Title: Characterise Tables of an OMOP Common Data Model Instance
Version: 0.5.1
Maintainer: Cecilia Campanile <cecilia.campanile@ndorms.ox.ac.uk>
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.
License: Apache License (≥ 2)
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: CodelistGenerator, CohortCharacteristics, DBI, duckdb, DT, flextable, gt, here, knitr, lubridate, odbc, OmopViewer (≥ 0.3.0), sortable, reactable, remotes, rmarkdown, RPostgres, shinyWidgets, testthat (≥ 3.0.0), withr, omock (≥ 0.3.0), covr, ggplot2, visOmopResults (≥ 0.5.0), devtools
Config/testthat/edition: 3
Config/testthat/parallel: true
Imports: CDMConnector (≥ 1.3.0), cli, clock, CohortConstructor (≥ 0.3.1), dplyr, glue, lifecycle, omopgenerics (≥ 0.4.1), PatientProfiles (≥ 1.3.1), purrr, rlang, stringr, tibble, tidyr
Depends: R (≥ 4.1)
URL: https://OHDSI.github.io/OmopSketch/
BugReports: https://github.com/OHDSI/OmopSketch/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-06-19 19:09:02 UTC; ccampanile
Author: Marta Alcalde-Herraiz ORCID iD [aut], Kim Lopez-Guell ORCID iD [aut], Elin Rowlands ORCID iD [aut], Cecilia Campanile ORCID iD [aut, cre], Edward Burn ORCID iD [aut], Martí Català ORCID iD [aut]
Repository: CRAN
Date/Publication: 2025-06-19 19:50:06 UTC

OmopSketch: Characterise Tables of an OMOP Common Data Model Instance

Description

logo

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.

Author(s)

Maintainer: Cecilia Campanile cecilia.campanile@ndorms.ox.ac.uk (ORCID)

Authors:

See Also

Useful links:

Summarise Database Characteristics for OMOP CDM

Description

Summarise Database Characteristics for OMOP CDM

Usage

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

Arguments

cdm

A cdm_reference object representing the Common Data Model (CDM) reference.

omopTableName

A character vector specifying the OMOP tables from the CDM to include in the analysis. If "person" is present, it will only be used for missing value summarisation.

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.

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 containing the results of the characterisation.

Examples



cdm <- mockOmopSketch(numberIndividuals = 100)

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

PatientProfiles::mockDisconnect(cdm)

Helper for consistent documentation of dateRange.

Description

Helper for consistent documentation of dateRange.

Arguments

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.

Helper for consistent documentation of interval.

Description

Helper for consistent documentation of interval.

Arguments

interval

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

Creates a mock database to test OmopSketch package.

Description

Creates a mock database to test OmopSketch package.

Usage

mockOmopSketch(
  con = NULL,
  writeSchema = NULL,
  numberIndividuals = 100,
  seed = NULL
)

Arguments

con

A DBI connection to create the cdm mock object. By default, the connection would be a 'duckdb' one.

writeSchema

Name of an schema of the DBI connection with writing permissions.

numberIndividuals

Number of individuals to create in the cdm reference object.

seed

An optional integer used to set the seed for random number generation, ensuring reproducibility of the generated data. If provided, this seed allows the function to produce consistent results each time it is run with the same inputs. If 'NULL', the seed is not set, which can lead to different outputs on each run.

Value

A mock cdm_reference object.

Examples


mockOmopSketch(numberIndividuals = 100)

Plot the concept counts of a summariseConceptSetCounts output.

Description

Plot the concept counts of a summariseConceptSetCounts output.

Usage

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

Arguments

result

A summarised_result object (output of summariseConceptSetCounts).

facet

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 ggplot2 object showing the concept counts.

Examples


library(dplyr)

cdm <- mockOmopSketch()

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")

PatientProfiles::mockDisconnect(cdm)

Create a ggplot2 plot from the output of summariseInObservation().

Description

Create a ggplot2 plot from the output of summariseInObservation().

Usage

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

Arguments

result

A summarised_result object (output of summariseInObservation).

facet

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 ggplot showing the table counts

Examples


library(dplyr)

cdm <- mockOmopSketch()

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 == "Number person-days") |>
  plotInObservation(facet = "sex", colour = "age_group")

PatientProfiles::mockDisconnect(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
)

Arguments

result

A summarised_result object.

variableName

The variable to plot it can be: "number subjects", "records per person", "duration" or "days to next observation period".

plotType

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

facet

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

colour

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

Value

A ggplot2 object.

Examples


cdm <- mockOmopSketch(numberIndividuals = 100)

result <- summariseObservationPeriod(observationPeriod = cdm$observation_period)

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

PatientProfiles::mockDisconnect(cdm)

Create a ggplot of the records' count trend.

Description

Create a ggplot of the records' count trend.

Usage

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

Arguments

result

Output from summariseRecordCount().

facet

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 ggplot showing the table counts

Examples


cdm <- mockOmopSketch()

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 ~ .)

