Filters and Plots
Keman Xiang
Background
In this notebook, we’ll review a scenario, and create a data visualization with ggplot2. We will make use of the filters and facets features of ggplot2 to create custom visualizations based on different criteria.
The Scenario
As junior data analysts for a hotel booking company, we have been
asked to clean hotel booking data, create visualizations with
ggplot2 to gain insight into the data, and present
different facets of the data through visualization. Now, we are going to
build on the work we performed previously to apply filters to our data
visualizations in ggplot2.
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
Read 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 in our previous
notebooks. 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: Making many different charts
As a refresher, here’s the scatter plot we created earlier.
ggplot(data = hotel_bookings) +
geom_point(mapping = aes(x = lead_time, y = children))## Warning: Removed 4 rows containing missing values (`geom_point()`).
We decide to create a bar chart showing each hotel type and market segment. We use different colors to represent each market segment:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel, fill = market_segment))
We decide to use the facet_wrap() function to create a separate plot for each market segment:
ggplot(data = hotel_bookings) +
geom_bar(mapping = aes(x = hotel)) +
facet_wrap(~market_segment)
Step 5: Filtering
For the next step, we will need to have the tidyverse
package installed and loaded.
#install.packages('tidyverse')
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsUse the filter() function to create a data set that only
includes the data we want:
onlineta_city_hotels <- filter(hotel_bookings,
(hotel=="City Hotel" &
hotel_bookings$market_segment=="Online TA"))We can use theView() function to check out our new data
frame:
#View(onlineta_city_hotels)We can use the pipe operator (%>%) to do this in steps!
We name this data frame onlineta_city_hotels_v2:
onlineta_city_hotels_v2 <- hotel_bookings %>%
filter(hotel=="City Hotel") %>%
filter(market_segment=="Online TA")This code chunk generates the same data frame by using the
View() function:
#View(onlineta_city_hotels_v2)Step 6: Use our new dataframe
Using the code for our previous scatterplot, replace
data with either onlineta_city_hotels to plot
the data our stakeholder requested:
ggplot(data = onlineta_city_hotels) +
geom_point(mapping = aes(x = lead_time, y = children))## Warning: Removed 1 rows containing missing values (`geom_point()`).
Based on our previous filter, this scatterplot shows data for online bookings for city hotels. The plot reveals that bookings with children tend to have a shorter lead time, and bookings with 3 children have a significantly shorter lead time (<200 days). So, promotions targeting families can be made closer to the valid booking dates.
Wrap Up
Filters allow us to create different views of our data and allow us to investigate more specific relationships within our data.