Title: Diagnostics for OMOP Common Data Model Drug Records
Version: 1.1.4
Description: Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.
License: Apache License (≥ 2)
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 4.0)
Imports: CDMConnector (≥ 1.4.0), dplyr (≥ 1.0.0), magrittr (≥ 2.0.0), rlang (≥ 1.0.0), tidyr (≥ 1.2.0), tidyselect (≥ 1.2.0), checkmate (≥ 2.0.0), glue (≥ 1.5.0), DrugUtilisation (≥ 0.7.0), omopgenerics (≥ 0.2.3), R6
Suggests: testthat (≥ 3.0.0), duckdb, odbc, DBI, knitr, rmarkdown, zip, lubridate, tibble, DT, graphics, SqlRender, ggplot2, plotly, pryr, tictoc, here, shiny (≥ 1.6.0), shinycssloaders, shinyWidgets, shinyjs, shinytest2
Config/testthat/edition: 3
URL: https://darwin-eu.github.io/DrugExposureDiagnostics/, https://github.com/darwin-eu/DrugExposureDiagnostics
BugReports: https://github.com/darwin-eu/DrugExposureDiagnostics/issues
VignetteBuilder: knitr
Language: en-US
Collate: 'DrugExposureDiagnostics-package.R' 'checkDaysSupply.R' 'checkDrugDose.R' 'checkDrugExposureDuration.R' 'checkDrugQuantity.R' 'checkDrugRoutes.R' 'checkDrugSig.R' 'checkDrugSourceConcepts.R' 'checkDrugType.R' 'checkDrugsMissing.R' 'checkTimeBetween.R' 'checkVerbatimEndDate.R' 'shinyModule.R' 'dataPlotPanel.R' 'executeChecks.R' 'getDrugRecords.R' 'globals.R' 'ingredientDescendantsInDb.R' 'metaDataPanel.R' 'mockDrugExposure.R' 'obscureCounts.R' 'runBenchmark.R' 'shiny.R' 'shinyApp.R' 'summariseChecks.R' 'utils-pipe.R' 'utils.R'
NeedsCompilation: no
Packaged: 2025-06-16 10:07:31 UTC; ginberg
Author: Ger Inberg ORCID iD [aut, cre], Edward Burn ORCID iD [aut], Theresa Burkard ORCID iD [aut], Yuchen Guo ORCID iD [ctb], Marti Catala ORCID iD [ctb], Mike Du ORCID iD [ctb], Xintong Li ORCID iD [ctb], Ross Williams ORCID iD [ctb], Erasmus MC [cph]
Maintainer: Ger Inberg <g.inberg@erasmusmc.nl>
Repository: CRAN
Date/Publication: 2025-06-16 10:30:02 UTC

DrugExposureDiagnostics: Diagnostics for OMOP Common Data Model Drug Records

Description

Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.

Author(s)

Maintainer: Ger Inberg g.inberg@erasmusmc.nl (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

DrugExposureDiagnostics ShinyApp

Description

DrugExposureDiagnostics shiny app that shows tables and plots

Details

The module consists of the following:

"dataPlotPanel"

Table and a plot (bar or box) for each check.

"metaDataPanel"

Table containing the metadata.

Super class

DrugExposureDiagnostics::ShinyModule -> ShinyApp

Methods

Public methods

Inherited methods

Method new()

Initializer method

Usage
ShinyApp$new(resultList, database_id = NULL)
Arguments
resultList

(list) List containing the output of the checks

database_id

(character) Database identifier (optional)

Returns

(invisible(self))

Method clone()

The objects of this class are cloneable with this method.

Usage
ShinyApp$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Module Decorator Class

Description

Module Decorator Class

Module Decorator Class

Active bindings

instanceId

(character(1)) Random ID

parentNamespace

(character(1)) Namespace parent module

moduleName

(character(1)) Module name

moduleId

(character(1)) Module id moduleName-instanceId

namespace

(character(1)) Namespace, composed like: ⁠[parentNamespace-]moduleName-instanceId⁠ where parentNamespace is optional

reactiveValues

(reactivevalues) Reactive values. use shiny::isolate() to get a non-reactive item from the reactive environment.

Methods

Public methods

Method new()

Initializer method

Usage
ShinyModule$new()
Returns

(self)

Method validate()

Validator method

Usage
ShinyModule$validate()
Returns

(self)

Method UI()

Method to include a tagList to include the body.