PatientProfiles::mockDisconnect(cdm = cdm)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

omopgenerics

bind, exportSummarisedResult, importSummarisedResult, settings, suppress

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,
  title = "Database characterisation",
  logo = "ohdsi",
  theme = "bslib::bs_theme(bootswatch = 'flatly')"
)

Arguments

result

A summarised_result object containing the results from the databaseCharacteristics() function. This object should include summaries of various OMOP CDM tables, such as population characteristics, clinical records, missing data, and more

directory

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

title

Title of the shiny. Default is "Characterisation"

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. Default is "bslib::bs_theme(bootswatch = 'flatly')" to use the Flatly theme from the Bootswatch collection. You can customise this to use other themes.

Value

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

Examples

## Not run: 

library(OmopSketch)
cdm <- mockOmopSketch()
res <- databaseCharacteristics(cdm = cdm)
shinyCharacteristics(result = res, directory = here::here())

## End(Not run)

Summarise an omop table from a cdm object. You will obtain information related to the number of records, number of subjects, whether the records are in observation, number of present domains and number of present concepts.

Description

Summarise an omop table from a cdm object. You will obtain information related to the number of records, number of subjects, whether the records are in observation, number of present domains and number of present concepts.

Usage

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

Arguments

cdm

A cdm_reference object.

omopTableName

A character vector of the names of the tables to summarise in the cdm object.

recordsPerPerson

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

inObservation

Boolean variable. Whether to include the percentage of records in observation.

standardConcept

Boolean variable. Whether to summarise standard concept information.

sourceVocabulary

Boolean variable. Whether to summarise source vocabulary information.

domainId

Boolean variable. Whether to summarise domain id of standard concept id information.

typeConcept

Boolean variable. Whether to summarise type concept id field information.

sex

Boolean variable. Whether to stratify by sex (TRUE) or not (FALSE).

ageGroup

A list of age groups to stratify results by.

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.

Value

A summarised_result object.

Examples


cdm <- mockOmopSketch()

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

summarisedResult

PatientProfiles::mockDisconnect(cdm = cdm)

Summarise concept counts in patient-level data. Only concepts recorded during observation period are counted.

Description

[Deprecated]

Usage

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

Arguments

cdm

A cdm object

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

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

ageGroup

A list of ageGroup vectors of length two. Code use will be thus summarised by age groups.

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.

Value

A summarised_result object with results overall and, if specified, by strata.

Summarise concept use in patient-level data. Only concepts recorded during observation period are counted.

Description

Summarise concept use in patient-level data. Only concepts recorded during observation period are counted.

Usage

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

Arguments

cdm

A cdm object

omopTableName

A character vector of the names of the tables to summarise in the cdm object.

countBy

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

year

deprecated

interval

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

sex

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

ageGroup

A list of ageGroup vectors of length two. Code use will be thus summarised by age groups.

sample

An integer to sample the tables to only that number of records. If NULL no sample is done.

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.

Value

A summarised_result object with results overall and, if specified, by strata.

Examples


library(OmopSketch)
library(CDMConnector)
library(duckdb)

requireEunomia()
con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")

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

Summarise concept counts in patient-level data. Only concepts recorded during observation period are counted.

Description

Summarise concept counts in patient-level data. 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
)

Arguments

cdm

A cdm object

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

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

ageGroup

A list of ageGroup vectors of length two. Code use will be thus summarised by age groups.

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.

Value

A summarised_result object with results overall and, if specified, by strata.

Examples


library(OmopSketch)

cdm <- mockOmopSketch()

cs <- list(sinusitis = c(4283893, 257012, 40481087, 4294548))

results <- summariseConceptSetCounts(cdm, conceptSet = cs)

results

PatientProfiles::mockDisconnect(cdm)

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

Description

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

Usage

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 results by.

sex

Boolean variable. Whether to stratify by sex (TRUE) or not (FALSE). For output = "sex" this stratification is not applied.

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.

Value

A summarised_result object.

Examples


library(dplyr, warn.conflicts = FALSE)

cdm <- mockOmopSketch()

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

result |>
  glimpse()

PatientProfiles::mockDisconnect(cdm)

Summarise missing data in omop tables

Description

Summarise missing data in omop tables

Usage

summariseMissingData(
  cdm,
  omopTableName,
  col = NULL,
  sex = FALSE,
  year = lifecycle::deprecated(),
  interval = "overall",
  ageGroup = NULL,
  sample = 1e+06,
  dateRange = NULL
)

Arguments

cdm

A cdm object

omopTableName

A character vector of the names of the tables to summarise in the cdm object.

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

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

year

deprecated

interval

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

ageGroup

A list of ageGroup vectors of length two. Code use will be thus summarised by age groups.

sample

An integer to sample the table to only that number of records. If NULL no sample is done.

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.

Value

A summarised_result object with results overall and, if specified, by strata.

Examples


cdm <- mockOmopSketch(numberIndividuals = 100)

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

PatientProfiles::mockDisconnect(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(
  observationPeriod,
  estimates = c("mean", "sd", "min", "q05", "q25", "median", "q75", "q95", "max",
    "density"),
  byOrdinal = TRUE,
  ageGroup = NULL,
  sex = FALSE,
  dateRange = NULL
)

Arguments

observationPeriod

observation_period omop table.

estimates

Estimates to summarise the variables of interest ( ⁠records per person⁠, ⁠duration in days⁠ and ⁠days to next observation period⁠).

