R/data-mariokart.R

In OpenIntroStat/openintro-r-package: Data Sets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' Wii Mario Kart auctions from Ebay
#'
#' Auction data from Ebay for the game Mario Kart for the Nintendo Wii. This
#' data was collected in early October 2009.
#'
#' There are several interesting features in the data. First off, note that
#' there are two outliers in the data. These serve as a nice example of what
#' one should do when encountering an outlier: examine the data point and
#' remove it only if there is a good reason. In these two cases, we can see
#' from the auction titles that they included other items in their auctions
#' besides the game, which justifies removing them from the data set.
#'
#' This data set includes all auctions for a full week in October 2009.
#' Auctions were included in the data set if they satisfied a number of
#' conditions. (1) They were included in a search for "wii mario kart" on
#' ebay.com, (2) items were in the Video Games > Games > Nintendo Wii section
#' of Ebay, (3) the listing was an auction and not exclusively a "Buy it Now"
#' listing (sellers sometimes offer an optional higher price for a buyer to end
#' bidding and win the auction immediately, which is an \emph{optional} Buy it
#' Now auction), (4) the item listed was the actual game, (5) the item was
#' being sold from the US, (6) the item had at least one bidder, (7) there were
#' no other items included in the auction with the exception of racing wheels,
#' either generic or brand-name being acceptable, and (8) the auction did not
#' end with a Buy It Now option.
#'
#' @name mariokart
#' @docType data
#' @format A data frame with 143 observations on the following 12 variables.
#' All prices are in US dollars.
#' \describe{
#'   \item{id}{Auction ID assigned by Ebay.}
#'   \item{duration}{Auction length, in days.}
#'   \item{n_bids}{Number of bids.}
#'   \item{cond}{Game condition, either \code{new} or \code{used}.}
#'   \item{start_pr}{Start price of the auction.}
#'   \item{ship_pr}{Shipping price.}
#'   \item{total_pr}{Total price, which equals the auction price plus the
#'   shipping price.}
#'   \item{ship_sp}{Shipping speed or method.}
#'   \item{seller_rate}{The seller's rating on Ebay. This is the number
#'   of positive ratings minus the number of negative ratings for the seller.}
#'   \item{stock_photo}{Whether the auction feature photo was a stock
#'   photo or not. If the picture was used in many auctions, then it was called a
#'   stock photo.}
#'   \item{wheels}{Number of Wii wheels included in the auction. These are steering
#'   wheel attachments to make it seem as though you are actually driving in the
#'   game. When used with the controller, turning the wheel actually causes the
#'   character on screen to turn.}
#'   \item{title}{The title of the auctions.}
#' }
#' @source Ebay.
#' @keywords datasets
#' @examples
#'
#' library(ggplot2)
#' library(broom)
#' library(dplyr)
#'
#' # Identify outliers
#' ggplot(mariokart, aes(x = total_pr, y = cond)) +
#'   geom_boxplot()
#'
#' # Replot without the outliers
#' mariokart %>%
#'   filter(total_pr < 80) %>%
#'   ggplot(aes(x = total_pr, y = cond)) +
#'   geom_boxplot()
#'
#' # Fit a multiple regression models
#' mariokart_no <- mariokart %>% filter(total_pr < 80)
#' m1 <- lm(total_pr ~ cond + stock_photo + duration + wheels, data = mariokart_no)
#' tidy(m1)
#' m2 <- lm(total_pr ~ cond + stock_photo + wheels, data = mariokart_no)
#' tidy(m2)
#' m3 <- lm(total_pr ~ cond + wheels, data = mariokart_no)
#' tidy(m3)
#'
#' # Fit diagnostics
#' aug_m3 <- augment(m3)
#'
#' ggplot(aug_m3, aes(x = .fitted, y = .resid)) +
#'   geom_point() +
#'   geom_hline(yintercept = 0, linetype = "dashed") +
#'   labs(x = "Fitted values", y = "Residuals")
#'
#' ggplot(aug_m3, aes(x = .fitted, y = abs(.resid))) +
#'   geom_point() +
#'   geom_hline(yintercept = 0, linetype = "dashed") +
#'   labs(x = "Fitted values", y = "Absolute value of residuals")
#'
#' ggplot(aug_m3, aes(x = 1:nrow(aug_m3), y = .resid)) +
#'   geom_point() +
#'   geom_hline(yintercept = 0, linetype = "dashed") +
#'   labs(x = "Order of data collection", y = "Residuals")
#'
#' ggplot(aug_m3, aes(x = cond, y = .resid)) +
#'   geom_boxplot() +
#'   labs(x = "Condition", y = "Residuals")
#'
#' ggplot(aug_m3, aes(x = wheels, y = .resid)) +
#'   geom_point() +
#'   labs(
#'     x = "Number of wheels", y = "Residuals",
#'     title = "Notice curvature"
#'   )
"mariokart"