R/data.R

In etc5523-2020/r-package-assessment-KarinaZou: Covid-19 data with shiny app

#' The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
#' The coronavirus provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic.
#'
#'
#' @format A data frame with 7 variables.
#' \describe{
#'   \item{date}{Date in YYYY-MM-DD format which from 2020-01-22 to 2020-10-07. }
#'   \item{province}{The province or state, when applicable.}
#'   \item{country}{The country or region name}
#'   \item{lat}{Latitude.}
#'   \item{long}{Longitude.}
#'   \item{type}{the type of case (i.e.recover, confirmed, death).}
#'   \item{cases}{The daily cases.}
#'  \item{code}{The code.}
#'    }
#'
#' @source
#' The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
#' \href{https://systems.jhu.edu/research/public-health/ncov/}{website}.
#'
#' @keywords datasets, coronavirus, COVID19, worldwide
#'
"df"
#'#' The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
#' The coronavirus provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic.
#'
#'
#' @format A data frame with 7 variables.
#' \describe{
#'   \item{date}{Date in YYYY-MM-DD format which from 2020-01-22 to 2020-10-07. }
#'   \item{province}{The province or state, when applicable.}
#'   \item{country}{The country or region name}
#'   \item{lat}{Latitude.}
#'   \item{long}{Longitude.}
#'   \item{type}{the type of case (i.e.recover, confirmed, death).}
#'   \item{cases}{The daily cases.}
#'  \item{code}{The code.}
#'    }
#'
#' @source
#' The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
#' \href{https://systems.jhu.edu/research/public-health/ncov/}{website}.
#'
#' @keywords datasets, coronavirus, COVID19, worldwide
#'

"summary_df"

#' The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset from 2020-01-22 to 2020-10-07.
#' Changes in the number of confirmed cases in Brazil, the United States, and India
#'
#'
#' @format A data frame with 7 variables.
#' \describe{
#' \item{date}{Date in YYYY-MM-DD format which from 2020-01-22 to 2020-10-07. }
#' \item{province}{.}
#' \item{country}{Brazil, the United States, and India.}
#' \item{lat}{Latitude of these country.}
#' \item{long}{Longitude of these country.}
#' \item{type}{the type of case (confirmed).}
#' \item{cases}{The daily cases.}
#'    }
#'
#' @source
#' The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
#' \href{https://systems.jhu.edu/research/public-health/ncov/}{website}.
#'
#' @keywords datasets, coronavirus, COVID19, worldwide.Brazil, the United States,India,comfirmed
#'
#'

"tb"

#' The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset for US.
#'
#' Cumulative the sum Confirmed, Death, Recovered from 2020-10-07 to 2020-01-22.
#'
#' @format A data frame with 4 variables.
#' \describe{
#'   \item{date}{Date in YYYY-MM-DD format which from 2020-01-22 to 2020-10-07.}
#'   \item{confirmed}{everyday total confirmed in US.}
#'   \item{death}{everyday total death in US.}
#'   \item{recovered}{everyday total recovered in US.}
#'   }
#'
#' @source
#' Johns Hopkins University Center for Systems Science and Engineering
#' (JHU CCSE) Coronavirus \href{https://systems.jhu.edu/research/public-health/ncov/}{website}.
#'
#'