Package 'OmopSketch' reference manual

Title:	Characterise Tables of an OMOP Common Data Model Instance
Description:	Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.
Authors:	Marta Alcalde-Herraiz [aut] (ORCID: <https://orcid.org/0009-0002-4405-1814>), Kim Lopez-Guell [aut] (ORCID: <https://orcid.org/0000-0002-8462-8668>), Elin Rowlands [aut] (ORCID: <https://orcid.org/0009-0005-5166-0417>), Cecilia Campanile [aut, cre] (ORCID: <https://orcid.org/0009-0007-6629-4661>), Edward Burn [aut] (ORCID: <https://orcid.org/0000-0002-9286-1128>), Martí Català [aut] (ORCID: <https://orcid.org/0000-0003-3308-9905>)
Maintainer:	Cecilia Campanile <[email protected]>
License:	Apache License (>= 2)
Version:	1.0.1.900
Built:	2026-06-04 17:01:06 UTC
Source:	https://github.com/ohdsi/omopsketch

Tables in the cdm_reference that contain clinical information

Description

This function provides a list of allowed inputs for the omopTableName argument in summariseClinicalRecords().

Usage

clinicalTables()
clinicalTables()

Value

A character vector with table names.

Examples

library(OmopSketch)

clinicalTables()

library(OmopSketch)

clinicalTables()

Summarise Database Characteristics for OMOP CDM

Description

Summarise Database Characteristics for OMOP CDM

Usage

databaseCharacteristics(
  cdm,
  omopTableName = c("visit_occurrence", "visit_detail", "condition_occurrence",
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement",
    "observation", "death"),
  sample = NULL,
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL,
  interval = "overall",
  conceptIdCounts = FALSE,
  ...
)
databaseCharacteristics(
  cdm,
  omopTableName = c("visit_occurrence", "visit_detail", "condition_occurrence",
    "drug_exposure", "procedure_occurrence", "device_exposure", "measurement",
    "observation", "death"),
  sample = NULL,
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL,
  interval = "overall",
  conceptIdCounts = FALSE,
  ...
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

omopTableName

A character vector of the names of the tables to summarise in the cdm object. Run clinicalTables() to check the available options.

sample

Either an integer or a character string.

If an integer (n > 0), the function will first sample n distinct person_ids from the person table and then subset the input tables to those subjects.
If a character string, it must be the name of a cohort in the cdm; in this case, the input tables are subset to subjects (subject_id) belonging to that cohort.
Use NULL to disable subsetting (default value).

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

dateRange

A vector of two dates defining the desired study period. Only the start_date column of the OMOP table is checked to ensure it falls within this range. If dateRange is NULL, no restriction is applied.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

conceptIdCounts

Logical; whether to summarise concept ID counts (TRUE) or not (FALSE).

...

additional arguments passed to the OmopSketch functions that are used internally.

Value

A summarised_result object with the results.

Examples

## Not run: 
library(OmopSketch)
library(omock)
library(dplyr)
library(here)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- databaseCharacteristics(
  cdm = cdm,
  sample = 100,
  omopTableName = c("drug_exposure", "condition_occurrence"),
  sex = TRUE,
  ageGroup = list(c(0, 50), c(51, 100)),
  interval = "years",
  conceptIdCounts = FALSE
)

result |>
  glimpse()

shinyCharacteristics(result = result, directory = here())

cdmDisconnect(cdm = cdm)

## End(Not run)

## Not run: 
library(OmopSketch)
library(omock)
library(dplyr)
library(here)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- databaseCharacteristics(
  cdm = cdm,
  sample = 100,
  omopTableName = c("drug_exposure", "condition_occurrence"),
  sex = TRUE,
  ageGroup = list(c(0, 50), c(51, 100)),
  interval = "years",
  conceptIdCounts = FALSE
)

result |>
  glimpse()

shinyCharacteristics(result = result, directory = here())

cdmDisconnect(cdm = cdm)

## End(Not run)

Creates a mock database to test OmopSketch package

Description

Usage

mockOmopSketch(
  numberIndividuals = 100,
  con = lifecycle::deprecated(),
  writeSchema = lifecycle::deprecated(),
  seed = lifecycle::deprecated()
)
mockOmopSketch(
  numberIndividuals = 100,
  con = lifecycle::deprecated(),
  writeSchema = lifecycle::deprecated(),
  seed = lifecycle::deprecated()
)

Arguments

numberIndividuals

Number of individuals to create in the cdm reference object.

con

deprecated.

writeSchema

deprecated.

seed

deprecated.

Value

A mock cdm_reference object.

Plot the concept counts of a summariseConceptSetCounts output

Description

Usage

plotConceptSetCounts(result, facet = NULL, colour = NULL)
plotConceptSetCounts(result, facet = NULL, colour = NULL)

Arguments

result

A summarised_result object (output of summariseConceptSetCounts()).

