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#' Probabilistic forecast of the 2020 US Presidential election from 538
#'
#' A dataset containing the predicted probabilities for each major party candidate
#' to receive different numbers of electoral votes in the 2020 US Presidential
#' election according to FiveThirtyEight as of October 16, 2020.
#'
#' @name pres_pred_2020
#' @docType data
#' @format A data frame with 539 rows and 11 variables:
#' \describe{
#'   \item{cycle}{The election cycle: `2020`}
#'   \item{branch}{The kind of race this forecast pertains to: `"President"`}
#'   \item{model}{The model type: `"polls-plus"`}
#'   \item{modeldate}{Date of the model run, as a [Date] object: `"2020-10-16"`}
#'   \item{candidate_inc}{Name of the incumbent: `"Trump"`}
#'   \item{candidate_chal}{Name of the challenger: `"Biden"`}
#'   \item{evprob_inc}{Chance that the incumbent wins `total_ev` electoral votes: [numeric] between `0` and `1`}
#'   \item{evprob_chal}{Chance that the challenger wins `total_ev` electoral votes: [numeric] between `0` and `1`}
#'   \item{total_ev}{Number of electoral votes in question: [integer] between `0` and `538`}
#'   \item{timestamp}{Date and time the simulations were run, as a [POSIXct] object: `"2020-10-16 21:08:11 EST"`}
#'   \item{simulations}{Number of simulations run: `40000`}
#' }
#'
#' @source This dataset contains the electoral vote output of
#' [FiveThirtyEight's 2020 US Presidential Election Forecast](https://projects.fivethirtyeight.com/2020-election-forecast/)
#' as of Oct 16, 2020. The original dataset is licensed under the
#' [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/). The dataset
#' was obtained from the
#' [FiveThirtyEight website](https://projects.fivethirtyeight.com/2020-general-data/presidential_ev_probabilities_2020.csv),
#' and its metadata description is based on the dataset description in the
#' [FiveThirtyEight Github repository](https://github.com/fivethirtyeight/data/tree/master/election-forecasts-2020).
#' The dataset has been only been slightly modified: date formats were converted
#' from strings into R date objects; empty columns related to third party candidates
#' were dropped; and the data frame was sorted by `total_ev`.
#'
#' @keywords datasets
#' @examples
#'
#' library(ggplot2)
#'
#' data(pres_pred_2020, package = "plinko")
#'
#' ggplot(pres_pred_2020, aes(x = total_ev, y = evprob_chal)) +
#'   geom_col() +
#'   geom_vline(xintercept = 269) +
#'   labs(
#'     x = "Electoral votes for Biden",
#'     y = "Predicted probability",
#'     title = "Predictive distribution for Biden's electoral votes",
#'     subtitle = "FiveThirtyEight's model of the 2020 US election as of Oct 16, 2020"
#'   )
#'
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