Package 'TreatmentPatterns'

Title: Analyzes Real-World Treatment Patterns of a Study Population of Interest
Description: Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
Authors: Aniek Markus [aut] , Maarten van Kessel [cre]
Maintainer: Maarten van Kessel <[email protected]>
License: Apache License (>= 2)
Version: 3.0.0
Built: 2025-02-06 15:23:38 UTC
Source: https://github.com/darwin-eu/treatmentpatterns

Help Index

CharacterizationPlots

Description

Class to handle the characterization plots.

Super class

TreatmentPatterns::ShinyModule -> CharacterizationPlots

Methods

Public methods

Inherited methods

Method uiMenu()

Method to include a menuItem to link to the body.

Usage
CharacterizationPlots$uiMenu(
  label = "Characteristics",
  tag = "characteristics"
)
Arguments
label

(character(1))
Label to show for the menuItem.

tag

(character(1))
Tag to use internally in input.

Returns

(menuItem)

Method uiBody()

Method to include a tabItem to include the body.

Usage
CharacterizationPlots$uiBody()
Returns

(tabItem)

Method server()

Method to handle the back-end.

Usage
CharacterizationPlots$server(input, output, session, inputHandler)
Arguments
input

(input)
Input from the server function.

output

(output)
Output from the server function.

session

(session)
Session from the server function.

inputHandler

(inputHandler)
InputHandler class.

Returns

(NULL)

Method clone()

The objects of this class are cloneable with this method.

Usage
CharacterizationPlots$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

computePathways

Description

Compute treatment patterns according to the specified parameters within specified cohorts.

Usage

computePathways(
  cohorts,
  cohortTableName,
  cdm = NULL,
  connectionDetails = NULL,
  cdmSchema = NULL,
  resultSchema = NULL,
  analysisId = 1,
  description = "",
  tempEmulationSchema = NULL,
  includeTreatments = "startDate",
  indexDateOffset = 0,
  minEraDuration = 0,
  splitEventCohorts = NULL,
  splitTime = NULL,
  eraCollapseSize = 30,
  combinationWindow = 30,
  minPostCombinationDuration = 30,
  filterTreatments = "First",
  maxPathLength = 5
)

Arguments

cohorts

(data.frame())
Data frame containing the following columns and data types:

cohortId numeric(1)

Cohort ID's of the cohorts to be used in the cohort table.

cohortName character(1)

Cohort names of the cohorts to be used in the cohort table.

type character(1) ["target", "event', "exit"]

Cohort type, describing if the cohort is a target, event, or exit cohort
cohortTableName

(character(1))
Cohort table name.
cdm

(CDMConnector::cdm_from_con(): NULL)
Optional; Ignores connectionDetails, cdmSchema, and resultSchema.
connectionDetails

(DatabaseConnector::createConnectionDetails(): NULL)
Optional; In congruence with cdmSchema and resultSchema. Ignores cdm.
cdmSchema

(character(1): NULL)
Optional; In congruence with connectionDetails and resultSchema. Ignores cdm.
resultSchema

(character(1): NULL)
Optional; In congruence with connectionDetails and cdmSchema. Ignores cdm.
analysisId

(character(1)) Identifier for the TreatmentPatterns analysis.
description

(character(1)) Description of the analysis.
tempEmulationSchema

Schema used to emulate temp tables
includeTreatments

(character(1): "startDate")

"startDate"

Include treatments after the target cohort start date and onwards.

