OmopSketch

https://ohdsi.github.io/OmopSketch/
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OmopSketch is an R package for characterising and visualising OMOP Common Data Model instances to...
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### Install OmopSketch

Source: https://ohdsi.github.io/OmopSketch/index.html

Installation instructions for the package from CRAN or GitHub.

```R
install.packages("OmopSketch")
```

```R
# install.packages("remotes")
remotes::install_github("OHDSI/OmopSketch")
```

--------------------------------

### Example usage of summariseOmopSnapshot

Source: https://ohdsi.github.io/OmopSketch/reference/summariseOmopSnapshot.html

Full example demonstrating the creation of a mock CDM, summarising it, and displaying the result.

```R
# \donttest{
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Summarise temporal trends example

Source: https://ohdsi.github.io/OmopSketch/reference/summariseTrend.html

Example demonstrating the setup of a mock CDM and the execution of a trend summary with stratification by age and sex.

```R
# \donttest{
library(OmopSketch)
library(omock)
library(dplyr)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into 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"))
#> Warning: `type` not included in facet or colour, but have multiple values.


cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Summarise concept counts example

Source: https://ohdsi.github.io/OmopSketch/reference/summariseConceptIdCounts.html

Example demonstrating how to initialise a mock CDM, perform concept count summarisation, and display the results.

```R
# \donttest{
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

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

tableConceptIdCounts(result = result)

cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Example usage of tableTopConceptCounts

Source: https://ohdsi.github.io/OmopSketch/reference/tableTopConceptCounts.html

Demonstrates how to generate a table of the top 5 concepts from a mock CDM dataset.

```R
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

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

tableTopConceptCounts(result = result, top = 5)

cdmDisconnect(cdm = cdm)
```

--------------------------------

### Example of tableRecordCount

Source: https://ohdsi.github.io/OmopSketch/reference/tableRecordCount.html

Demonstrates creating a record count summary from a mock CDM and visualizing it with tableRecordCount.

```R
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into 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)
```

--------------------------------

### Generate a visual snapshot table

Source: https://ohdsi.github.io/OmopSketch/reference/tableOmopSnapshot.html

This example demonstrates how to create a mock CDM, generate a snapshot summary, and render it as a visual table.

```R
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

result <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = result)

cdmDisconnect(cdm = cdm)
```

--------------------------------

### Example usage of plotPerson

Source: https://ohdsi.github.io/OmopSketch/reference/plotPerson.html

Example workflow demonstrating the creation of a mock CDM, summarising person data, and preparing for visualisation.

```R
# \donttest{
library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)
#> ! The following column types were changed:
#> • variable_name: from integer to character

cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Generate and display a trend table

Source: https://ohdsi.github.io/OmopSketch/reference/tableTrend.html

This example demonstrates how to summarize trends in a CDM and display the result using tableTrend.

```R
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)
```

--------------------------------

### Example usage of plotRecordCount

Source: https://ohdsi.github.io/OmopSketch/reference/plotRecordCount.html

Demonstrates creating a mock CDM, summarizing record counts, and generating a plot.

```R
# \donttest{
library(omock)
library(OmopSketch)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

summarisedResult <- summariseRecordCount(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  ageGroup = list("<=20" = c(0, 20), ">20" = c(21, Inf)),
  sex = TRUE
)
#> Warning: `summariseRecordCount()` was deprecated in OmopSketch 1.0.0.
#> ℹ Please use `summariseTrend()` instead.

plotRecordCount(
  result = summarisedResult,
  colour = "age_group",
  facet = sex ~ .
)
#> Warning: `plotRecordCount()` was deprecated in OmopSketch 1.0.0.
#> ℹ Please use `plotTrend()` instead.


cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Generate and visualize observation period summary

Source: https://ohdsi.github.io/OmopSketch/reference/tableObservationPeriod.html

Example demonstrating how to create a mock CDM, summarize observation periods, and generate a table and plot.

