R/ae_attendances.R

In NHSRdatasets: NHS and Healthcare-Related Data for Education and Training

#' NHS England Accident & Emergency Attendances and Admissions
#'
#' Reported attendances, 4 hour breaches and admissions for all A&E departments
#' in England for the years 2016/17 through 2018/19 (Apr-Mar). The data has been
#' tidied to be easily usable within the tidyverse of packages.
#'
#' Data sourced from \href{https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity/}{NHS England Statistical Work Areas}
#' which is available under the \href{https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/}{Open Government Licence v3.0}
#'
#' @docType data
#'
#' @keywords datasets hospital a&e
#'
#' @format Tibble with six columns
#' \describe{
#' \item{period}{The month that this data relates to}
#' \item{org_code}{The \href{https://digital.nhs.uk/services/organisation-data-service}{ODS} code for this provider}
#' \item{type}{The \href{https://web.archive.org/web/20200128111444/https://www.datadictionary.nhs.uk/data_dictionary/attributes/a/acc/accident_and_emergency_department_type_de.asp}{department type}.
#'             either 1, 2 or other}
#' \item{attendances}{the number of patients who attended this department in this month}
#' \item{breaches}{the number of patients who breaches the 4 hour target in this month}
#' \item{admissions}{the number of patients admitted from A&E to the hospital in this month}
#' }
#'
#' @source \href{https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity/}{NHS England Statistical Work Areas}
#'
#' @usage data(ae_attendances)
#'
#' @examples
#' data(ae_attendances)
#' library(dplyr)
#' library(ggplot2)
#' library(scales)
#'
#' # Create a plot of the performance for England over time
#' ae_attendances %>%
#'   group_by(period) %>%
#'   summarise_at(vars(attendances, breaches), sum) %>%
#'   mutate(performance = 1 - breaches / attendances) %>%
#'   ggplot(aes(period, performance)) +
#'   geom_hline(yintercept = 0.95, linetype = "dashed") +
#'   geom_line() +
#'   geom_point() +
#'   scale_y_continuous(labels = percent) +
#'   labs(title = "4 Hour performance over time")
#'
#' # Now produce a plot showing the performance of each trust
#' ae_attendances %>%
#'   group_by(org_code) %>%
#'   # select organisations that have a type 1 department
#'   filter(any(type == "1")) %>%
#'   summarise_at(vars(attendances, breaches), sum) %>%
#'   arrange(desc(attendances)) %>%
#'   mutate(performance = 1 - breaches / attendances,
#'          overall_performance = 1 - sum(breaches) / sum(attendances),
#'          rank = rank(-performance, ties.method = "first") / n()) %>%
#'   ggplot(aes(rank, performance)) +
#'   geom_vline(xintercept = c(0.25, 0.5, 0.75), linetype = "dotted") +
#'   geom_hline(yintercept = 0.95, colour = "red") +
#'   geom_hline(aes(yintercept = overall_performance), linetype = "dotted") +
#'   geom_point() +
#'   scale_y_continuous(labels = percent) +
#'   theme_minimal() +
#'   theme(panel.grid = element_blank(),
#'         axis.text.x = element_blank()) +
#'   labs(title = "4 Hour performance by trust",
#'        subtitle = "Apr-16 through Mar-19",
#'        x = "", y = "")
#'
"ae_attendances"