#' Chicago AirBnB Data
#' 
#' The AirBnB data was collated by Trinh and Ameri as part of a course project
#' at St Olaf College, and distributed with "Broadening Your Statistical Horizons" by Legler and Roback.
#' This data set, a subset of the airbnb data in the bayesrules package, includes the prices and features for 869 AirBnB listings in Chicago, collected in 2016.
#' 
#' @format A data frame with 869 rows and 12 variables. Each row represents a single AirBnB listing.
#' \describe{
#'   \item{price}{the nightly price of the listing (in USD)}
#'   \item{rating}{the listing's average rating, on a scale from 1 to 5}
#'   \item{reviews}{number of user reviews the listing has}
#'   \item{room_type}{the type of listing (eg: Shared room)}
#'   \item{accommodates}{number of guests the listing accommodates}
#'   \item{bedrooms}{the number of bedrooms the listing has}
#'   \item{minimum_stay}{the minimum number of nights to stay in the listing}
#'   \item{neighborhood}{the neighborhood in which the listing is located}
#'   \item{district}{the broader district in which the listing is located}
#'   \item{walk_score}{the neighborhood's rating for walkability (0 - 100)}
#'   \item{transit_score}{the neighborhood's rating for access to public transit (0 - 100)}
#'   \item{bike_score}{the neighborhood's rating for bikeability (0 - 100)}
#' }
#' @source Ly Trinh and Pony Ameri (2018). Airbnb Price Determinants: A Multilevel Modeling Approach. Project for Statistics 316-Advanced Statistical Modeling, St. Olaf College.
#' Julie Legler and Paul Roback (2019). Broadening Your Statistical Horizons: Generalized Linear Models and Multilevel Models. \url{https://bookdown.org/roback/bookdown-bysh/}.
#' \url{https://github.com/proback/BeyondMLR/blob/master/data/airbnb.csv/}

"airbnb_small"