```R
# \donttest{
library(OmopSketch)
library(dplyr, warn.conflicts = FALSE)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

result <- summariseObservationPeriod(cdm = cdm)
#> Warning: ! There are 2649 individuals not included in the person table.

tableObservationPeriod(result = result)

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


cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Example usage of summariseObservationPeriod

Source: https://ohdsi.github.io/OmopSketch/reference/summariseObservationPeriod.html

A complete workflow demonstrating how to initialize a mock CDM, generate a summary, display the results in a table, and visualize the duration.

```R
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)
```

--------------------------------

### Example usage of plotConceptSetCounts

Source: https://ohdsi.github.io/OmopSketch/reference/plotConceptSetCounts.html

Demonstrates creating a mock CDM, summarizing concept set counts, and plotting the results.

```R
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

result <- summariseConceptSetCounts(
  cdm = cdm,
  conceptSet = list(
    "asthma" = c(4051466, 317009),
    "rhinitis" = c(4280726, 4048171, 40486433)
  )
)
#> Warning: `summariseConceptSetCounts()` was deprecated in OmopSketch 0.5.0.
#> Warning: ! `codelist` cast to integers.
#> ℹ Searching concepts from domain condition in condition_occurrence.
#> ℹ Counting concepts

result |>
  filter(variable_name == "Number subjects") |>
  plotConceptSetCounts(
    facet = "codelist_name",
    colour = "standard_concept_name"
  )
#> Warning: `plotConceptSetCounts()` was deprecated in OmopSketch 0.5.0.


cdmDisconnect(cdm = cdm)
```

--------------------------------

### Example usage of tableInObservation

Source: https://ohdsi.github.io/OmopSketch/reference/tableInObservation.html

Demonstrates creating a summary object and passing it to tableInObservation. Note that this function is deprecated.

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into 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()
#> Warning: `tableInObservation()` was deprecated in OmopSketch 1.0.0.
#> ℹ Please use `tableTrend()` instead.
```

--------------------------------

### Visualize trends with plotTrend

Source: https://ohdsi.github.io/OmopSketch/reference/plotTrend.html

Example demonstrating the creation of a trend plot using a mock CDM dataset.

```R
library(dplyr)
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into 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)
```

--------------------------------

### Example: Visualizing Concept ID Counts

Source: https://ohdsi.github.io/OmopSketch/reference/tableConceptIdCounts.html

Demonstrates generating a summary of concept ID counts from a mock CDM and displaying the standard concepts in a table.

```R
# \donttest{
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

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

Database
OMOP table
Standard concept name
Standard concept id
Standard vocabulary
N records
GiBleed
condition_occurrence
Acute allergic reaction
4084167
Snomed
161
GiBleed
condition_occurrence
Acute bacterial sinusitis
4294548
Snomed
939
GiBleed
condition_occurrence
Acute bronchitis
260139
Snomed
8184
GiBleed
condition_occurrence
Acute cholecystitis
198809
Snomed
35
GiBleed
condition_occurrence
Acute viral pharyngitis
4112343
Snomed
10217
GiBleed
condition_occurrence
Alzheimer's disease
378419
Snomed
117
GiBleed
condition_occurrence
Anemia
439777
Snomed
102
GiBleed
condition_occurrence
Angiodysplasia of stomach
4310024
Snomed
388
GiBleed
condition_occurrence
Appendicitis
440448
Snomed
157
GiBleed
condition_occurrence
Asthma
317009
Snomed
5
1–10 of 80 rows
Show 102550100
Previous12345...8Next


cdmDisconnect(cdm = cdm)
# }
```

--------------------------------

### Example usage of plotObservationPeriod

Source: https://ohdsi.github.io/OmopSketch/reference/plotObservationPeriod.html

Demonstrates creating a boxplot of observation period duration using a mock CDM reference.

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

result <- summariseObservationPeriod(cdm = cdm)
#> Warning: ! There are 2649 individuals not included in the person table.

tableObservationPeriod(result = result)

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


cdmDisconnect(cdm = cdm)
```

--------------------------------

### Generate a Shiny application for database characteristics

Source: https://ohdsi.github.io/OmopSketch/reference/shinyCharacteristics.html

Example usage of shinyCharacteristics to create an interactive app from a mock CDM dataset.

```R
if (FALSE) { # \dontrun{
library(OmopSketch)
library(omock)
library(here)

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

res <- databaseCharacteristics(cdm = cdm)

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

cdmDisconnect(cdm = cdm)
} # }
```

--------------------------------

### Execute Database Characterisation

Source: https://ohdsi.github.io/OmopSketch/reference/databaseCharacteristics.html

Demonstrates how to initialize a mock CDM, run the characterisation, and export results to a Shiny application.

```R
if (FALSE) { # \dontrun{
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)
} # }
```

--------------------------------

### Create a mock CDM for OmopSketch

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_observation_period.html

Load necessary libraries and initialize a mock CDM reference using the omock package.

```R
library(dplyr)
#>
#> Attaching package: 'dplyr'
#>
#> The following objects are masked from 'package:stats':
#>
#>     filter, lag
#>
#> The following objects are masked from 'package:base':
#>
#>     intersect, setdiff, setequal, union
library(OmopSketch)
library(omock)

# Connect to mock database
cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.
```