Usage
ShinyModule$UI()
Returns

(tagList)

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.

Check if Days_supply is the same as datediff(drug_exp_start_date,drug_exp_end_date)

Description

Check if Days_supply is the same as datediff(drug_exp_start_date,drug_exp_end_date)

Usage

checkDaysSupply(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

whether to get result by drug concept

sampleSize

the sample size given in execute checks

Value

a table with the stats of days supply compared to start and end date

Check the database type.

Description

Check the database type.

Usage

checkDbType(cdm, type = "cdm_reference", messageStore)

Arguments

cdm

CDMConnector reference object

type

type of the database, default cdm_reference

messageStore

checkmate collection

Get a summary of the daily drug dose

Description

Get a summary of the daily drug dose

Usage

checkDrugDose(cdm, ingredientConceptId, sampleSize = NULL, minCellCount = 5)

Arguments

cdm

CDMConnector reference object

ingredientConceptId

ingredient

sampleSize

Maximum number of records of an ingredient to estimate dose coverage. If an ingredient has more, a random sample equal to sampleSize will be considered. If NULL, all records will be used.

minCellCount

minimum number of events to report- results lower than this will be obscured. If NULL all results will be reported.

Value

a table with the stats about the daily dose

Check the drug sig field; this is the verbatim instruction for the drug as written by the provider.

Description

Check the drug sig field; this is the verbatim instruction for the drug as written by the provider.

Usage

checkDrugSig(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

whether to get result by drug concept

sampleSize

the sample size given in execute checks

Value

a table with a summary of the sig values

Check ingredient is present in given table

Description

Check ingredient is present in given table

Usage

checkIngredientInTable(cdm, conceptId, tableName, messageStore)

Arguments

cdm

CDMConnector reference object

conceptId

ingredient concept id to check

tableName

name of the table to check

messageStore

checkmate collection

Check is an ingredient

Description

Check is an ingredient

Usage

checkIsIngredient(cdm, conceptId, messageStore)

Arguments

cdm

CDMConnector reference object

conceptId

ingredient concept id to check

messageStore

checkmate collection

Check if given object is a boolean.

Description

Check if given object is a boolean.

Usage

checkLogical(input, messageStore, null.ok = TRUE)

Arguments

input

the input

messageStore

checkmate collection

null.ok

if value null is allowed

Check that the sample is bigger than the mincellcount

Description

Check that the sample is bigger than the mincellcount

Usage

checkSampleMinCellCount(sampleSize, minCellCount, messageStore)

Arguments

sampleSize

sample size for sampling

minCellCount

minimum cell count below which to obsure results

messageStore

checkmate collection

Check if given table exists in cdm.

Description

Check if given table exists in cdm.

Usage

checkTableExists(cdm, tableName, messageStore)

Arguments

cdm

CDMConnector reference object

tableName

checkmate collection

messageStore

the message store

Check the verbatim_end_date field

Description

Check the verbatim_end_date field

Usage

checkVerbatimEndDate(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

whether to get result by drug concept

sampleSize

the sample size given in execute checks

Value

a table with the stats about the verbatim_end_date

Store the given input in a remote database table. It will be stored either in a permanent table or a temporary table depending on tablePrefix.

Description

Store the given input in a remote database table. It will be stored either in a permanent table or a temporary table depending on tablePrefix.

Usage

computeDBQuery(table, tablePrefix, tableName, cdm, overwrite = TRUE)

Arguments

table

the input table

tablePrefix

The stem for the permanent tables that will be created when running the diagnostics. Permanent tables will be created using this prefix, and any existing tables that start with this will be at risk of being dropped or overwritten. If NULL, temporary tables will be used throughout.

tableName

the input table

cdm

cdm reference object

overwrite

if the table should be overwritten (default TRUE).

Value

reference to the table

dataPlotPanel

Description

Class to view the data and plot view of a DrugExposureDiagnostics check.

Value

self

Super class

DrugExposureDiagnostics::ShinyModule -> dataPlotPanel

Methods

Public methods

Inherited methods

Method new()

Method to handle the back-end.