Columns to face by. Formula format can be provided. See possible columns to face by with: visOmopResults::tidyColumns().

colour

Columns to colour by. See possible columns to colour by with: visOmopResults::tidyColumns().

Value

A plot visualisation.

Examples


library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptSetCounts(
  cdm = cdm,
  conceptSet = list(
    "asthma" = c(4051466, 317009),
    "rhinitis" = c(4280726, 4048171, 40486433)
  )
)

result |>
  filter(variable_name == "Number subjects") |>
  plotConceptSetCounts(
    facet = "codelist_name",
    colour = "standard_concept_name"
  )

cdmDisconnect(cdm = cdm)


library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptSetCounts(
  cdm = cdm,
  conceptSet = list(
    "asthma" = c(4051466, 317009),
    "rhinitis" = c(4280726, 4048171, 40486433)
  )
)

result |>
  filter(variable_name == "Number subjects") |>
  plotConceptSetCounts(
    facet = "codelist_name",
    colour = "standard_concept_name"
  )

cdmDisconnect(cdm = cdm)

Create a ggplot2 plot from the output of summariseInObservation()

Description

Usage

plotInObservation(result, facet = NULL, colour = NULL)
plotInObservation(result, facet = NULL, colour = NULL)

Arguments

result

A summarised_result object (output of summariseInObservation()).

Columns to face by. Formula format can be provided. See possible columns to face by with: visOmopResults::tidyColumns().

colour

Columns to colour by. See possible columns to colour by with: visOmopResults::tidyColumns().

Value

A plot visualisation.

Examples


library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseInObservation(
  observationPeriod = cdm$observation_period,
  output = c("person-days", "record"),
  ageGroup = list("<=40" = c(0, 40), ">40" = c(41, Inf)),
  sex = TRUE
)

result |>
  filter(variable_name == "Person-days") |>
  plotInObservation(facet = "sex", colour = "age_group")

cdmDisconnect(cdm = cdm)


library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseInObservation(
  observationPeriod = cdm$observation_period,
  output = c("person-days", "record"),
  ageGroup = list("<=40" = c(0, 40), ">40" = c(41, Inf)),
  sex = TRUE
)

result |>
  filter(variable_name == "Person-days") |>
  plotInObservation(facet = "sex", colour = "age_group")

cdmDisconnect(cdm = cdm)

Create a plot from the output of summariseObservationPeriod()

Description

Create a plot from the output of summariseObservationPeriod()

Usage

plotObservationPeriod(
  result,
  variableName = "Number subjects",
  plotType = "barplot",
  facet = NULL,
  colour = NULL,
  style = NULL,
  type = NULL
)
plotObservationPeriod(
  result,
  variableName = "Number subjects",
  plotType = "barplot",
  facet = NULL,
  colour = NULL,
  style = NULL,
  type = NULL
)

Arguments

result

A summarised_result object (output of summariseObservationPeriod()).

variableName

The variable to plot it can be: "Number subjects", "Records per person", "Duration in days" or "Days to next observation period".

plotType

The plot type, it can be: "barplot", "boxplot", "densityplot" or "cumulativeplot".

Columns to face by. Formula format can be provided. See possible columns to face by with: visOmopResults::tidyColumns().

colour

Columns to colour by. See possible columns to colour by with: visOmopResults::tidyColumns().

style

Visual theme to apply. Character, or NULL. If a character, this may be either the name of a built-in style (see visOmopResults::plotStyle()), or a path to a .yml file that defines a custom style. If NULL, the function will use the explicit default style, unless a global style option is set (see visOmopResults::setGlobalPlotOptions()) or a _brand.yml file is present (in that order).

type

Character string indicating the output plot format. See visOmopResults::plotType() for the list of supported plot types. If type = NULL, the function will use the global setting defined via visOmopResults::setGlobalPlotOptions() (if available); otherwise, a standard ggplot2 plot is produced by default.

Value

A plot visualisation.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseObservationPeriod(cdm = cdm)

tableObservationPeriod(result = result)

plotObservationPeriod(
  result = result,
  variableName = "Duration in days",
  plotType = "boxplot"
)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseObservationPeriod(cdm = cdm)

tableObservationPeriod(result = result)

plotObservationPeriod(
  result = result,
  variableName = "Duration in days",
  plotType = "boxplot"
)

cdmDisconnect(cdm = cdm)

Visualise the output of `summarisePerson()`

Description

Visualise the output of summarisePerson()

Usage

plotPerson(result, variableName = NULL, style = NULL, type = NULL)
plotPerson(result, variableName = NULL, style = NULL, type = NULL)

Arguments

result

A summarised_result object (output of summarisePerson()).

variableName

The variable to plot, a choice between unique(result$variable_name). If NULL it will only work if only one variable is present in the result object.

style

type

Value

A plot visualisation.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)

Create a ggplot of the records' count trend

Description

Usage

plotRecordCount(result, facet = NULL, colour = NULL)
plotRecordCount(result, facet = NULL, colour = NULL)

Arguments

result

A summarised_result object (output of summariseRecordCount()).