"endDate"

Include treatments before target cohort end date and before.
indexDateOffset

(integer(1): 0)
Offset the index date of the Target cohort.
minEraDuration

(integer(1): 0)
Minimum time an event era should last to be included in analysis
splitEventCohorts

(character(n): "")
Specify event cohort to split in acute (< X days) and therapy (>= X days)
splitTime

(integer(1): 30)
Specify number of days (X) at which each of the split event cohorts should be split in acute and therapy
eraCollapseSize

(integer(1): 30)
Window of time between which two eras of the same event cohort are collapsed into one era
combinationWindow

(integer(1): 30)
Window of time two event cohorts need to overlap to be considered a combination treatment
minPostCombinationDuration

(integer(1): 30)
Minimum time an event era before or after a generated combination treatment should last to be included in analysis
filterTreatments

(character(1): "First" ["first", "Changes", "all"])
Select first occurrence of (‘First’); changes between (‘Changes’); or all event cohorts (‘All’).
maxPathLength

(integer(1): 5)
Maximum number of steps included in treatment pathway

Value

(Andromeda::andromeda()) andromeda object containing non-sharable patient level data outcomes.

Examples

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

createSankeyDiagram

Description

Create sankey diagram.

Usage

createSankeyDiagram(
  treatmentPathways,
  groupCombinations = FALSE,
  colors = NULL,
  ...
)

Arguments

treatmentPathways

(data.frame())
The contents of the treatmentPathways.csv-file as a data.frame().
groupCombinations

(logical(1): FALSE)

TRUE

Group all combination treatments in category "Combination".

FALSE

Do not group combination treatments.
colors

(character(n)) Vector of hex color codes.
...

Paramaters for sankeyNetwork.

Value

(htmlwidget)

Examples

# Dummy data, typically read from treatmentPathways.csv
treatmentPathways <- data.frame(
  pathway = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSankeyDiagram(treatmentPathways)

createSunburstPlot

Description

New sunburstPlot function

Usage

createSunburstPlot(treatmentPathways, groupCombinations = FALSE, ...)

Arguments

treatmentPathways

(data.frame())
The contents of the treatmentPathways.csv-file as a data.frame().
groupCombinations

(logical(1): FALSE)

TRUE

Group all combination treatments in category "Combination".

FALSE

Do not group combination treatments.
...

Paramaters for sunburst.

Value

(htmlwidget)

Examples

# Dummy data, typically read from treatmentPathways.csv
treatmentPatwhays <- data.frame(
  pathway = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSunburstPlot(treatmentPatwhays)

executeTreatmentPatterns

Description

Compute treatment patterns according to the specified parameters within specified cohorts. For more customization, or investigation of patient level outcomes, you can run computePathways and export separately.

Usage

executeTreatmentPatterns(
  cohorts,
  cohortTableName,
  cdm = NULL,
  connectionDetails = NULL,
  cdmSchema = NULL,
  resultSchema = NULL,
  tempEmulationSchema = NULL,
  minEraDuration = 0,
  eraCollapseSize = 30,
  combinationWindow = 30,
  minCellCount = 5
)

Arguments

cohorts

(data.frame())
Data frame containing the following columns and data types:

cohortId numeric(1)

Cohort ID's of the cohorts to be used in the cohort table.

cohortName character(1)

Cohort names of the cohorts to be used in the cohort table.

type character(1) ["target", "event', "exit"]

Cohort type, describing if the cohort is a target, event, or exit cohort
cohortTableName

(character(1))
Cohort table name.
cdm

(CDMConnector::cdm_from_con(): NULL)
Optional; Ignores connectionDetails, cdmSchema, and resultSchema.
connectionDetails

(DatabaseConnector::createConnectionDetails(): NULL)
Optional; In congruence with cdmSchema and resultSchema. Ignores cdm.
cdmSchema

(character(1): NULL)
Optional; In congruence with connectionDetails and resultSchema. Ignores cdm.
resultSchema

(character(1): NULL)
Optional; In congruence with connectionDetails and cdmSchema. Ignores cdm.
tempEmulationSchema

(character(1)) Schema to emulate temp tables.
minEraDuration

(integer(1): 0)
Minimum time an event era should last to be included in analysis
eraCollapseSize

(integer(1): 30)
Window of time between which two eras of the same event cohort are collapsed into one era
combinationWindow

(integer(1): 30)
Window of time two event cohorts need to overlap to be considered a combination treatment
minCellCount

(integer(1): 5)
Minimum count required per pathway. Censors data below x as ⁠<x⁠. This minimum value will carry over to the sankey diagram and sunburst plot.

