R/plot.JP.state.R

In covid19-dashboard-us/cdcar: COVID-19 Data Checking and Repairing (CDCAR)

#' COVID-19 Data Checking and Reparing (CDCAR)
#'
#' This function detects the order-dependency violation of county/state level covid-19 data (or similar type of data including the count time series and epidemic data).
#'
#' @import dplyr
#' @import segmented
#'
#' @param dat A data frame or list containing the time series of infection or death cases.
#' \cr
#' @param level Level of data for detection. Level "county" represents county level data, and level "state" means state level data. Default is set to "county".
#' \cr
#' @param state.show Within the dataset, which state the detection algorithm should focus on.
#' \cr
#' @param county.show For county level data, which county the detection algorithm should focus on.
#' \cr
#' @param plot.show Whether show the time series plot during the detection procedure. Default is set to \code{NULL}.
#' \cr
#' @param cp.sd Default is set to 2.
#' \cr
#' @param slope.r Default is set to 5.
#' \cr
#' @param manual.decision Whether using the interactive decision mode. Default is set to \code{FALSE}.
#' \cr
#' @return A list containing the following information.
#' \cr
#' \code{dat.sub} The data which contains the information on the state/county specified.
#' \cr
#' \code{dat.new} The data after the order-dependency violation detection. \code{NA} represents the data point that needs repairment.
#' \cr
#' \code{n.OD} Number of order-dependency violation.
#' \cr
#' \code{dates.CP} The dates in the time series that the change-point occurred.
#' \cr
#'
#' @details This R package is the implementation program for article entitled "Comparing and Integrating US COVID-19 Data from Multiple Sources: A County-Level Dataset with Local Features" by Guannan Wang, Zhiling Gu, Xinyi Li, Shan Yu, Myungjin Kim, Yueying Wang, Lei Gao, and Li Wang.
#'
#' @examples
#'
#' @export
#'
plot.JP.state <- function(res, dat){
  res.sub = res %>% select(-"sum.jump")
  dat = dat %>% select(names(res.sub))
  d = (dat[,-1])
  dates = as.Date(names(dat[,-c(1)]), "X%Y.%m.%d")
  dailynew =  d[,-1]- d[,-length(d)]
  names(dailynew) = dates[-1]

  for(i in which(res$sum.jump > 0 )){
    df = data.frame(dates =dates[-1], dailynew = as.numeric(dailynew[i,]))
    plot(df, main = paste0(dat[i,1]) )
    ind = which(res.sub[i,-1]==1)
    points(x = dates[ind], y = dailynew[i,ind-1],lwd = 2, col = 2)
  }
}