Columns to face by. Formula format can be provided. See possible columns to face by with: visOmopResults::tidyColumns().

colour

Columns to colour by. See possible columns to colour by with: visOmopResults::tidyColumns().

Value

A plot visualisation.

Examples


library(omock)
library(OmopSketch)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

summarisedResult <- summariseRecordCount(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

plotRecordCount(
  result = summarisedResult,
  colour = "age_group",
  facet = sex ~ .
)

cdmDisconnect(cdm = cdm)


library(omock)
library(OmopSketch)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

summarisedResult <- summariseRecordCount(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

plotRecordCount(
  result = summarisedResult,
  colour = "age_group",
  facet = sex ~ .
)

cdmDisconnect(cdm = cdm)

Create a ggplot2 plot from the output of summariseTrend()

Description

Create a ggplot2 plot from the output of summariseTrend()

Usage

plotTrend(
  result,
  output = NULL,
  facet = "type",
  colour = NULL,
  style = NULL,
  type = NULL
)
plotTrend(
  result,
  output = NULL,
  facet = "type",
  colour = NULL,
  style = NULL,
  type = NULL
)

Arguments

result

A summarised_result object (output of summariseTrend()).

output

The output to plot. Accepted values are: "record", "person", "person-days", "age", "sex" If not specified, the function will default to:

the only available output if there is just one in the results, or
"record" if multiple outputs are present.

Columns to face by. Formula format can be provided. See possible columns to face by with: visOmopResults::tidyColumns().

colour

Columns to colour by. See possible columns to colour by with: visOmopResults::tidyColumns().

style

type

Value

A plot visualisation.

Examples


library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseTrend(cdm,
  episode = "observation_period",
  output = c("person-days", "record"),
  interval = "years",
  ageGroup = list("<=40" = c(0, 40), ">40" = c(41, Inf)),
  sex = TRUE
)

plotTrend(
  result = result,
  output = "record",
  colour = "sex",
  facet = "age_group"
)

cdmDisconnect(cdm = cdm)


library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseTrend(cdm,
  episode = "observation_period",
  output = c("person-days", "record"),
  interval = "years",
  ageGroup = list("<=40" = c(0, 40), ">40" = c(41, Inf)),
  sex = TRUE
)

plotTrend(
  result = result,
  output = "record",
  colour = "sex",
  facet = "age_group"
)

cdmDisconnect(cdm = cdm)

Generate an interactive Shiny application that visualises the results obtained from the `databaseCharacteristics()` function

Description

Generate an interactive Shiny application that visualises the results obtained from the databaseCharacteristics() function

Usage

shinyCharacteristics(
  result,
  directory,
  background = TRUE,
  title = "Database characterisation",
  logo = "ohdsi",
  theme = NULL
)
shinyCharacteristics(
  result,
  directory,
  background = TRUE,
  title = "Database characterisation",
  logo = "ohdsi",
  theme = NULL
)

Arguments

result

A summarised_result object (output of databaseCharacteristics()).

directory

A character string specifying the directory where the application will be saved.

background

Background panel for the Shiny app.

If set to TRUE (default), a standard background panel with a general description will be included.
If set to FALSE, no background panel will be displayed.
If it is a path (e.g., "path/to/file.md") tha file will be used as background panel of your shiny App.

title

Title of the shiny. Default is "Characterisation".

logo

Name of a logo or path to a logo. If NULL no logo is included. Only svg format allowed for the moment.

theme

A character string specifying the theme for the Shiny application. It can be any of the OmopViewer supported themes.

Value

This function invisibly returns NULL and generates a static Shiny app in the specified directory.

Examples

## Not run: 
library(OmopSketch)
library(omock)
library(here)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

res <- databaseCharacteristics(cdm = cdm)

shinyCharacteristics(result = res, directory = here())

cdmDisconnect(cdm = cdm)

## End(Not run)

## Not run: 
library(OmopSketch)
library(omock)
library(here)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

res <- databaseCharacteristics(cdm = cdm)

shinyCharacteristics(result = res, directory = here())

cdmDisconnect(cdm = cdm)

## End(Not run)

Summarise an omop table from a cdm object

Description

You will obtain information related to the number of records, number of subjects, whether the records are in observation, number of present domains, number of present concepts, missing data and inconsistencies in start date and end date.

