R6 class storing health statistics for a single hospital.

Details

This R6 class is designed to store bed occupancy statistics for a single hospital location. This class can be used to plot summary statistics and create shiny UI/server objects.

Super class

HealthDataScotland::health_unit -> hospital

Methods

Inherited methods

Method `plot_types()`

Get character vector of plot types for hospital grp

Usage

hospital$plot_types()

Method `plot()`

Plot hospital unit.

Usage

hospital$plot(type, ...)

Arguments

type: (character(1))
Character specifying plot type. See plot_types for options.
...: Passed to plot functions.

Examples

x <- example_hospital_unit()
x[["plot"]](type = "specialty_line", data_type = "annual")

Method `plot_data()`

Generate plot data for hospital unit.

Usage

hospital$plot_data(type, ...)

Arguments

type: (character(1))
Character specifying plot type. See plot_types for options.
...: Passed to plot data functions.

Examples

x <- example_hospital_unit()
x[["plot_data"]](type = "specialty_line", data_type = "annual")

Method `plot_info()`

Get plot info for hospital unit.

Usage

hospital$plot_info(type, ...)

Arguments

type: (character(1))
Character specifying plot type. See plot_types for options.
...: Passed to plot info functions.

Examples

x <- example_hospital_unit()
x[["plot_info"]](type = "specialty_line")

Method `summary_types()`

Get character vector of summary types for hospital unit.

Usage

hospital$summary_types()

Method `summary()`

Summarise hospital data.

Usage

hospital$summary(type, ...)

Arguments

type: (character(1))
Character specifying summary type. See summary_types for options.
...: Passed to method.

Examples

x <- example_hospital_unit()
x[["summary"]](type = "specialty_summary")

Method `summary_info()`

Get summary info for hospital unit.

Usage

hospital$summary_info(type, ...)

Arguments

type: (character(1))
Character specifying summary type. See summary_types for options.
...: Passed to summary info functions.

Examples

x <- example_hospital_unit()
x[["summary_info"]](type = "specialty_summary")

Method `ui()`

Create UI for hospital object.

Usage

hospital$ui(ns)

Arguments

ns: Namespace of shiny application page.

Method `server()`

Create server for hospital object.

Usage

hospital$server()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

hospital$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(dplyr)
meta <- HealthDataScotland::example_hospital_metadata |>
  rename("ID" = "HospitalCode", "HBName" = "HealthBoard") |>
  filter(.data[["ID"]] == "A101H")
data <- HealthDataScotland::example_hospital_data |>
  rename("ID" = "Location") |>
  filter(.data[["ID"]] == "A101H")
x <- hospital[["new"]](meta, data)
x[["ID"]]()
#> [1] "A101H"
x[["title"]]()
#> [1] "Arran War Memorial Hospital"
x[["address"]]()
#> [1] "Lamlash, Isle of Arran, KA278LF"
x[["health_board"]]()
#> [1] "S08000015"
x[["metadata"]]()
#> # A tibble: 1 × 16
#>   datasetID          ID    HospitalName AddressLine1 AddressLine2 AddressLine2QF
#>   <chr>              <chr> <chr>        <chr>        <chr>        <chr>         
#> 1 c698f450-eeed-41a… A101H Arran War M… Lamlash      Isle of Arr… NA            
#> # ℹ 10 more variables: AddressLine3 <chr>, AddressLine3QF <chr>,
#> #   AddressLine4 <chr>, AddressLine4QF <chr>, Postcode <chr>, HBName <chr>,
#> #   HSCP <chr>, CouncilArea <chr>, IntermediateZone <chr>, DataZone <chr>
x[["data"]]()
#> # A tibble: 21 × 23
#>    datasetID          FinancialYear FinancialYearQF HB    HBQF  ID    LocationQF
#>    <chr>              <chr>         <chr>           <chr> <chr> <chr> <chr>     
#>  1 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  2 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  3 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  4 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  5 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  6 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  7 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  8 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#>  9 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#> 10 d719af13-5fb3-430… 2022/23       NA              S080… NA    A101H NA        
#> # ℹ 11 more rows
#> # ℹ 16 more variables: Specialty <chr>, SpecialtyQF <chr>, SpecialtyName <chr>,
#> #   SpecialtyNameQF <chr>, SpecialtyGrouping <chr>, SpecialtyGroupingQF <chr>,
#> #   AverageAvailableStaffedBeds <dbl>, AverageAvailableStaffedBedsQF <chr>,
#> #   PercentageOccupancy <dbl>, PercentageOccupancyQF <chr>,
#> #   AllStaffedBeds <dbl>, AllStaffedBedsQF <chr>, AverageOccupiedBeds <dbl>,
#> #   AverageOccupiedBedsQF <chr>, TotalOccupiedBeds <dbl>, …
x[["plot"]](type = "specialty_line")

  
  
    
      
      
  