--------------------------------

### Load OmopSketch and dplyr

Source: https://ohdsi.github.io/OmopSketch/index.html

Initialize the required libraries for data manipulation and CDM sketching.

```R
library(OmopSketch)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
```

--------------------------------

### Create a mock CDM for database characterisation

Source: https://ohdsi.github.io/OmopSketch/articles/database_characteristics.html

Initializes a mock CDM reference using the GiBleed dataset and the omock package for testing purposes.

```R
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#>     filter, lag
#> The following objects are masked from 'package:base':
#>
#>     intersect, setdiff, setequal, union
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

cdm
#>
#> ── # OMOP CDM reference (duckdb) of GiBleed ────────────────────────────────────
#> • omop tables: care_site, cdm_source, concept, concept_ancestor, concept_class,
#> concept_relationship, concept_synonym, condition_era, condition_occurrence,
#> cost, death, device_exposure, domain, dose_era, drug_era, drug_exposure,
#> drug_strength, fact_relationship, location, measurement, metadata, note,
#> note_nlp, observation, observation_period, payer_plan_period, person,
#> procedure_occurrence, provider, relationship, source_to_concept_map, specimen,
#> visit_detail, visit_occurrence, vocabulary
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -
```

--------------------------------

### Restrict analysis by date range

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_trend.html

Use the dateRange argument to filter records based on start dates or overlap periods.

```R
summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "drug_exposure",
  dateRange = as.Date(c("1990-01-01", "2010-01-01"))
)
```

--------------------------------

### Create and display a CDM snapshot

Source: https://ohdsi.github.io/OmopSketch/index.html

Capture metadata about the CDM version and data release, then display it as a flextable.

```R
snapshot <- summariseOmopSnapshot(cdm = cdm)

tableOmopSnapshot(result = snapshot, type = "flextable")
```

--------------------------------

### Summarise missing data in CDM tables

Source: https://ohdsi.github.io/OmopSketch/reference/summariseMissingData.html

Initializes a mock CDM, calculates missing data statistics for specific tables, and displays the results.

```R
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 record trends for episode tables

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_trend.html

Splits record counts into start and end date measures when using episode tables like observation_period.

```R
summarisedResult <- summariseTrend(
  cdm = cdm,
  episode = "observation_period",
  output = "record",
  interval = "years"
)

summarisedResult |>
  filter(grepl("Number of records", variable_name)) |>
  select(variable_name, additional_level, estimate_name, estimate_value)
#> # A tibble: 450 × 4
#>    variable_name                 additional_level   estimate_name estimate_value
#>    <chr>                         <chr>              <chr>         <chr>         
#>  1 Number of records: start_date 1908-01-01 to 190… count         2             
#>  2 Number of records: end_date   1908-01-01 to 190… count         0             
#>  3 Number of records: start_date 1908-01-01 to 190… percentage    0.04          
#>  4 Number of records: end_date   1908-01-01 to 190… percentage    0.00          
#>  5 Number of records: start_date 1909-01-01 to 190… count         22            
#>  6 Number of records: end_date   1909-01-01 to 190… count         0             
#>  7 Number of records: start_date 1909-01-01 to 190… percentage    0.41          
#>  8 Number of records: end_date   1909-01-01 to 190… percentage    0.00          
#>  9 Number of records: start_date 1910-01-01 to 191… count         28            
#> 10 Number of records: end_date   1910-01-01 to 191… count         0             
#> # ℹ 440 more rows
```

--------------------------------

### Generate a Shiny application for characterisation results

Source: https://ohdsi.github.io/OmopSketch/articles/database_characteristics.html

Creates a Shiny application in the specified directory to browse and visualise characterisation results.

```R
shinyCharacteristics(result = result, directory = "path/to/your/shiny")
```

```R
shinyCharacteristics(
  result = result, 
  directory = "path/to/my/shiny",
  title = "Characterisation of my data",
  logo = "path/to/my/logo.svg",
  theme = "scarlet", 
  background = "path/to/my/background.md"
)
```

--------------------------------

### Apply Apache License Boilerplate

Source: https://ohdsi.github.io/OmopSketch/LICENSE.html

Use this boilerplate notice to apply the Apache License to your work, replacing bracketed fields with your specific information.