Usage

summariseClinicalRecords(
  cdm,
  omopTableName,
  recordsPerPerson = c("mean", "sd", "median", "q25", "q75", "min", "max"),
  conceptSummary = TRUE,
  missingData = TRUE,
  quality = TRUE,
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL,
  inObservation = lifecycle::deprecated(),
  standardConcept = lifecycle::deprecated(),
  sourceVocabulary = lifecycle::deprecated(),
  domainId = lifecycle::deprecated(),
  typeConcept = lifecycle::deprecated()
)
summariseClinicalRecords(
  cdm,
  omopTableName,
  recordsPerPerson = c("mean", "sd", "median", "q25", "q75", "min", "max"),
  conceptSummary = TRUE,
  missingData = TRUE,
  quality = TRUE,
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL,
  inObservation = lifecycle::deprecated(),
  standardConcept = lifecycle::deprecated(),
  sourceVocabulary = lifecycle::deprecated(),
  domainId = lifecycle::deprecated(),
  typeConcept = lifecycle::deprecated()
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

omopTableName

A character vector of the names of the tables to summarise in the cdm object. Run clinicalTables() to check the available options.

recordsPerPerson

Generates summary statistics for the number of records per person. Set to NULL if no summary statistics are required.

conceptSummary

Logical. If TRUE, includes summaries of concept-level information, including:

Domain ID of standard concepts.
Type concept ID.
Standard vs non-standard concepts.
Source vocabulary usage.

missingData

Logical. If TRUE, includes a summary of missing data for relevant fields.

quality

Logical. If TRUE, performs basic data quality checks, including:

Percentage of records within the observation period.
Number of records with end date before start date.
Number of records with start date before the person's birth date.

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

dateRange

inObservation

Deprecated. Use quality = TRUE instead.

standardConcept

Deprecated. Use conceptSummary = TRUE instead.

sourceVocabulary

Deprecated. Use conceptSummary = TRUE instead.

domainId

Deprecated. Use conceptSummary = TRUE instead.

typeConcept

Deprecated. Use conceptSummary = TRUE instead.

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = c("mean", "sd"),
  quality = TRUE,
  conceptSummary = TRUE,
  missingData = TRUE
)

tableClinicalRecords(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  recordsPerPerson = c("mean", "sd"),
  quality = TRUE,
  conceptSummary = TRUE,
  missingData = TRUE
)

tableClinicalRecords(result = result)

cdmDisconnect(cdm = cdm)

Summarise concept counts in patient-level data

Description

Only concepts recorded during observation period are counted.

Usage

summariseConceptCounts(
  cdm,
  conceptId,
  countBy = c("record", "person"),
  concept = TRUE,
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL
)
summariseConceptCounts(
  cdm,
  conceptId,
  countBy = c("record", "person"),
  concept = TRUE,
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

conceptId

List of concept IDs to summarise.

countBy

Either "record" for record-level counts or "person" for person-level counts

concept

TRUE or FALSE. If TRUE code use will be summarised by concept.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

dateRange

Details

Value

A summarised_result object with the results.

Summarise concept use in patient-level data

Description

Only concepts recorded during observation period are counted.

Usage

summariseConceptIdCounts(
  cdm,
  omopTableName,
  countBy = "record",
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  inObservation = FALSE,
  sample = NULL,
  dateRange = NULL,
  year = lifecycle::deprecated()
)
summariseConceptIdCounts(
  cdm,
  omopTableName,
  countBy = "record",
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  inObservation = FALSE,
  sample = NULL,
  dateRange = NULL,
  year = lifecycle::deprecated()
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

omopTableName

A character vector of the names of the tables to summarise in the cdm object. Run clinicalTables() to check the available options.

countBy

Either "record" for record-level counts or "person" for person-level counts.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

inObservation

Logical. If TRUE, the results are stratified to indicate whether each record occurs within an observation period.

sample

Either an integer or a character string.

If an integer (n > 0), the function will first sample n distinct person_ids from the person table and then subset the input tables to those subjects.
If a character string, it must be the name of a cohort in the cdm; in this case, the input tables are subset to subjects (subject_id) belonging to that cohort.
Use NULL to disable subsetting (default value).

dateRange

year

deprecated.

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptIdCounts(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  countBy = c("record", "person"),
  sex = TRUE
)

tableConceptIdCounts(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptIdCounts(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  countBy = c("record", "person"),
  sex = TRUE
)

tableConceptIdCounts(result = result)

cdmDisconnect(cdm = cdm)

Summarise concept counts in patient-level data

Description

Only concepts recorded during observation period are counted.

Usage

summariseConceptSetCounts(
  cdm,
  conceptSet,
  countBy = c("record", "person"),
  concept = TRUE,
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL
)
summariseConceptSetCounts(
  cdm,
  conceptSet,
  countBy = c("record", "person"),
  concept = TRUE,
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  dateRange = NULL
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

conceptSet

List of concept IDs to summarise.

countBy

Either "record" for record-level counts or "person" for person-level counts

concept

TRUE or FALSE. If TRUE code use will be summarised by concept.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

dateRange

Details

Value

A summarised_result object with the results.