Initializer method

Usage
dataPlotPanel$new(
  data,
  dataByConcept = NULL,
  id,
  title,
  description,
  plotPercentage,
  byConcept,
  downloadFilename,
  selectedColumns = colnames(data)
)
Arguments
data

data from the DrugExposureDiagnostics package.

dataByConcept

data by drug concept

id

the unique reference id for the module

title

panel title

description

description of data table

plotPercentage

if plot by percentage should be enabled

byConcept

add byConcept switch

downloadFilename

filename of the downloaded file

selectedColumns

default selected columns

input

(input)
Input from the server function.

output

(output)
Output from the server function.

session

(session)
Session from the server function.

Returns

(NULL)

Method uiBody()

Method to include a tabPanel to include the body.

Usage
dataPlotPanel$uiBody()
Returns

(tabItem)

Method clone()

The objects of this class are cloneable with this method.

Usage
dataPlotPanel$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Execute given checks on Drug Exposure.

Description

Execute given checks on Drug Exposure.

Usage

executeChecks(
  cdm,
  ingredients = c(1125315),
  subsetToConceptId = NULL,
  checks = c("missing", "exposureDuration", "quantity"),
  minCellCount = 5,
  sample = 10000,
  tablePrefix = NULL,
  earliestStartDate = "2010-01-01",
  verbose = FALSE,
  byConcept = TRUE,
  exposureTypeId = NULL,
  outputFolder = NULL,
  databaseId = CDMConnector::cdmName(cdm),
  filename = NULL
)

Arguments

cdm

CDMConnector reference object

ingredients

vector of ingredients, by default: acetaminophen

subsetToConceptId

vector of concept IDs of the ingredients to filter. If a concept ID is positive it will be included, a negative one will be excluded. If NULL, all concept IDs for an ingredient will be considered.

checks

the checks to be executed, by default the missing values, the exposure duration and the quantity. Possible options are "missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply", "verbatimEndDate", "dose", "sig", "quantity", "daysBetween" and "diagnosticsSummary"

minCellCount

minimum number of events to report- results lower than this will be obscured. If 0 all results will be reported.

sample

the number of samples, default 10.000

tablePrefix

The stem for the permanent tables that will be created when running the diagnostics. Permanent tables will be created using this prefix, and any existing tables that start with this will be at risk of being dropped or overwritten. If NULL, temporary tables will be used throughout.

earliestStartDate

the earliest date from which a record can be included

verbose

verbose, default FALSE

byConcept

boolean argument whether to return results by Concept or overall only

exposureTypeId

id of the drug exposure type to be filtered on (e.g. only prescribed). By default all record types will be taken into account.

outputFolder

folder to write to. If NULL, results will not be written to file

databaseId

database identifier

filename

output file name, if NULL it will be equal to databaseId

Value

named list with results

Examples

## Not run: 
db <- DBI::dbConnect(" Your database connection here ")
cdm <- CDMConnector::cdmFromCon(
  con = db,
  cdmSchema = "cdm schema name"
)
result <- executeChecks(
  cdm = cdm,
  ingredients = c(1125315)
)

## End(Not run)

Execute given checks on Drug Exposure for a single ingredient.

Description

Execute given checks on Drug Exposure for a single ingredient.

Usage

executeChecksSingleIngredient(
  cdm,
  ingredient = 1125315,
  subsetToConceptId = NULL,
  checks = c("missing", "exposureDuration", "quantity"),
  minCellCount = 5,
  sampleSize = 10000,
  tablePrefix = NULL,
  earliestStartDate = "2010-01-01",
  verbose = FALSE,
  byConcept = FALSE,
  exposureTypeId = NULL
)

Arguments

cdm

CDMConnector reference object

ingredient

ingredient, by default: acetaminophen

subsetToConceptId

vector of concept IDs of the ingredients to filter. If a concept ID is positive it will be included, a negative one will be excluded. If NULL, all concept IDs for an ingredient will be considered.

checks

the checks to be executed, by default the missing values, the exposure duration and the quantity.

minCellCount

minimum number of events to report- results lower than this will be obscured. If 0 all results will be reported.

sampleSize

the number of samples, default 10.000

tablePrefix

The stem for the permanent tables that will be created when running the diagnostics. Permanent tables will be created using this prefix, and any existing tables that start with this will be at risk of being dropped or overwritten. If NULL, temporary tables will be used throughout.

earliestStartDate

the earliest date from which a record can be included

verbose

verbose, default FALSE

byConcept

boolean argument whether to return results by Concept or overall only

exposureTypeId

id of the drug exposure type to be filtered on (e.g. only prescribed). By default all record types will be taken into account.