Value

TreatmentPatternsResults

Examples

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (require("CirceR", character.only = TRUE, quietly = TRUE)) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  },
  error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  executeTreatmentPatterns(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )
    
  DBI::dbDisconnect(con, shutdown = TRUE)
}

export

Description

Export andromeda generated by computePathways object to sharable csv-files and/or a zip archive.

Usage

export(
  andromeda,
  outputPath = NULL,
  ageWindow = 10,
  minCellCount = 5,
  censorType = "minCellCount",
  archiveName = NULL,
  nonePaths = FALSE,
  stratify = FALSE
)

Arguments

andromeda

(Andromeda::andromeda()) Andromeda object.
outputPath

(character: NULL) Output path where to write output files to. When set to NULL no files will be written, and only the results object is returned.
ageWindow

(integer(n): 10)
Number of years to bin age groups into. It may also be a vector of integers. I.e. c(0, 18, 150) which will results in age group 0-18 which includes subjects ⁠< 19⁠. And age group 18-150 which includes subjects ⁠> 18⁠.
minCellCount

(integer(1): 5)
Minimum count required per pathway. Censors data below x as ⁠<x⁠. This minimum value will carry over to the sankey diagram and sunburst plot.
censorType

(character(1))

"minCellCount"

Censors pathways <minCellCount to minCellCount.

"remove"

Censors pathways <minCellCount by removing them completely.

"mean"

Censors pathways <minCellCount to the mean of all frequencies below minCellCount
archiveName

(character(1): NULL)
If not NULL adds the exported files to a ZIP-file with the specified archive name.
nonePaths

(logical(1)) Should None paths be included? This will fetch all persons included in the target cohort and assign them a "None" pathway. Significantly impacts performance.
stratify

(logical(1)) Should pathways be stratified? This will perform pairwise stratification between age, sex, and index year. Significantly impacts performance.

Value

TreatmentPatternsResults object

Examples

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  results <- export(
    andromeda = outputEnv
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

getResultsDataModelSpecifications

Description

Gets the results data model specifications of TreatmentPatterns.

Usage

getResultsDataModelSpecifications()

Value

data.frame

Examples

{
getResultsDataModelSpecifications()
}

InputHandler

Description

Class to handle input from the user. Supports direct paths or input fields through setDataPath().

Super class

TreatmentPatterns::ShinyModule -> InputHandler

Active bindings

reactiveValues

(reactiveValues)
reactiveValues class created by reactiveValues.

Methods

Public methods

Inherited methods

Method uiMenu()

Method to include a menuItem to link to the body.

Usage
InputHandler$uiMenu(label = "File upload", tag = "fileUpload")
Arguments
label

(character(1))
Label to show for the menuItem.

tag

(character(1))
Tag to use internally in input.

Returns

(menuItem)

Method uiBody()

Method to include a tabItem to include the body.

Usage
InputHandler$uiBody()
Returns

(tabItem)

Method server()

Method to handle the back-end.

Usage
InputHandler$server(input, output, session)
Arguments
input

(input)
Input from the server function.

output

(output)
Output from the server function.

session

(session)
Session from the server function.

Returns

(NULL)

Method uiDatabaseSelector()

Method to include a uiOutput to select between multiple uploaded files.

Usage
InputHandler$uiDatabaseSelector()
Returns

(uiOutput)

Method setDataPath()

Method to dictate where the data is coming from, either from the input through the shiny application, or from a specified path. When one is provided, the other is ignored.

Usage
InputHandler$setDataPath(tag = "uploadField", input = NULL, path = NULL)
Arguments
tag

(character(1))
Tag to use internally in input.

input

(input)
Input from the server function of the shiny app.

path

(character(1))
Path to a zip-file containing TreatmentPatterns output files.