Summarise the number of people in observation during a specific interval of time

Description

Usage

summariseInObservation(
  observationPeriod,
  interval = "overall",
  output = "record",
  ageGroup = NULL,
  sex = FALSE,
  dateRange = NULL
)
summariseInObservation(
  observationPeriod,
  interval = "overall",
  output = "record",
  ageGroup = NULL,
  sex = FALSE,
  dateRange = NULL
)

Arguments

observationPeriod

An observation_period omop table. It must be part of a cdm_reference object.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

output

Output format. It can be either the number of records ("record") that are in observation in the specific interval of time, the number of person-days ("person-days"), the number of subjects ("person"), the number of females ("sex") or the median age of population in observation ("age").

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

dateRange

Value

A summarised_result object with the results.

Summarise missing data in omop tables

Description

Summarise missing data in omop tables

Usage

summariseMissingData(
  cdm,
  omopTableName,
  col = NULL,
  sex = FALSE,
  interval = "overall",
  ageGroup = NULL,
  sample = 1e+05,
  dateRange = NULL,
  year = lifecycle::deprecated()
)
summariseMissingData(
  cdm,
  omopTableName,
  col = NULL,
  sex = FALSE,
  interval = "overall",
  ageGroup = NULL,
  sample = 1e+05,
  dateRange = NULL,
  year = lifecycle::deprecated()
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

omopTableName

A character vector of the names of the tables to summarise in the cdm object. Run clinicalTables() to check the available options.

col

A character vector of column names to check for missing values. If NULL, all columns in the specified tables are checked. Default is NULL.

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

sample

Either an integer or a character string.

If an integer (n > 0), the function will first sample n distinct person_ids from the person table and then subset the input tables to those subjects.
If a character string, it must be the name of a cohort in the cdm; in this case, the input tables are subset to subjects (subject_id) belonging to that cohort.
Use NULL to disable subsetting (default value).

dateRange

year

deprecated

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseMissingData(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "visit_occurrence"),
  sample = 10000
)

tableMissingData(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseMissingData(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "visit_occurrence"),
  sample = 10000
)

tableMissingData(result = result)

cdmDisconnect(cdm = cdm)

Summarise the observation period table getting some overall statistics in a summarised_result object

Description

Summarise the observation period table getting some overall statistics in a summarised_result object

Usage

summariseObservationPeriod(
  cdm,
  estimates = c("mean", "sd", "min", "q05", "q25", "median", "q75", "q95", "max",
    "density"),
  missingData = TRUE,
  quality = TRUE,
  byOrdinal = TRUE,
  ageGroup = NULL,
  sex = FALSE,
  dateRange = NULL,
  observationPeriod = lifecycle::deprecated()
)
summariseObservationPeriod(
  cdm,
  estimates = c("mean", "sd", "min", "q05", "q25", "median", "q75", "q95", "max",
    "density"),
  missingData = TRUE,
  quality = TRUE,
  byOrdinal = TRUE,
  ageGroup = NULL,
  sex = FALSE,
  dateRange = NULL,
  observationPeriod = lifecycle::deprecated()
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

estimates

Estimates to summarise the variables of interest ( ⁠Records per person⁠, ⁠Duration in days⁠ and ⁠Days to next observation period⁠).

missingData

Logical. If TRUE, includes a summary of missing data for relevant fields.

quality

Logical. If TRUE, performs basic data quality checks, including:

Number of subjects not included in person table.
Number of records with end date before start date.
Number of records with start date before the person's birth date.

byOrdinal

Boolean variable. Whether to stratify by the ordinal observation period (e.g., 1st, 2nd, etc.) (TRUE) or simply analyze overall data (FALSE)

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

dateRange

observationPeriod

deprecated.

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseObservationPeriod(cdm = cdm)

tableObservationPeriod(result = result)

plotObservationPeriod(
  result = result,
  variableName = "Duration in days",
  plotType = "boxplot"
)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseObservationPeriod(cdm = cdm)

tableObservationPeriod(result = result)

plotObservationPeriod(
  result = result,
  variableName = "Duration in days",
  plotType = "boxplot"
)

cdmDisconnect(cdm = cdm)

Summarise a cdm_reference object creating a snapshot with the metadata of the cdm_reference object

Description

Summarise a cdm_reference object creating a snapshot with the metadata of the cdm_reference object

Usage

summariseOmopSnapshot(cdm)
summariseOmopSnapshot(cdm)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

cdmDisconnect(cdm = cdm)

Summarise person table

Description

Summarise person table

Usage

summarisePerson(cdm)
summarisePerson(cdm)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)

Summarise record counts of an omop_table using a specific time interval

Description

Only records that fall within the observation period are considered.

Usage

summariseRecordCount(
  cdm,
  omopTableName,
  interval = "overall",
  ageGroup = NULL,
  sex = FALSE,
  sample = NULL,
  dateRange = NULL
)
summariseRecordCount(
  cdm,
  omopTableName,
  interval = "overall",
  ageGroup = NULL,
  sex = FALSE,
  sample = NULL,
  dateRange = NULL
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

omopTableName

A character vector of the names of the tables to summarise in the cdm object. Run clinicalTables() to check the available options.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

sample

Either an integer or a character string.