```text
Copyright [yyyy] [name of copyright owner]

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

  http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```

--------------------------------

### mockOmopSketch()

Source: https://ohdsi.github.io/OmopSketch/reference/mockOmopSketch.html

Creates a mock cdm_reference object for testing the OmopSketch package.

```APIDOC
## mockOmopSketch()

### Description
Creates a mock cdm_reference object to test the OmopSketch package.

### Usage
```R
mockOmopSketch(numberIndividuals = 100)
```

### Arguments
- **numberIndividuals** (numeric) - Optional - Number of individuals to create in the cdm reference object. Defaults to 100.

### Value
A mock cdm_reference object.
```

--------------------------------

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

Source: https://ohdsi.github.io/OmopSketch/reference/tableOmopSnapshot.html

Creates a visual table from a summarise_omop_snapshot result.

```APIDOC
## tableOmopSnapshot

### Description
Creates a visual table from a summarised_result object (output of summariseOmopSnapshot()).

### Arguments
- **result** (summarised_result) - Required - A summarised_result object.
- **header** (vector) - Optional - A vector specifying the elements to include in the header.
- **hide** (vector) - Optional - Columns to drop from the output table.
- **groupColumn** (vector) - Optional - Columns to use as group labels.
- **type** (character) - Optional - Character string specifying the desired output table format.
- **style** (string/list) - Optional - Defines the visual formatting of the table (pre-defined style, YAML file path, or list of custom R code).

### Value
A formatted table visualisation.
```

--------------------------------

### Create a cdm_reference object

Source: https://ohdsi.github.io/OmopSketch/index.html

Initialize a cdm_reference object using the omock package for testing purposes.

```R
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Loading bundled GiBleed tables from package data.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.
cdm
#> 
#> ── # OMOP CDM reference (duckdb) of GiBleed ────────────────────────────────────
#> • omop tables: care_site, cdm_source, concept, concept_ancestor, concept_class,
#> concept_relationship, concept_synonym, condition_era, condition_occurrence,
#> cost, death, device_exposure, domain, dose_era, drug_era, drug_exposure,
#> drug_strength, fact_relationship, location, measurement, metadata, note,
#> note_nlp, observation, observation_period, payer_plan_period, person,
#> procedure_occurrence, provider, relationship, source_to_concept_map, specimen,
#> visit_detail, visit_occurrence, vocabulary
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -
```

--------------------------------

### Summarise observation periods with default settings

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_observation_period.html

Generates a summary of the observation_period table using the default configuration.

```R
summarisedResult <- summariseObservationPeriod(cdm = cdm)

summarisedResult
```

--------------------------------

### tableTrend(result, header, hide, groupColumn, type, style)

Source: https://ohdsi.github.io/OmopSketch/reference/tableTrend.html

Creates a visual table from a summarised_result object generated by summariseTrend().

```APIDOC
## tableTrend(result, header, hide, groupColumn, type, style)

### Description
Creates a visual table from a summariseTrend() result. This function formats the summarized data into a presentable table using specified headers, grouping, and styling options.

### Arguments
- **result** (summarised_result) - Required - A summarised_result object (output of summariseTrend()).
- **header** (vector) - Optional - A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header.
- **hide** (vector) - Optional - Columns to drop from the output table.
- **groupColumn** (vector) - Optional - Columns to use as group labels.
- **type** (string) - Optional - Type of formatting output table between 'gt', 'datatable' and 'reactable'. Default is 'gt'.
- **style** (string/list) - Optional - Defines the visual formatting of the table. Can be a pre-defined style name, a YAML file path, or a list of custom R code.

### Value
A formatted table visualisation.
```

--------------------------------

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

Source: https://ohdsi.github.io/OmopSketch/reference/tableTopConceptCounts.html

Generates a formatted table visualization of the most common concepts from a summarised_result object.

```APIDOC
## tableTopConceptCounts

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

### Arguments
- **result** (summarised_result) - Required - The output object from `summariseConceptIdCounts()`.
- **top** (Integer) - Optional - The number of top concepts to display. Defaults to 10.
- **countBy** (Character) - Optional - Either 'person' or 'record'. If NULL, the value present in the data is used.
- **type** (Character) - Optional - Specifies the desired output table format. If NULL, global options or a default 'gt' table is used.
- **style** (Character/List) - Optional - Defines the visual formatting of the table. Can be a pre-defined style name, a YAML file path, or a list of custom R code.

### Value
A formatted table visualisation.

### Example
```r
library(OmopSketch)
library(omock)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
result <- summariseConceptIdCounts(cdm = cdm, omopTableName = "condition_occurrence")
tableTopConceptCounts(result = result, top = 5)
```
```

--------------------------------

### Select specific tables for characterization

Source: https://ohdsi.github.io/OmopSketch/articles/database_characteristics.html

