Password caching

If you would like your git password cached for a week for this project, type the following in the Terminal:

git config --global credential.helper 'cache --timeout 604800'

You will need to enter your GitHub username and password one more time after caching the password. After that you won’t need to enter your credentials for 604800 seconds = 7 days.

Packages

We will use the following packages in today’s lab. You may need to install the package skimr using the install.packages() command in your console.

library(tidyverse)
library(stringr)
library(knitr)
library(skimr)
library(broom)

Data

Today’s data is about Airbnb listings in Nashville, TN. The data was obtained from http://insideairbnb.com/; it was originally scraped from airbnb.com.

You can see a visualization of some of the data used in today’s lab at http://insideairbnb.com/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)
• property_type: Type of dwelling (House, Apartment, etc.)
• 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)
• number_of_reviews: Total number of reviews for the listing
• review_scores_rating: Average review score (0 - 100)
• minimum_nights: Number of nights required to book rental

Data wrangling & EDA

1. Some Airbnb rentals have cleaning fees, and we want to include the cleaning fee when we calculate the total rental cost.

• What type of variable is cleaning_fee (categorical / quantitative)?
• What type is it stored as in the data frame? Why is cleaning_fee stored in the data frame as this type?

2. Let’s recode cleaning_fee, so it is stored correctly in the data frame. To do so, write code that completes the following tasks:
   • Use the str_replace function in the stringr package to remove the dollar sign, $, i.e. replace the dollar sign with a space. The dollar sign is a special character, so use \\$ in your code to identify the dollar sign.
   • Recode cleaning_fee, so it is numeric in the data frame.

Be sure to save the numeric version of cleaning_fee to use in the remainder of the lab.

3. Visualize the distribution of cleaning_fee and display the appropriate summary statistics. Use the graph and summary statistics to describe the distribution of cleaning_fee.

4. Next, let’s examine the property_type.

   • How many different categories of property_type are in the dataset? Show code and output to support your answer.
   • Which 4 property types are most common in the data? These 4 property types make up what percent of the observations in the data? Show code and output to support your answer.

5. Since an overwhelming majority of the observations in the data are one of the top 4 property types, we would like to create a simplified version of the property_type variable that has 5 categories.

Create a new variable called prop_type_simp that has 5 categories: the four categories from the previous question and “Other” for all other property types. Be sure to save the new variable in the data frame.

6. What are the 4 most common values for the variable minimum_nights? Which value in the top 4 stands out? What is the likely intended purpose for Airbnb listings with this seemingly unusual value for minimum_nights? Show code and output to support your answer.

Airbnb is most commonly used for travel purposes, i.e. as an alternative to traditional hotels, so we only want to include Airbnb listings in our regression analysis that are intended for travel purposes. Filter airbnb so that it only includes observations with minimum_nights <= 3`.

You will use this filtered dataset for the remainder of the lab.