R/data.R

In gravitas: Explore Probability Distributions for Bivariate Temporal Granularities

#' Smart meter data for ten households
#'
#' Customer Trial data conducted as part of
#' Smart Grid Smart City (SGSC) project
#' (2010-2014) based in Newcastle,
#' New South Wales
#' and areas in Sydney.
#' It contains half hourly interval meter readings (KWh)
#' of electricity consumption of households.
#'
#' @format A tsibble with 259, 235 rows and 3 columns.
#' \describe{
#'  \item{customer_id}{household ID}
#'  \item{reading_datetime}{Date time for which data is recorded (index)}
#'  \item{general_supply_kwh}{electricity supplied to this household}
#' }
#' @source \url{https://data.gov.au/dataset/ds-dga-4e21dea3-9b87-4610-94c7-15a8a77907ef/details?q=smart-meter}
"smart_meter10"



#' Cricket data set for different seasons of Indian Premier League
#'
#' The Indian Premier League played by teams representing
#' different cities in India from 2008 to 2016.
#'
#' @format A tibble with 8560 rows and 11 variables.
#' \describe{
#'  \item{season}{years representing IPL season}
#'  \item{match_id}{match codes}
#'  \item{batting_team}{name of batting team}
#'  \item{bowling_team}{name of bowling team}
#'  \item{inning}{innings of the match}
#'  \item{over}{overs of the inning}
#'  \item{wicket}{number of wickets in each over}
#'  \item{dot_balls}{number of balls with no runs in an over}
#'  \item{runs_per_over}{Runs for each over}
#'  \item{run_rate}{run rate for each over}
#'  }
#' @source \url{https://www.kaggle.com/josephgpinto/ipl-data-analysis/data}
#' @examples
#' data(cricket)
#'
#'library(tsibble)
#'library(dplyr)
#'library(ggplot2)
#'
#' # convert data set to a tsibble ----
#' cricket_tsibble <- cricket %>%
#'   mutate(data_index = row_number()) %>%
#'   as_tsibble(index = data_index)
#' # set the hierarchy of the units in a table ----
#' hierarchy_model <- tibble::tibble(
#'   units = c("index", "over", "inning", "match"),
#'   convert_fct = c(1, 20, 2, 1)
#' )
#'# Compute granularities ----
#' cricket_tsibble %>%
#'   create_gran("over_inning",
#'                hierarchy_model)
#' # Visualise distribution of runs across granularities ----
#' cricket_tsibble %>%
#'   filter(batting_team %in% c("Mumbai Indians",
#'                              "Chennai Super Kings"))%>%
#'   prob_plot("inning", "over",
#'   hierarchy_model,
#'   response = "runs_per_over",
#'   plot_type = "lv")
"cricket"