Returns

(invisible(self))

Method clone()

The objects of this class are cloneable with this method.

Usage
InputHandler$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

launchResultsExplorer

Description

Launches the ResultExplorer shinyApp.

Usage

launchResultsExplorer()

Value

(shinyApp)

Examples

if (interactive()) {
  launchResultsExplorer()
}

plotEventDuration

Description

plotEventDuration

Usage

plotEventDuration(
  eventDurations,
  minCellCount = 0,
  treatmentGroups = "both",
  eventLines = NULL,
  includeOverall = TRUE
)

Arguments

eventDurations

(data.frame) Contents of summaryEventDuration.csv file.
minCellCount

(numeric(1): 0) Min Cell Count per event group.
treatmentGroups

(character(1): "both") "group": Only mono-, and combination-events. "individual": Only individual (combination) events. "both": Both mono-, and combination-events, and individual (combination) events.
eventLines

(numeric(n): NULL) Event lines to include, i.e. c(1, 2, 3) includes first (1), second (2), and third (3) lines of events. NULL will include all eventLines.
includeOverall

(logical(1): TRUE) TRUE: Include an overall column with the eventLines. FALSE: Exclude the overall column.

Value

ggplot

Examples

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  results <- export(outputEnv)

  plotEventDuration(
    eventDurations = results$summary_event_duration,
    minCellCount = 5,
    treatmentGroups = "group",
    eventLines = 1:4,
    includeOverall = FALSE
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

SankeyDiagram

Description

Class to handle the Sankey diagram of TreatmentPatterns.

Super classes

TreatmentPatterns::ShinyModule -> TreatmentPatterns::InteracitvePlot -> SankeyDiagram

Methods

Public methods

Inherited methods

Method clone()

The objects of this class are cloneable with this method.

Usage
SankeyDiagram$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

ShinyModule

Description

ShinyModule super class

Active bindings

namespace

Namespace of the module.

Methods

Public methods

Method new()

Initializer method

Usage
ShinyModule$new(namespace)
Arguments
namespace

(character(1))

Returns

(invisible(self))

Method validate()

Validator method

Usage
ShinyModule$validate()
Returns

(invisible(self))

Method uiMenu()

Method to include a menuItem to link to the body.

Usage
ShinyModule$uiMenu(label, tag)
Arguments
label

(character(1))
Label to show for the menuItem.

tag

(character(1))
Tag to use internally in input.

Returns

(menuItem)

Method uiBody()

Method to include a tabItem to include the body.

Usage
ShinyModule$uiBody()
Returns

(tabItem)

Method server()

Method to handle the back-end.

Usage
ShinyModule$server(input, output, session)
Arguments
input

(input)
Input from the server function.

output

(output)
Output from the server function.

session

(session)
Session from the server function.

Returns

(NULL)

Method clone()

The objects of this class are cloneable with this method.

Usage
ShinyModule$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

SunburstPlot

Description

Class to handle the Sunburst plot of TreatmentPatterns.

Super classes

TreatmentPatterns::ShinyModule -> TreatmentPatterns::InteracitvePlot -> SunburstPlot

Methods

Public methods

Inherited methods

Method clone()

The objects of this class are cloneable with this method.

Usage
SunburstPlot$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

TreatmentPatternsResults Class

Description

Houses the results of a TreatmentPatterns analysis. Each field corresponds to a file. Plotting methods are provided.

