dsbox: Data Science Course in a Box

#' Bike Rentals In D.C.
#'
#' Bike sharing systems are new generation of traditional bike rentals where whole
#' process from membership, rental and return back has become automatic. Through these
#' systems, user is able to easily rent a bike from a particular position and return
#' back at another position. As of May 2018, there are about over 1600 bike-sharing programs
#' around the world, providing more than 18 million bicycles for public use. Today, there
#' exists great interest in these systems due to their important role in traffic,
#' environmental and health issues.
#'
#' Apart from interesting real world applications of bike sharing systems, the
#' of data being generated by these systems make them attractive for the research.
#' Opposed to other transport services such as bus or subway, the duration of travel,
#' departure and arrival position is explicitly recorded in these systems. This
#' feature turns bike sharing system into a virtual sensor network that can be used
#' for sensing mobility in the city. Hence, it is expected that most of important
#' events in the city could be detected via monitoring these data.
#'
#' The dataset comes from
#' [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset). Sources of the
#' data itself are
#' [Capital Bikeshare](https://www.capitalbikeshare.com/system-data),
#' [i-weather.com](https://i-weather.com/) for weather data, and
#' [dc.gov](https://dchr.dc.gov/page/holiday-schedules) for the holiday schedule.
#'
#' The data have been modified to better serve the goals of introductory data science education.
#'
#' @source http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset
#' @format A tibble with 731 rows and 16 variables:
#' \describe{
#'   \item{instant}{record index}
#'   \item{dteday}{date}
#'   \item{season}{season (1:winter, 2:spring, 3:summer, 4:fall)}
#'   \item{yr}{year (0: 2011, 1:2012)}
#'   \item{mnth}{month (1 to 12)}
#'   \item{holiday}{whether day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)}
#'   \item{weekday}{day of the week}
#'   \item{workingday}{if day is neither weekend nor holiday is 1, otherwise is 0.}
#'   \item{weathersit}{1: Clear, Few clouds, Partly cloudy, Partly cloudy, 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist, 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds, 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog}
#'   \item{temp}{Normalized temperature in Celsius. The values are divided by 41 (max)}
#'   \item{atemp}{Normalized feeling temperature in Celsius. The values are divided by 50 (max)}
#'   \item{hum}{Normalized humidity. The values are divided by 100 (max)}
#'   \item{windspeed}{Normalized wind speed. The values are divided by 67 (max)}
#'   \item{casual}{Count of casual users}
#'   \item{registered}{Count of registered users}
#'   \item{cnt}{Count of total rental bikes including both casual and registered}
#' }
#' @examples
#'
#' library(ggplot2)
#'
#' ggplot(dcbikeshare, aes(x = dteday, y = cnt)) +
#'   geom_point() +
#'   labs(
#'     x = "Date",
#'     y = "Bike Rentals"
#'   )
#'
"dcbikeshare"