If an integer (n > 0), the function will first sample n distinct person_ids from the person table and then subset the input tables to those subjects.
If a character string, it must be the name of a cohort in the cdm; in this case, the input tables are subset to subjects (subject_id) belonging to that cohort.
Use NULL to disable subsetting (default value).

dateRange

Details

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseRecordCount(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure"),
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

tableRecordCount(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseRecordCount(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure"),
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

tableRecordCount(result = result)

cdmDisconnect(cdm = cdm)

Summarise temporal trends in OMOP tables

Description

This function summarises temporal trends from OMOP CDM tables, considering only data within the observation period. It supports both event and episode tables and can report trends such as number of records, number of subjects, person-days, median age, and number of females.

Usage

summariseTrend(
  cdm,
  event = NULL,
  episode = NULL,
  output = "record",
  interval = "overall",
  ageGroup = NULL,
  sex = FALSE,
  inObservation = FALSE,
  dateRange = NULL
)
summariseTrend(
  cdm,
  event = NULL,
  episode = NULL,
  output = "record",
  interval = "overall",
  ageGroup = NULL,
  sex = FALSE,
  inObservation = FALSE,
  dateRange = NULL
)

Arguments

cdm

A cdm_reference object. Use CDMConnector to create a reference to a database or omock to create a reference to synthetic data.

event

A character vector of OMOP table names to treat as event tables (uses only start date).

episode

A character vector of OMOP table names to treat as episode tables (uses start and end date).

output

A character vector indicating what to summarise. Options include "record" (default), "person", "person-days", "age", "sex". If included, the number of person-days and end dates are computed only for episode tables.

interval

Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".

ageGroup

A list of age groups to stratify the results by. Each element represents a specific age range. You can give them specific names, e.g. ageGroup = list(children = c(0, 17), adult = c(18, Inf)).

sex

Logical; whether to stratify results by sex (TRUE) or not (FALSE).

inObservation

Logical. If TRUE, the results are stratified to indicate whether each record occurs within an observation period.

dateRange

A vector of two dates defining the desired study period. If dateRange is NULL, no restriction is applied.

Details

Event tables: Records are included if their start date falls within the study period. Each record contributes to the time interval containing the start date.
Episode tables: Records are included if their start or end date overlaps with the study period. Records are trimmed to the date range, and contribute to all overlapping time intervals between start and end dates.

Value

A summarised_result object with the results.

Examples


library(OmopSketch)
library(omock)
library(dplyr)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseTrend(
  cdm = cdm,
  event = c("condition_occurrence", "drug_exposure"),
  episode = "observation_period",
  output = "person",
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE,
  dateRange = as.Date(c("1950-01-01", "2010-12-31"))
)

plotTrend(result = result, facet = sex ~ omop_table, colour = c("age_group"))

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)
library(dplyr)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseTrend(
  cdm = cdm,
  event = c("condition_occurrence", "drug_exposure"),
  episode = "observation_period",
  output = "person",
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE,
  dateRange = as.Date(c("1950-01-01", "2010-12-31"))
)

plotTrend(result = result, facet = sex ~ omop_table, colour = c("age_group"))

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseClinicalRecord() output

Description

Create a visual table from a summariseClinicalRecord() output

Usage

tableClinicalRecords(
  result,
  header = "cdm_name",
  hide = c(omopgenerics::settingsColumns(result),
    omopgenerics::additionalColumns(result)),
  groupColumn = c("omop_table", omopgenerics::strataColumns(result)),
  type = NULL,
  style = NULL
)
tableClinicalRecords(
  result,
  header = "cdm_name",
  hide = c(omopgenerics::settingsColumns(result),
    omopgenerics::additionalColumns(result)),
  groupColumn = c("omop_table", omopgenerics::strataColumns(result)),
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summariseClinicalRecords()).

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.

hide

Columns to drop from the output table.

groupColumn

Columns to use as group labels, to see options use visOmopResults::tableColumns(result).

type

Character string specifying the desired output table format. See visOmopResults::tableType() for supported table types. If type = NULL, global options (set via visOmopResults::setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure"),
  recordsPerPerson = c("mean", "sd"),
  inObservation = TRUE,
  standardConcept = TRUE,
  sourceVocabulary = TRUE,
  domainId = TRUE,
  typeConcept = TRUE
)

summarisedResult |>
  suppress(minCellCount = 5) |>
  tableClinicalRecords()

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure"),
  recordsPerPerson = c("mean", "sd"),
  inObservation = TRUE,
  standardConcept = TRUE,
  sourceVocabulary = TRUE,
  domainId = TRUE,
  typeConcept = TRUE
)

summarisedResult |>
  suppress(minCellCount = 5) |>
  tableClinicalRecords()

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseConceptIdCounts() result

Description

Create a visual table from a summariseConceptIdCounts() result

Usage

tableConceptIdCounts(result, display = "overall", type = "reactable")
tableConceptIdCounts(result, display = "overall", type = "reactable")

Arguments

result

A summarised_result object (output of summariseConceptIdCounts()).

display

A character string indicating which subset of the data to display. Options are:

"overall": Show all source and standard concepts.
"standard": Show only standard concepts.
"source": Show only source codes.
"missing standard": Show only source codes that are missing a mapped standard concept.

type

Type of formatting output table, either "reactable" or "datatable".