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 results by.

sex

Boolean variable. Whether to stratify by sex (TRUE) or not (FALSE).

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.

Value

A summarised_result object with the summarised data.

Examples


library(dplyr, warn.conflicts = FALSE)

cdm <- mockOmopSketch(numberIndividuals = 100)

result <- summariseObservationPeriod(observationPeriod = cdm$observation_period)

result |>
  glimpse()

PatientProfiles::mockDisconnect(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)

Arguments

cdm

A cdm_reference object.

Value

A summarised_result object.

Examples


cdm <- mockOmopSketch(numberIndividuals = 10)

summariseOmopSnapshot(cdm = cdm)

Summarise record counts of an omop_table using a specific time interval. Only records that fall within the observation period are considered.

Description

Summarise record counts of an omop_table using a specific time interval. Only records that fall within the observation period are considered.

Usage

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

Arguments

cdm

A cdm_reference object.

omopTableName

A character vector of omop tables from the cdm.

interval

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

ageGroup

A list of age groups to stratify results by.

sex

Whether to stratify by sex (TRUE) or not (FALSE).

sample

An integer to sample the tables to only that number of records. If NULL no sample is done.

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.

Value

A summarised_result object.

Examples


library(dplyr, warn.conflicts = FALSE)

cdm <- mockOmopSketch()

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

summarisedResult |>
  glimpse()

PatientProfiles::mockDisconnect(cdm = cdm)

Create a visual table from a summariseClinicalRecord() output.

Description

Create a visual table from a summariseClinicalRecord() output.

Usage

tableClinicalRecords(result, type = "gt")

Arguments

result

Output from summariseClinicalRecords().

type

Type of formatting output table. See visOmopResults::tableType() for allowed options.

Value

A formatted table object with the summarised data.

Examples


cdm <- mockOmopSketch()

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()

PatientProfiles::mockDisconnect(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")

Arguments

result

A summarised_result object.

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 reactable or datatable object with the summarised data.

Examples


library(OmopSketch)
library(CDMConnector)
library(duckdb)

requireEunomia()
con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")

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

Create a visual table from a summariseInObservation() result.

Description

Create a visual table from a summariseInObservation() result.

Usage

tableInObservation(result, type = "gt")

Arguments

result

A summarised_result object.

type

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

Value

A formatted table object with the summarised data.

Examples


library(dplyr, warn.conflicts = FALSE)

cdm <- mockOmopSketch()

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

result |>
  tableInObservation()

PatientProfiles::mockDisconnect(cdm)

Create a visual table from a summariseMissingData() result.

Description

Create a visual table from a summariseMissingData() result.

Usage

tableMissingData(result, type = "gt")

Arguments

result

A summarised_result object.

type

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

Value

A formatted table object with the summarised data.

Examples


cdm <- mockOmopSketch(numberIndividuals = 100)

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

tableMissingData(result = result)

PatientProfiles::mockDisconnect(cdm = cdm)

Create a visual table from a summariseObservationPeriod() result.

Description

Create a visual table from a summariseObservationPeriod() result.

Usage

tableObservationPeriod(result, type = "gt")

Arguments

result

A summarised_result object.

type

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

Value

A formatted table object with the summarised data.

Examples


cdm <- mockOmopSketch(numberIndividuals = 100)

result <- summariseObservationPeriod(observationPeriod = cdm$observation_period)

tableObservationPeriod(result = result)

PatientProfiles::mockDisconnect(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, type = "gt")

Arguments

result

Output from summariseOmopSnapshot().

type

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

Value

A formatted table object with the summarised data.

Examples


cdm <- mockOmopSketch(numberIndividuals = 10)

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

PatientProfiles::mockDisconnect(cdm)

Create a visual table from a summariseRecordCount() result.

Description

Create a visual table from a summariseRecordCount() result.

Usage

tableRecordCount(result, type = "gt")

Arguments

result

A summarised_result object.

type

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

Value

A formatted table object with the summarised data.

Examples


library(dplyr, warn.conflicts = FALSE)

cdm <- mockOmopSketch()

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)

PatientProfiles::mockDisconnect(cdm = cdm)

Create a visual table of the most common concepts from summariseConceptIdCounts() output. This function takes a summarised_result object and generates a formatted table highlighting the most frequent concepts.

Description

Create a visual table of the most common concepts from summariseConceptIdCounts() output. 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 = "gt")

Arguments

result

A summarised_result object, typically returned by 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

Character. The output table format. Defaults to "gt". Use visOmopResults::tableType() to see all supported formats.

Value

A formatted table object displaying the top concepts from the summarised data.

Examples


library(OmopSketch)
library(CDMConnector)
library(duckdb)

requireEunomia()
con <- dbConnect(drv = duckdb(dbdir = eunomiaDir()))
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")

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

tableTopConceptCounts(result = result, top = 5)

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