x[["plot_data"]](type = "specialty_line")
#> # A tibble: 4 × 7
#>   ID    FinancialYear HospitalName SpecialtyName PercentageOccupancy name  value
#>   <chr> <chr>         <chr>        <chr>                       <dbl> <chr> <dbl>
#> 1 A101H 2022/23       Arran War M… All Specialt…                77.4 Annu…  3263
#> 2 A101H 2022/23       Arran War M… All Specialt…                77.4 Annu…  2526
#> 3 A101H 2023/24       Arran War M… All Specialt…                68.5 Annu…  3631
#> 4 A101H 2023/24       Arran War M… All Specialt…                68.5 Annu…  2488
x[["plot_info"]](type = "specialty_line")
#> [1] "This line chart shows the number of\n          available staffed beds and the number occupied (y-axis)\n          across time (x-axis). Settings can be used to show data for\n          different specialties (default is all specialities) and\n          for different statistics ('annual' for the annual number of\n          beds and 'daily' for the average daily number of beds)."
x[["summary"]](type = "specialty_summary")
#> # A tibble: 21 × 8
#>    `Financial Year` Specialty      Annual percentage oc…¹ Annual number of ava…²
#>    <chr>            <chr>                           <dbl>                  <dbl>
#>  1 2022/23          General Medic…                   NA                        0
#>  2 2022/23          Cardiology                       NA                        0
#>  3 2022/23          Geriatric Med…                   NA                        0
#>  4 2022/23          General Surge…                   NA                        0
#>  5 2022/23          Trauma and Or…                   NA                        0
#>  6 2022/23          GP Other than…                   77.4                   3263
#>  7 2022/23          All Acute Spe…                   NA                        0
#>  8 2022/23          All Specialti…                   77.4                   3263
#>  9 2022/23          Community Gro…                   77.4                   3263
#> 10 2022/23          Medical Group…                   NA                        0
#> # ℹ 11 more rows
#> # ℹ abbreviated names: ¹`Annual percentage occupancy`,
#> #   ²`Annual number of available staffed beds`
#> # ℹ 4 more variables: `Annual number of occupied beds` <dbl>,
#> #   `Daily percentage occupancy` <dbl>,
#> #   `Daily average number of available staffed beds` <dbl>,
#> #   `Daily average number of occupied beds` <dbl>
x[["summary_info"]](type = "specialty_summary")
#> [1] "This summary table presents the annual and daily average number of available staffed beds,\n        the number of annual and daily average beds occupied, and the percentage occupancy for\n        the selected hospital"
if (FALSE) { # \dontrun{
x[["ui"]]()
x[["server"]]()
} # }

## ------------------------------------------------
## Method `hospital$plot`
## ------------------------------------------------

x <- example_hospital_unit()
x[["plot"]](type = "specialty_line", data_type = "annual")

  
  
    
      
      
  

## ------------------------------------------------
## Method `hospital$plot_data`
## ------------------------------------------------

x <- example_hospital_unit()
x[["plot_data"]](type = "specialty_line", data_type = "annual")
#> # A tibble: 4 × 7
#>   ID    FinancialYear HospitalName SpecialtyName PercentageOccupancy name  value
#>   <chr> <chr>         <chr>        <chr>                       <dbl> <chr> <dbl>
#> 1 A101H 2022/23       Arran War M… All Specialt…                77.4 Annu…  3263
#> 2 A101H 2022/23       Arran War M… All Specialt…                77.4 Annu…  2526
#> 3 A101H 2023/24       Arran War M… All Specialt…                68.5 Annu…  3631
#> 4 A101H 2023/24       Arran War M… All Specialt…                68.5 Annu…  2488

## ------------------------------------------------
## Method `hospital$plot_info`
## ------------------------------------------------

x <- example_hospital_unit()
x[["plot_info"]](type = "specialty_line")
#> [1] "This line chart shows the number of\n          available staffed beds and the number occupied (y-axis)\n          across time (x-axis). Settings can be used to show data for\n          different specialties (default is all specialities) and\n          for different statistics ('annual' for the annual number of\n          beds and 'daily' for the average daily number of beds)."

## ------------------------------------------------
## Method `hospital$summary`
## ------------------------------------------------

x <- example_hospital_unit()
x[["summary"]](type = "specialty_summary")
#> # A tibble: 21 × 8
#>    `Financial Year` Specialty      Annual percentage oc…¹ Annual number of ava…²
#>    <chr>            <chr>                           <dbl>                  <dbl>
#>  1 2022/23          General Medic…                   NA                        0
#>  2 2022/23          Cardiology                       NA                        0
#>  3 2022/23          Geriatric Med…                   NA                        0
#>  4 2022/23          General Surge…                   NA                        0
#>  5 2022/23          Trauma and Or…                   NA                        0
#>  6 2022/23          GP Other than…                   77.4                   3263
#>  7 2022/23          All Acute Spe…                   NA                        0
#>  8 2022/23          All Specialti…                   77.4                   3263
#>  9 2022/23          Community Gro…                   77.4                   3263
#> 10 2022/23          Medical Group…                   NA                        0
#> # ℹ 11 more rows
#> # ℹ abbreviated names: ¹`Annual percentage occupancy`,
#> #   ²`Annual number of available staffed beds`
#> # ℹ 4 more variables: `Annual number of occupied beds` <dbl>,
#> #   `Daily percentage occupancy` <dbl>,
#> #   `Daily average number of available staffed beds` <dbl>,
#> #   `Daily average number of occupied beds` <dbl>

## ------------------------------------------------
## Method `hospital$summary_info`
## ------------------------------------------------

x <- example_hospital_unit()
x[["summary_info"]](type = "specialty_summary")
#> [1] "This summary table presents the annual and daily average number of available staffed beds,\n        the number of annual and daily average beds occupied, and the percentage occupancy for\n        the selected hospital"

R6 class storing health statistics for a single hospital.

Details

Super class

Methods

Public methods

Method plot_types()

Usage

Method plot()

Usage

Arguments

Examples

Method plot_data()

Usage

Arguments

Examples

Method plot_info()

Usage

Arguments

Examples

Method summary_types()

Usage

Method summary()

Usage

Arguments

Examples

Method summary_info()

Usage

Arguments

Examples

Method ui()

Usage

Arguments

Method server()

Usage

Method clone()

Usage

Arguments

Examples

Method `plot_types()`

Method `plot()`

Method `plot_data()`

Method `plot_info()`

Method `summary_types()`

Method `summary()`

Method `summary_info()`

Method `ui()`

Method `server()`

Method `clone()`