(~deposit_type) +
theme(axis.text.x = element_text(angle = 45))
Now you should have three bar charts– but notice that the ‘Refundable’
chart has much less data plotted than the other two.
Now, you could put all of this in one chart and explore the differences by deposit type and market segment.
Run the code chunk below to find out; notice how the ~ character is being used before the variables that the chart is being split by:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = distribution_channel)) +
facet_wrap(~deposit_type~market_segment) +
theme(axis.text.x = element_text(angle = 45))
These charts are probably overwhelming and too hard to read, but it can be useful if you are exploring your data through visualizations.
Activity Wrap Up
The ggplot2 package allows you to create a variety of
visualizations in R, from simple scatter plots to
complicated, multi-faceted bar charts. You can practice these skills by
modifying the code chunks in the rmd file, or use this code as a
starting point in your own project console. As you continue exploring
aesthetic arguments in ggplot2, consider how you might use
visualizations to gain insights and make observations about other kinds
of data in the future.