Value

named list with results

Get all options that can be passed to the "checks" parameter

Description

Get all options that can be passed to the "checks" parameter

Usage

getAllCheckOptions()

Check missings in drug exposure records

Description

Check missings in drug exposure records

Usage

getDrugMissings(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

by individual drug Concept

sampleSize

the sample size given in execute checks

Value

a table with a summary of missing records

Drug exposure records for ingredients of interest

Description

Drug exposure records for ingredients of interest

Usage

getDrugRecords(
  cdm,
  ingredient,
  includedConceptsTable,
  drugRecordsTable = "drug_exposure",
  exposureTypeId = NULL,
  tablePrefix = NULL,
  verbose = FALSE
)

Arguments

cdm

CDMConnector reference object

ingredient

Concept ID for ingredient of interest

includedConceptsTable

includedConceptsTable

drugRecordsTable

drugRecordsTable, default "drug_exposure"

exposureTypeId

id of the drug exposure type to be filtered on (e.g. only prescribed). By default all record types will be taken into account.

tablePrefix

The stem for the permanent tables that will be created when running the diagnostics. Permanent tables will be created using this prefix, and any existing tables that start with this will be at risk of being dropped or overwritten. If NULL, temporary tables will be used throughout.

verbose

verbose

Value

a table containing drug exposure records

Get drug exposure route types

Description

Get drug exposure route types

Usage

getDrugRoutes(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

by individual drug Concept

sampleSize

the sample size given in execute checks

Value

a table with the drug exposure route types

Check drug exposure source types

Description

Check drug exposure source types

Usage

getDrugSourceConcepts(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

modified drug exposure table

sampleSize

the sample size given in execute checks

Value

a table with the drug source concepts

Get drug exposure record types

Description

Get drug exposure record types

Usage

getDrugTypes(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

by individual drug Concept

sampleSize

the sample size given in execute checks

Value

a table with the drug exposure record types

Compute the difference in days between 2 variables in a database table.

Description

Compute the difference in days between 2 variables in a database table.

Usage

getDuration(
  cdm,
  tableName = "drug_exposure",
  startDateCol = "drug_exposure_start_date",
  endDateCol = "drug_exposure_end_date",
  colName = "duration"
)

Arguments

cdm

CDMConnector reference object

tableName

the table name

startDateCol

the start date column name

endDateCol

the end date column name

colName

the result column name

Value

the table with as new column the duration

Get the descendants for the given ingredients

Description

Get the descendants for the given ingredients

Usage

ingredientDescendantsInDb(
  cdm,
  ingredient,
  drugRecordsTable = "drug_exposure",
  tablePrefix = NULL,
  verbose = FALSE
)

Arguments

cdm

CDMConnector reference object

ingredient

ingredient concept id for ingredient of interest

drugRecordsTable

table name of the drug exposure records, default "drug_exposure"

tablePrefix

The stem for the permanent tables that will be created when running the diagnostics. Permanent tables will be created using this prefix, and any existing tables that start with this will be at risk of being dropped or overwritten. If NULL, temporary tables will be used throughout.

verbose

if verbose set to TRUE, the function will output extra messages

Value

temp table with concepts used

metaDataPanel

Description

Class to view the metadata of a DrugExposureDiagnostics execution.

Value

self

Super class

DrugExposureDiagnostics::ShinyModule -> metaDataPanel

Methods

Public methods

Inherited methods

Method new()

Method to handle the back-end.

Initializer method

Usage
metaDataPanel$new(data, id, title, description, downloadFilename)
Arguments
data

data from the DrugExposureDiagnostics package.

id

the unique reference id for the module

title

panel title

description

description of data table

downloadFilename

filename of the downloaded file

input

(input)
Input from the server function.

output

(output)
Output from the server function.

session

(session)
Session from the server function.

Returns

(NULL)

Method uiBody()

Method to include a tabPanel to include the body.

Usage
metaDataPanel$uiBody()
Returns

(tabItem)

Method clone()

The objects of this class are cloneable with this method.