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptIdCounts(cdm = cdm, omopTableName = "condition_occurrence")
tableConceptIdCounts(result = result, display = "standard")

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptIdCounts(cdm = cdm, omopTableName = "condition_occurrence")
tableConceptIdCounts(result = result, display = "standard")

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseInObservation() result

Description

Usage

tableInObservation(result, type = "gt")
tableInObservation(result, type = "gt")

Arguments

result

A summarised_result object (output of summariseInObservation()).

type

Type of formatting output table. See visOmopResults::tableType() for allowed options. Default is "gt".

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseInObservation(
  observationPeriod = cdm$observation_period,
  interval = "years",
  output = c("person-days", "record"),
  ageGroup = list("<=60" = c(0, 60), ">60" = c(61, Inf)),
  sex = TRUE
)

result |>
  tableInObservation()

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseInObservation(
  observationPeriod = cdm$observation_period,
  interval = "years",
  output = c("person-days", "record"),
  ageGroup = list("<=60" = c(0, 60), ">60" = c(61, Inf)),
  sex = TRUE
)

result |>
  tableInObservation()

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseMissingData() result

Description

Create a visual table from a summariseMissingData() result

Usage

tableMissingData(
  result,
  header = "cdm_name",
  hide = c("variable_name", omopgenerics::settingsColumns(result)),
  groupColumn = c("omop_table", omopgenerics::strataColumns(result)),
  type = NULL,
  style = NULL
)
tableMissingData(
  result,
  header = "cdm_name",
  hide = c("variable_name", omopgenerics::settingsColumns(result)),
  groupColumn = c("omop_table", omopgenerics::strataColumns(result)),
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summariseMissingData()).

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.

hide

Columns to drop from the output table.

groupColumn

Columns to use as group labels, to see options use visOmopResults::tableColumns(result).

type

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseMissingData(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "visit_occurrence")
)

tableMissingData(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseMissingData(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "visit_occurrence")
)

tableMissingData(result = result)

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseObservationPeriod() result

Description

Create a visual table from a summariseObservationPeriod() result

Usage

tableObservationPeriod(
  result,
  header = "cdm_name",
  hide = omopgenerics::settingsColumns(result),
  groupColumn = omopgenerics::strataColumns(result),
  type = NULL,
  style = NULL
)
tableObservationPeriod(
  result,
  header = "cdm_name",
  hide = omopgenerics::settingsColumns(result),
  groupColumn = omopgenerics::strataColumns(result),
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summariseObservationPeriod()).

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.

hide

Columns to drop from the output table.

groupColumn

Columns to use as group labels, to see options use visOmopResults::tableColumns(result).

type

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseObservationPeriod(cdm = cdm)

tableObservationPeriod(result = result)

plotObservationPeriod(
  result = result,
  variableName = "Duration in days",
  plotType = "boxplot"
)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseObservationPeriod(cdm = cdm)

tableObservationPeriod(result = result)

plotObservationPeriod(
  result = result,
  variableName = "Duration in days",
  plotType = "boxplot"
)

cdmDisconnect(cdm = cdm)

Create a visual table from a summarise_omop_snapshot result

Description

Create a visual table from a summarise_omop_snapshot result

Usage

tableOmopSnapshot(
  result,
  header = "cdm_name",
  hide = "variable_level",
  groupColumn = "variable_name",
  type = NULL,
  style = NULL
)
tableOmopSnapshot(
  result,
  header = "cdm_name",
  hide = "variable_level",
  groupColumn = "variable_name",
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summariseOmopSnapshot() ).

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.

hide

Columns to drop from the output table.

groupColumn

Columns to use as group labels, to see options use visOmopResults::tableColumns(result).

type

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

cdmDisconnect(cdm = cdm)

Visualise the results of `summarisePerson()` into a table

Description

Visualise the results of summarisePerson() into a table

Visualise the output of summarisePerson()

Usage

tablePerson(
  result,
  header = "cdm_name",
  hide = omopgenerics::settingsColumns(result),
  groupColumn = character(),
  type = NULL,
  style = NULL
)

tablePerson(
  result,
  header = "cdm_name",
  hide = omopgenerics::settingsColumns(result),
  groupColumn = character(),
  type = NULL,
  style = NULL
)
tablePerson(
  result,
  header = "cdm_name",
  hide = omopgenerics::settingsColumns(result),
  groupColumn = character(),
  type = NULL,
  style = NULL
)

tablePerson(
  result,
  header = "cdm_name",
  hide = omopgenerics::settingsColumns(result),
  groupColumn = character(),
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summarisePerson()).

