Clone assignment repo + start new project

A repository has already been created for you and your teammates. Everyone in your team has access to the same repo.

• Go to course organization on GitHub.

• In addition to your private individual repositories, you should now see a repo named lab-03-airbnb-[github_team_name]. Go to that repository.

• Each person on the team should clone the repository and open a new project in RStudio. Do not make any changes to the .Rmd file until the instructions tell you do to so.

Workflow: Using git and GitHub as a team

Assign each person on your team a number 1 through 4. For teams of three, Member 1 can take on the role of Member 4.

The following exercises must be done in order. Only one person should type in the .Rmd file and push updates at a time. When it is not your turn to type, you should still share ideas and contribute to the team’s discussion.

Update YAML

Team Member 1: Change the author to your team name and include each team member’s name in the author field of the YAML in the following format. Team Name: Member 1, Member 2, Member 3, Member 4.

Packages

We’ll use the following packages in this lab.

library(tidyverse)
library(knitr)
library(broom)
library(patchwork)

The data

For today’s lab, we will look at data on Airbnb listings in Nashville, TN. The data was obtained from http://insideairbnb.com/; it was originally scraped from airbnb.com. Visualizations of some of the of this data are available at http://insideairbnb.com/nashville/.

It is more important than ever that public spaces such as hotels and Airbnbs are exceptionally clean, so we will explore the cleanliness ratings for these Airbnb listings. More specifically we are interested in understanding the relationship between the cleanliness rating and the price of these Airbnb rentals in Nashville.

airbnb <- read_csv("data/nashville-airbnb.csv")

We will use the following variables in this lab:

• price: Cost per night (in U.S. dollars)
• cleaning_fee: Cleaning fee (in U.S. dollars)
• room_type:
  • Entire home/apt (guests have entire place to themselves)
  • Private room (Guests have private room to sleep, all other rooms shared)
  • Shared room (Guests sleep in room shared with others)
• review_scores_cleanliness: Average cleanliness score (0 - 10)

Exercises

Exploratory data analysis

1. Since we’re focusing on the cleanliness of Airbnbs, we want to only include the rentals that charge a cleaning fee (hopefully they’re cleaner than ones with no cleaning fee!). We’d also like to consider only those rentals that in that are for an entire home / apartment. Create a new data frame called clean_airbnb that filters for

   • Airbnb rentals that have a cleaning fee Hint: Look at the way cleaning_fee is stored in the data set before filtering.
   • Airbnb rentals that are for an entire home / apartment

How many observations are in the new data frame?

You will use the filtered data frame for the next few exercises.

⊕Type ?typeof in the console to learn more about the typeof function.

1. We are interested in using the average cleanliness rating to understand variation in the price. How is price stored in the data set? Use the code below to see the data type of price using the typeof function.

typeof(clean_airbnb$price)

Why is the price stored as this data type?

⊕See the tidyr reference page for more information about the extract function.

1. Let’s update price so we can use it for regression analysis. To do so, we will first use the extract function in tidyr package to create a column of values with the numeric values. Then we can update price so it is a numeric data type. Fill in the code below to extract the values of the price and store them as a numeric variable.

clean_airbnb <- clean_airbnb %>% 
  extract(price, "price") %>% #extract price values
  mutate(price = ______) #change to numeric data type

1. Now, let’s examine the updated variable price. Make a plot to visualize the distribution of price. Based on the plot, how would you describe the distribution of price?

2. We want to analyze Airbnbs that are most representative of those available in Nashville, so create a new data frame called clean_airbnb_300 that only includes rentals that are $300 or less per night. Make a plot to visualize the distribution of price in the updated data frame.

You will use clean_airbnb_300 for the remainder of the lab.

⊕Find out more about density plots on the geom_density reference page. See the patchwork reference page for more information.

1. Make a histogram and a density plot to visualize the distribution of the predictor variable review_scores_cleanliness. Use the patchwork function to display the plots side-by-side.
   • What are 1 - 2 ways a density plot is similar to a histogram?
   • What are 1 - 2 ways a density plot differs from a histogram?

Simple linear regression

1. Fit a simple linear regression model to describe the relationship between the price and average cleanliness rating for Airbnbs in Nashville. Show the code and model output.

   • Would you stay at an Airbnb that is in the subset of rentals represented by the intercept? Briefly explain why or why not given the meaning of the intercept.

2. Let’s use ANOVA to see if there is a linear relationship between the price and cleanliness rating for Airbnbs in Nashville. Make the ANOVA table for this model. Show the code and output.

3. Calculate \(R^2\) and interpret it. Is this value surprising? Briefly explain why or why not.

1. Explain what the F-statistic means in the context of the data.

2. Based on the ANOVA test, is there a linear relationship between the average cleanliness rating and price of Airbnbs in Nashville? Briefly explain.