Active bindings

attrition

(data.frame)

metadata

(data.frame)

treatment_pathways

(data.frame)

summary_event_duration

(data.frame)

counts_age

(data.frame)

counts_sex

(data.frame)

counts_year

(data.frame)

cdm_source_info

(data.frame)

analyses

(data.frame)

arguments

(list)

Methods

Public methods

Method new()

Initializer method

Usage
TreatmentPatternsResults$new(
  attrition = NULL,
  metadata = NULL,
  treatmentPathways = NULL,
  summaryEventDuration = NULL,
  countsAge = NULL,
  countsSex = NULL,
  countsYear = NULL,
  cdmSourceInfo = NULL,
  analyses = NULL,
  arguments = NULL,
  filePath = NULL
)
Arguments
attrition

(data.frame) attrition result.

metadata

(⁠data.frame)⁠) metadata result.

treatmentPathways

(⁠data.frame)⁠) treatmentPathways result.

summaryEventDuration

(⁠data.frame)⁠) summaryEventDuration result.

countsAge

(⁠data.frame)⁠) countsAge result.

countsSex

(⁠data.frame)⁠) countsSex result.

countsYear

(⁠data.frame)⁠) countsYear result.

cdmSourceInfo

(data.frame) cdmSourceInfo result.

analyses

(data.frame) Analyses result.

arguments

(list) Named list of arguments used.

filePath

(character) File path to either a directory or zip-file, containing the csv-files.

Method saveAsZip()

Save the results as a zip-file.

Usage
TreatmentPatternsResults$saveAsZip(path, name, verbose = TRUE)
Arguments
path

(character(1)) Path to write to.

name

(character(1)) File name.

verbose

(logical: TRUE) Verbose messaging.

Returns

self

Method saveAsCsv()

Save the results as csv-files.

Usage
TreatmentPatternsResults$saveAsCsv(path, verbose = TRUE)
Arguments
path

(character(1)) Path to write to.

verbose

(logical: TRUE) Verbose messaging.

Returns

self

Method uploadResultsToDb()

Upload results to a resultsDatabase using ResultModelManager.

Usage
TreatmentPatternsResults$uploadResultsToDb(
  connectionDetails,
  schema,
  prefix = "tp_",
  overwrite = TRUE,
  purgeSiteDataBeforeUploading = FALSE
)
Arguments
connectionDetails

(ConnectionDetails) ConnectionDetails object from DatabaseConnector.

schema

(character(1)) Schema to write tables to.

prefix

(character(1): "tp_") Table prefix.

overwrite

(logical(1): TRUE) Should tables be overwritten?

purgeSiteDataBeforeUploading

(logical: FALSE) Should site data be purged before uploading?

Returns

self

Method load()

Load data from files.

Usage
TreatmentPatternsResults$load(filePath)
Arguments
filePath

(character(1)) Path to a directory or zip-file containing the result csv-files.

Returns

self

Method plotSunburst()

Wrapper for TreatmentPatterns::createSunburstPlot(), but with data filtering step.

Usage
TreatmentPatternsResults$plotSunburst(
  age = "all",
  sex = "all",
  indexYear = "all",
  nonePaths = FALSE,
  ...
)
Arguments
age

(character(1)) Age group.

sex

(character(1)) Sex group.

indexYear

(character(1)) Index year group.

nonePaths

(logical(1)) Should None paths be included?

...

Parameters for TreatmentPatterns::createSunburstPlot()

Returns

htmlwidget

Method plotSankey()

Wrapper for TreatmentPatterns::createSankeyDiagram(), but with data filtering step.

Usage
TreatmentPatternsResults$plotSankey(
  age = "all",
  sex = "all",
  indexYear = "all",
  nonePaths = FALSE,
  ...
)
Arguments
age

(character(1)) Age group.

sex

(character(1)) Sex group.

indexYear

(character(1)) Index year group.

nonePaths

(logical(1)) Should None paths be included?

...

Parameters for TreatmentPatterns::createSankeyDiagram()

Returns

htmlwidget

Method plotEventDuration()

Wrapper for TreatmentPatterns::plotEventDuration().

Usage
TreatmentPatternsResults$plotEventDuration(...)
Arguments
...

Parameters for TreatmentPatterns::plotEventDuration()

Returns

ggplot

Method clone()

The objects of this class are cloneable with this method.

Usage
TreatmentPatternsResults$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.