R/data-BikeSharing.R

In glmbayes: Bayesian Generalized Linear Models (IID Samples)

#' Bike Sharing Dataset (Processed)
#'
#' A processed version of the UCI Bike Sharing Dataset (hourly data). The data
#' include derived variables for part of day, quarter, and Fourier terms for
#' cyclic effects of hour and month.
#'
#' @format A data frame with 17,379 observations and 25 variables (original plus derived):
#' \describe{
#'   \item{instant}{Record index.}
#'   \item{dteday}{Date.}
#'   \item{season}{Season (1: spring, 2: summer, 3: fall, 4: winter).}
#'   \item{yr}{Year (0: 2011, 1: 2012).}
#'   \item{mnth}{Month (1--12).}
#'   \item{hr}{Hour (0--23).}
#'   \item{holiday}{Whether the day is a holiday (0/1).}
#'   \item{weekday}{Day of week (0--6).}
#'   \item{workingday}{Working day (0/1).}
#'   \item{weathersit}{Weather situation (1--4).}
#'   \item{temp}{Normalized temperature.}
#'   \item{atemp}{Normalized feeling temperature.}
#'   \item{hum}{Normalized humidity.}
#'   \item{windspeed}{Normalized wind speed.}
#'   \item{casual}{Count of casual users.}
#'   \item{registered}{Count of registered users.}
#'   \item{cnt}{Total count (casual + registered).}
#'   \item{hr_num}{Hour as numeric (same as \code{hr}).}
#'   \item{month_num}{Month as integer.}
#'   \item{part_of_day}{Factor: Night (0--5h), Morning (6--11h), Afternoon (12--17h), Evening (18--23h).}
#'   \item{quarter}{Factor: Q1--Q4.}
#'   \item{hr_sin}{Sine term for 24-hour cycle.}
#'   \item{hr_cos}{Cosine term for 24-hour cycle.}
#'   \item{mon_sin}{Sine term for 12-month cycle.}
#'   \item{mon_cos}{Cosine term for 12-month cycle.}
#' }
#'
#' @source UCI Machine Learning Repository: Bike Sharing Dataset.
#' \url{https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset}
#'
#' @example inst/examples/Ex_BikeSharing.R
#'
#' @keywords datasets
#' @concept Bike sharing
#' @concept Count regression
"BikeSharing"