Usage
metaDataPanel$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Mock Drug exposure tables for ingredients of interest

Description

Mock Drug exposure tables for ingredients of interest

Usage

mockDrugExposure(
  drug_exposure = NULL,
  concept_ancestor = NULL,
  concept_relationship = NULL,
  concept = NULL,
  drug_strength = NULL,
  ingredient_drug_records = NULL,
  drug_exposure_size = 100,
  patient_size = 50,
  person = NULL,
  observation_period = NULL,
  amount_val = c(NA, 100, 200, 300),
  den_val = c(1, 10, 100),
  amount_unit = c(8587, 8576, 9655),
  num_unit = c(8587, 8576, 9655),
  denom_unit = c(8587, 8576, 8505),
  num_val = c(1, 2, 3),
  seed = 1
)

Arguments

drug_exposure

drug exposure table

concept_ancestor

concept_ancestor table

concept_relationship

concept_relationship table

concept

concept table

drug_strength

drug strength table

ingredient_drug_records

modified drug exposure table having drug name

drug_exposure_size

the sample size of the drug exposure table

patient_size

the number of unique patients in the drug exposure table

person

person table

observation_period

observation_period table

amount_val

vector of possible numeric amount value for the drug in the drug strength table

den_val

vector of possible numeric denominator value for the drug in drug strength table

amount_unit

vector of possible amount unit type drug strength table representing milligram, milliliter and microgram

num_unit

vector of possible numerator unit type drug strength table representing milligram, milliliter and microgram

denom_unit

vector of possible numerator unit type drug strength table representing milligram, milliliter and hour

num_val

vector of possible numeric numerator denominator value drug strength table

seed

seed to make results reproducible

Value

CDMConnector CDM reference object to duckdb database with mock data include concept_ancestor, concept, drug_strength, drug_exposure tables

Obscure the small number of counts

Description

Obscure the small number of counts

Usage

obscureCounts(table, tableName, minCellCount = 5, substitute = NA)

Arguments

table

the table as a tibble

tableName

the table name

minCellCount

the minimum number of counts that will be displayed. If 0 all results will be reported.

substitute

the substitute value if values will be obscured

Value

the input table with results obscured if minCellCount applies

Print duration from start to now and print it as well as new status message

Description

Print duration from start to now and print it as well as new status message

Usage

printDurationAndMessage(message, start)

Arguments

message

the message

start

the start time

Value

the current time

Run benchmark for ExecuteSingleIngredient

Description

Run benchmark for ExecuteSingleIngredient

Usage

runBenchmarkExecuteSingleIngredient(
  cdm,
  ingredients = c(1125315),
  subsetToConceptId = NULL,
  checks = c("missing", "exposureDuration", "quantity"),
  minCellCount = 5,
  sampleSize = 10000,
  tablePrefix = NULL,
  earliestStartDate = "2010-01-01",
  verbose = FALSE,
  byConcept = FALSE
)

Arguments

cdm

CDMConnector reference object

ingredients

vector of ingredients, by default: acetaminophen

subsetToConceptId

vector of concept IDs of the ingredients to filter. If a concept ID is positive it will be included, a negative one will be excluded. If NULL (default), all concept IDs for an ingredient will be considered.

checks

the checks to be executed, by default the missing values, the exposure duration and the quantity. Possible options are "missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply", "verbatimEndDate", "dose", "sig", "quantity" and "diagnosticsSummary"

minCellCount

minimum number of events to report- results lower than this will be obscured. If 0 all results will be reported.

sampleSize

the number of samples, default 10.000

tablePrefix

The stem for the permanent tables that will be created when running the diagnostics. Permanent tables will be created using this prefix, and any existing tables that start with this will be at risk of being dropped or overwritten. If NULL, temporary tables will be

earliestStartDate

the earliest date from which a record can be included

verbose

verbose, default FALSE

byConcept

boolean argument whether to return results by Concept or overall only

Value

a tibble with the time taken and memory usage for different analysis per ingredient

Examples

## Not run: 
cdm <- mockDrugExposure()

benchmarkResults <- runBenchmarkExecuteSingleIngredient(cdm)

## End(Not run)

Create a summary about the diagnostics results

Description

Create a summary about the diagnostics results

Usage

summariseChecks(resultList)

