HW 01 - All about Edinburgh

In this assignment you’ll get to put your newly acquired data visualisation and wrangling chops to use as well as continue practicing working with Git and GitHub.

Prerequisites

This assignment assumes that you’ve worked through all materials for week 1, week 2, and week 3. Make sure you’re familiar with this content.
This assignment uses datasets from the following interactive R tutorials that were assigned during these weeks. If you haven’t gone through the tutorials then, this is a good time to give them a try. While it’s not strictly required that you finish the tutorials before the homework, they will be useful if you want some guided practice before working on open-ended questions as part of this assignment.
- Airbnb listings in Edinburgh
- Road traffic accidents

Workflow

⊕IMPORTANT: If there is no GitHub repo created for you for this assignment, it means I didn’t have your GitHub username as of when I assigned the homework. Please let me know your GitHub username asap, and I can create your repo.

Haven't done HW 00? Click here for workflow overview.

For each assignment in this course you will start with a GitHub repo that I created for you and that contains the starter documents you will build upon when working on your assignment. The first step is always to bring these files into RStudio so that you can edit them, run them, view your results, and interpret them. This action is called cloning.

Then you will work in RStudio on the data analysis, making commits along the way (snapshots of your changes) and finally push all your work back to GitHub.

The next few steps will walk you through the process of getting information of the repo to be cloned, cloning your repo in a new RStudio Cloud project, and getting started with the analysis. If you would like to step-by-step instructions with screenshots, please review HW 00.

Step 1. Get URL of repo to be cloned: On GitHub, click on the green Code button, select HTTPS (this might already be selected by default). Click on the clipboard icon 📋 to copy the repo URL.

Step 2. Go to RStudio Cloud: Go to rstudio.cloud and then navigate to the course workspace via the left sidebar. Before you proceed, confirm that you are in the course workspace by checking out what’s on your top bar in RStudio Cloud; it should say Introduction to Data Science.

Step 3. Clone the repo: In RStudio, click on the down arrow next to New Project and then choose New Project from Git Repository. In the pop-up window, paste the URL you copied from GitHub, make sure the box for Add packages from the base project is checked (it should be, by default) and then click OK.

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

Step 1. Update the YAML: Open the R Markdown (Rmd) file in your project, change the author name to your name, and knit the document.

Step 2. Commit: Go to the Git pane in RStudio. You should see that your Rmd (R Markdown) file and its output, your md file (Markdown), file are listed there as recently changed files. Next, click on Diff. This will pop open a new window that shows you the difference between the last committed state of the document and its current state that includes your changes. If you’re happy with these changes, click on the checkboxes of all files in the list, and type “Update author name” in the Commit message box and hit Commit.

⊕Don’t fret if you miss a commit suggested or end up making more commits than what’s suggested here. The important thing is that you make incremental commits, the precise number doesn’t matter. And your commit messages should be informative, but the precise wording doesn’t matter either.

You don’t have to commit after every change, this would get quite cumbersome. You should consider committing states that are meaningful to you for inspection, comparison, or restoration. In the first few assignments we will tell you exactly when to commit and in some cases, what commit message to use. As the semester progresses we will let you make these decisions.

Step 3. Push: Now that you have made an update and committed this change, it’s time to push these changes to the web! Or more specifically, to your repo on GitHub. Why? So that others can see your changes. And by others, we mean the course teaching team (your repos in this course are private to you and us, only). In order to push your changes to GitHub, click on Push. This will prompt a dialogue box where you first need to enter your user name, and then your password.

Tired of typing your password?

Chances are your browser has already saved your password, but if not, you can ask Git to save (cache) your password for a period of time, where you indicate the period of time in seconds. For example, if you want it to cache your password for 1 hour, that would be 3,600 seconds. To do so, run the following in the console, usethis::use_git_config(credential.helper = "cache --timeout=3600"). If you want to cache it for a longer amount of time, you can adjust the number in the code.

Packages

R is an open-source language, and developers contribute functionality to R via packages. In this assignment we will use the following packages:

tidyverse: a collection of packages for doing data analysis in a “tidy” way
dsbox: a package that contains the datasets that we will use in this course

We use the library() function to load packages. In your R Markdown document you should see an R chunk labelled load-packages which has the necessary code for loading both packages. You should also load these packages in your Console, which you can do by sending the code to your Console by clicking on the Run Current Chunk icon (green arrow pointing right icon).

Note that these packages are also get loaded in your R Markdown environment when you Knit your R Markdown document.

Airbnb listings in Edinburgh

Once upon a time, people travelled 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.

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 following data viewer window pop up.

View(edibnb)

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.

How many observations (rows) does the dataset have? Instead of hard coding the number in your answer, use inline code.

✏️ Write your answer in your R Markdown document under the appropriate exercise, knit the document, commit your changes with a commit message that says “Completed Exercise 1”, and push. Make sure you commit and push all files so your Git pane is clear after the push.

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 visualisation, include your reasoning for the layout you chose for your facets.

✏️ Write your answer in your R Markdown document under the appropriate exercise, knit the document, commit your changes with a commit message that says “Completed Exercise 2”, and push. Make sure you commit and push all files so your Git pane is clear after the push.

Use a single pipeline to identity the neighbourhoods with the top five median listing prices. Then, in another pipeline filter the data for these five neighbourhoods and make ridge plots of the distributions of listing prices in these five neighbourhoods. In a third pipeline calculate the minimum, mean, median, standard deviation, IQR, and maximum listing price in each of these neighbourhoods. Use the visualisation and the summary statistics to describe the distribution of listing prices in the neighbourhoods. (Your answer will include three pipelines, one of which ends in a visualisation, and a narrative.)

✏️ Write your answer in your R Markdown document under the appropriate exercise, knit the document, commit your changes with a short and informative commit message, and push. Make sure you commit and push all files so your Git pane is clear after the push.

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.

Road traffic accidents in Edinburgh

Next we’ll look at traffic accidents in Edinburgh. The data are made available online by the UK Government. It covers all recorded accidents in Edinburgh in 2018 and some of the variables were modified for the purposes of this assignment.

The data can be found in the dsbox package, and it’s called accidents. 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 find out more about the dataset by inspecting its documentation, which you can access by running ?accidents in the Console or using the Help menu in RStudio to search for accidents. You can also find this information here.

Recreate the following plot, and describe in context of the data. In your answer, don’t forget to label your R chunk as well (where it says label-me-1). Your label should be short, informative, shouldn’t include spaces, and shouldn’t shouldn’t repeat a previous label.

Create another data visualisation based on these data and interpret it. You can choose any variables and any type of visualisation you like, but it must have at least three variables, e.g. a scatterplot of x vs. y isn’t enough, but if points are coloured by z, that’s fine. In your answer, don’t forget to label your R chunk as well (where it says label-me-2).

✏️ Now is a good time to commit and push your changes to GitHub with an appropriate commit message. Commit and push all changed files so that your Git pane is cleared up afterwards. Make sure that your last push to the repo comes before the deadline, 15 October, 16:00 UK time. You can confirm that what you committed and pushed are indeed in your repo that we will see by visiting your repo on GitHub. Make sure your Rmd and md files are there and that your md file contains all of your plots as well.