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.

hide

Columns to drop from the output table.

groupColumn

Columns to use as group labels, to see options use visOmopResults::tableColumns(result).

type

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)



library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)



library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseRecordCount() result

Description

Usage

tableRecordCount(result, type = "gt")
tableRecordCount(result, type = "gt")

Arguments

result

A summarised_result object (output of summariseRecordCount() ).

type

Type of formatting output table. See visOmopResults::tableType() for allowed options. Default is "gt".

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

summarisedResult <- summariseRecordCount(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure"),
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

tableRecordCount(result = summarisedResult)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

summarisedResult <- summariseRecordCount(
  cdm = cdm,
  omopTableName = c("condition_occurrence", "drug_exposure"),
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

tableRecordCount(result = summarisedResult)

cdmDisconnect(cdm = cdm)

Create a visual table of the most common concepts from `summariseConceptIdCounts()` output

Description

This function takes a summarised_result object and generates a formatted table highlighting the most frequent concepts.

Usage

tableTopConceptCounts(
  result,
  top = 10,
  countBy = NULL,
  type = NULL,
  style = NULL
)
tableTopConceptCounts(
  result,
  top = 10,
  countBy = NULL,
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summariseConceptIdCounts()).

top

Integer. The number of top concepts to display. Defaults to 10.

countBy

Either 'person' or 'record'. If NULL whatever is in the data is used.

type

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptIdCounts(cdm = cdm, omopTableName = "condition_occurrence")

tableTopConceptCounts(result = result, top = 5)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseConceptIdCounts(cdm = cdm, omopTableName = "condition_occurrence")

tableTopConceptCounts(result = result, top = 5)

cdmDisconnect(cdm = cdm)

Create a visual table from a summariseTrend() result

Description

Create a visual table from a summariseTrend() result

Usage

tableTrend(
  result,
  header = "cdm_name",
  hide = "variable_level",
  groupColumn = c("type", "omop_table"),
  type = NULL,
  style = NULL
)
tableTrend(
  result,
  header = "cdm_name",
  hide = "variable_level",
  groupColumn = c("type", "omop_table"),
  type = NULL,
  style = NULL
)

Arguments

result

A summarised_result object (output of summariseTrend()).

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.

hide

Columns to drop from the output table.

groupColumn

Columns to use as group labels, to see options use visOmopResults::tableColumns(result).

type

Type of formatting output table between gt, datatable and reactable. Default is "gt".

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (seevisOmopResults::setGlobalTableOptions()) or a _brand.yml file (if found); otherwise, the default style is applied.

Value

A formatted table visualisation.

Examples


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseTrend(
  cdm = cdm,
  episode = "observation_period",
  event = c("drug_exposure", "condition_occurrence"),
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

tableTrend(result = result)

cdmDisconnect(cdm = cdm)


library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

result <- summariseTrend(
  cdm = cdm,
  episode = "observation_period",
  event = c("drug_exposure", "condition_occurrence"),
  interval = "years",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)

tableTrend(result = result)

cdmDisconnect(cdm = cdm)

Package 'OmopSketch'

Help Index

Tables in the cdm_reference that contain clinical information

Description

Usage

Value

Examples

Summarise Database Characteristics for OMOP CDM

Description

Usage

Arguments

Value

Examples

Creates a mock database to test OmopSketch package

Description

Usage

Arguments

Value

Plot the concept counts of a summariseConceptSetCounts output

Description

Usage

Arguments

Value

Examples

Create a ggplot2 plot from the output of summariseInObservation()

Description

Usage

Arguments

Value

Examples

Create a plot from the output of summariseObservationPeriod()

Description

Usage

Arguments

Value

Examples

Visualise the output of summarisePerson()

Description

Usage

Arguments

Value

Examples

Create a ggplot of the records' count trend

Description

Usage

Arguments

Value

Examples

Create a ggplot2 plot from the output of summariseTrend()

Description

Usage

Arguments

Value

Examples

Generate an interactive Shiny application that visualises the results obtained from the databaseCharacteristics() function

Description

Usage

Arguments

Value

Examples

Summarise an omop table from a cdm object

Description

Usage

Arguments

Value

Examples

Summarise concept counts in patient-level data

Description

Usage

Arguments

Details

Value

Summarise concept use in patient-level data

Description

Usage

Arguments

Value

Examples

Summarise concept counts in patient-level data

Description

Visualise the output of `summarisePerson()`

Generate an interactive Shiny application that visualises the results obtained from the `databaseCharacteristics()` function