Once upon a time, people traveled all over the world, and some stayed in hotels and others chose to stay in other people’s houses that they booked through Airbnb. Recent developments in Edinburgh regarding the growth of Airbnb and its impact on the housing market means a better understanding of the Airbnb listings is needed. Using data provided by Airbnb, we can explore how Airbnb availability and prices vary by neighbourhood.

Getting started

⊕Optional: Instead of creating an R Project on your local computer, you could choose to use version control with GitHub as we do in the labs, but using your personal GitHub account rather than the stat408-s22 organization account. Follow the instructions for one of the ways to connect a project to a GitHub repo here.

Open RStudio. Navigate to File > New Project…, and select “New Directory”, then “New Project”. Name your directory something like “hw-02” and save it as a subdirectory of your STAT 408 folder on your computer. Click “Create Project”.
Download the R Markdown document hw-02.Rmd from the course calendar and save it to your newly created “hw-02” folder.
Open the hw-02.Rmd document in RStudio. Update the YAML, changing the author name to your name, and knit the document to PDF.
Make sure it compiles without errors. The output will be in the .pdf file with the same name in the same directory.

Packages

We’ll use the tidyverse package for much of the data wrangling and visualization and the data lives in the dsbox package.

First, try loading them into your RStudio session by running the following in your Console:

library(tidyverse)
library(dsbox)

Most likely, you received an error like:

Error in library(dsbox) : there is no package called ‘dsbox’

That error means that the package has not yet been installed. The tidyverse package lives on CRAN, so it is easily installed using the command

install.packages("tidyverse")

Note: You only need to run the install.packages() function once!

The dsbox package, however, is not yet on CRAN. It only lives on GitHub here. To install packages directly from GitHub, we can use the remotes library, which may also need to be installed:

install.packages("remotes") # Run this if needed
remotes::install_github("rstudio-education/dsbox")

If asked during the installation to update packages, type 1 (to choose to install All), then type Yes if it asks if you want to install. (It may ask you this more than once, and it may take a few minutes.) If all goes well, you should then be able to load the package using the library(dsbox) command from earlier.

Data

Once the required packages are loaded into your RStudio session, the data can be found in the dsbox package, and it’s called edibnb. Since the dataset is distributed with the package, we don’t need to load it separately; it becomes available to us when we load the package.

You can view the dataset as a spreadsheet using the View() function. Note that you should not put this function in your R Markdown document, but instead type it directly in the Console, as it pops open a new window (and the concept of popping open a window in a static document doesn’t really make sense…). When you run this in the Console, you’ll see the data viewer window pop up.

View(edibnb)

Alternatively, you can load the dataset into your environment using the data() function,

data(edibnb)

then navigate to the “Environment” tab and click on the edibnb name.

You can find out more about the dataset by inspecting its documentation, which you can access by running ?edibnb in the Console or using the Help menu in RStudio to search for edibnb. You can also find this information here.

Exercises

⊕Hint: The Markdown Quick Reference sheet has an example of inline R code that might be helpful. You can access it from the Help menu in RStudio.

How many observations (rows) does the dataset have? Instead of hard coding the number in your answer, use inline code.
Run View(edibnb) in your Console to view the data in the data viewer. What does each row in the dataset represent? That is, what is each observational unit?

Each column represents a variable. We can get a list of the variables in the data frame using the names() function.

names(edibnb)

You can find descriptions of each of the variables in the help file for the dataset, which you can access by running ?edibnb in your Console.

⊕Note: The plot will give a warning about some observations with non-finite values for price being removed. Don’t worry about the warning, it simply means that 199 listings in the data didn’t have prices available, so they can’t be plotted.

⊕Hint: Try the default facet setting first by running the code without the nrow and ncol arguments. Then, add values for the number of rows and columns of facets.

Fill in the blanks in the code below to create a faceted histogram where each facet represents a neighbourhood and displays the distribution of Airbnb prices in that neighbourhood. Think critically about whether it makes more sense to stack the facets on top of each other in a column, lay them out in a row, or wrap them around. Along with your visualization, include your reasoning for the layout you chose for your facets.

ggplot(data = ___, mapping = aes(x = ___)) +
  geom_histogram(binwidth = ___) +
  facet_wrap(~___, nrow = ___, ncol = ___)

Let’s de-construct this code:

ggplot() is the function we are using to build our plot, in layers.
In the first layer we always define the data frame as the first argument (unless we “pipe” (%>%) the data into the ggplot() function). Then, we define the mappings between the variables in the dataset and the aesthetics of the plot (e.g. x and y coordinates, colours, etc.).
In the next layer we represent the data with geometric shapes, in this case with a histogram. You should decide what makes a reasonable bin width for the histogram by trying out a few options.
In the final layer we facet the data by neighbourhood.

Create a visualization that will help you compare the distribution of review scores (review_scores_rating) across neighbourhoods. You get to decide what type of visualisation to create and there is more than one correct answer! In your answer, include a brief interpretation of how Airbnb guests rate properties in general and how the neighbourhoods compare to each other in terms of their ratings.

HW 02 - Airbnb listings in Edinburgh

Getting started

Packages

Data

Exercises

Exploration

Cite sources

Attribution