Arguments

resultList

a list with the diagnostics results

Value

a table containing the diagnostics summary

Summarise drug exposure record durations

Description

Summarise drug exposure record durations

Usage

summariseDrugExposureDuration(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

by individual drug Concept

sampleSize

the sample size given in execute checks

Value

a table with the drug exposure record durations

Summarise the quantity column of the drug_exposure table

Description

Summarise the quantity column of the drug_exposure table

Usage

summariseQuantity(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = sampleSize
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

whether to get result by drug concept

sampleSize

the sample size given in execute checks

Value

a table with the summarized quantity result

Check time in between drug records per person and report the summary

Description

Check time in between drug records per person and report the summary

Usage

summariseTimeBetween(
  cdm,
  drugRecordsTable = "ingredient_drug_records",
  byConcept = TRUE,
  sampleSize = 10000
)

Arguments

cdm

CDMConnector reference object

drugRecordsTable

a modified version of the drug exposure table, default "ingredient_drug_records"

byConcept

whether to get result by drug concept

sampleSize

the sample size given in execute checks

Value

a table with the stats about the time between

Validate the "checks" parameter

Description

Validate the "checks" parameter

Usage

validateChecks(checks, messageStore)

Arguments

checks

the checks that have been passed to executeChecks

messageStore

checkmate collection

View the results in the Shiny app

Description

View the results in the Shiny app

Usage

viewResults(
  dataFolder,
  makePublishable = FALSE,
  publishDir = file.path(getwd(), "ResultsExplorer"),
  overwritePublishDir = FALSE,
  launch.browser = FALSE
)

Arguments

dataFolder

A folder where the exported zip files with the results are stored. Zip files containing results from multiple databases can be placed in the same folder.

makePublishable

(Optional) copy data files to make app publishable to posit connect/shinyapp.io

publishDir

If make publishable is true - the directory that the shiny app is copied to

overwritePublishDir

(Optional) If make publishable is true - overwrite the directory for publishing

launch.browser

Should the app be launched in your default browser, or in a Shiny window. Note: copying to clipboard will not work in a Shiny window.

Details

Launches a Shiny app that allows the user to explore the diagnostics

Write a result to a file on disk.

Description

Write a result to a file on disk.

Usage

writeFile(result, resultName, databaseId, dbDir)

Arguments

result

check result

resultName

name of the result

databaseId

database identifier

dbDir

output directory for current db

Value

No return value, called for side effects

Examples

## Not run: 
resultList <- list("mtcars" = mtcars)
result <- writeZipToDisk(
  metadata = metadata,
  databaseId = "mtcars",
  outputFolder = here::here()
)

## End(Not run)

Write (ingredient) diagnostics results on disk in given output folder.

Description

Write (ingredient) diagnostics results on disk in given output folder.

Usage

writeIngredientResultToDisk(
  resultList,
  databaseId,
  outputFolder,
  clearDBDir = FALSE
)

Arguments

resultList

named list with results

databaseId

database identifier

outputFolder

folder to write to

clearDBDir

if database directory should be cleared

Value

No return value, called for side effects

Examples

## Not run: 
resultList <- list("mtcars" = mtcars)
result <- writeIngredientResultToDisk(
  resultList = resultList,
  databaseId = "mtcars",
  outputFolder = here::here()
)

## End(Not run)

Write diagnostics results to a zip file on disk in given output folder.

Description

Write diagnostics results to a zip file on disk in given output folder.

Usage

writeResultToDisk(resultList, databaseId, outputFolder, filename = NULL)

Arguments

resultList

named list with results

databaseId

database identifier

outputFolder

folder to write to

filename

output filename, if NULL it will be equal to databaseId

Value

No return value, called for side effects

Examples

## Not run: 
resultList <- list("mtcars" = mtcars)
result <- writeResultToDisk(
  resultList = resultList,
  databaseId = "mtcars",
  outputFolder = here::here()
)

## End(Not run)

Write (ingredient) diagnostics results on disk in given output folder.

Description

Write (ingredient) diagnostics results on disk in given output folder.

Usage

writeZipToDisk(metadata, databaseId, outputFolder, filename = NULL)

Arguments

metadata

metadata results

databaseId

database identifier

outputFolder

folder to write to

filename

output filename for the zip file

Value

No return value, called for side effects

Examples

## Not run: 
resultList <- list("mtcars" = mtcars)
result <- writeZipToDisk(
  metadata = metadata,
  databaseId = "mtcars",
  outputFolder = here::here()
)

## End(Not run)