Customize the analysis by specifying a subset of OMOP tables using the omopTableName argument.

```R
result <- databaseCharacteristics(
  cdm = cdm, 
  omopTableName = c("drug_exposure", "condition_occurrence")
)
```

--------------------------------

### summariseOmopSnapshot()

Source: https://ohdsi.github.io/OmopSketch/reference/index.html

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

```APIDOC
## summariseOmopSnapshot()

### Description
Summarise a cdm_reference object creating a snapshot with the metadata of the cdm_reference object.
```

--------------------------------

### Summarise clinical records in R

Source: https://ohdsi.github.io/OmopSketch/reference/summariseClinicalRecords.html

Initializes a mock CDM, performs a summary of the condition_occurrence table, and displays the result using tableClinicalRecords.

```R
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)
```

--------------------------------

### Generate a person summary table

Source: https://ohdsi.github.io/OmopSketch/reference/tablePerson.html

Creates a summary of the person table from a CDM reference and displays it as a table.

```R
library(OmopSketch)
library(omock)

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

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)

cdmDisconnect(cdm = cdm)
```

--------------------------------

### Usage of mockOmopSketch

Source: https://ohdsi.github.io/OmopSketch/reference/mockOmopSketch.html

Generates a mock cdm_reference object for testing purposes. Note that con, writeSchema, and seed arguments are deprecated.

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

--------------------------------

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

Source: https://ohdsi.github.io/OmopSketch/reference/summariseInObservation.html

Calculates observation statistics for an OMOP observation_period table.

```APIDOC
## summariseInObservation

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

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

### Arguments
- **observationPeriod** (omop table) - An observation_period omop table. It must be part of a cdm_reference object.
- **interval** (string) - Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".
- **output** (string) - Output format. It can be either the number of records ("record"), 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** (list) - A list of age groups to stratify the results by. Each element represents a specific age range.
- **sex** (logical) - Whether to stratify results by sex (TRUE) or not (FALSE).
- **dateRange** (vector) - A vector of two dates defining the desired study period.

### Value
A `summarised_result` object with the results.
```

--------------------------------

### Summarise with age groups and plot with aesthetics

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_observation_period.html

Aggregates data by sex and custom age groups, then visualises using colour and facet arguments.

```R
summarisedResult <- summariseObservationPeriod(
  cdm = cdm,
  sex = TRUE,
  ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf))
)

plotObservationPeriod(
  result = summarisedResult,
  colour = "sex",
  facet = "age_group"
)
```

--------------------------------

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

Source: https://ohdsi.github.io/OmopSketch/reference/summariseTrend.html

Summarises temporal trends from OMOP CDM tables, considering 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.

```APIDOC
## summariseTrend

### Description
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.

### Parameters
- **cdm** (cdm_reference) - Required - A cdm_reference object.
- **event** (character vector) - Optional - OMOP table names to treat as event tables (uses only start date).
- **episode** (character vector) - Optional - OMOP table names to treat as episode tables (uses start and end date).
- **output** (character vector) - Optional - Metrics to summarise: "record" (default), "person", "person-days", "age", "sex".
- **interval** (character) - Optional - Time interval to stratify by: "years", "quarters", "months", or "overall".
- **ageGroup** (list) - Optional - Age groups to stratify by, e.g., list(children = c(0, 17)).
- **sex** (logical) - Optional - Whether to stratify by sex.
- **inObservation** (logical) - Optional - If TRUE, stratifies by whether records occur within an observation period.
- **dateRange** (vector) - Optional - A vector of two dates defining the study period.

### Returns
- **summarised_result** (object) - A summarised_result object containing the calculated trends.
```

--------------------------------

### databaseCharacteristics()

Source: https://ohdsi.github.io/OmopSketch/reference/index.html

Summarise Database Characteristics for OMOP CDM.

```APIDOC
## databaseCharacteristics()

### Description
Summarise Database Characteristics for OMOP CDM.
```

--------------------------------

### BibTeX Citation for OmopSketch

Source: https://ohdsi.github.io/OmopSketch/authors.html

Use this BibTeX entry to cite the OmopSketch R package in academic publications.

```bibtex
@Manual{,
  title = {OmopSketch: Characterise Tables of an OMOP Common Data Model Instance},
  author = {Marta Alcalde-Herraiz and Kim Lopez-Guell and Elin Rowlands and Cecilia Campanile and Edward Burn and Martí Català},
  year = {2026},
  note = {R package version 1.1.0.900},
  url = {https://OHDSI.github.io/OmopSketch/},
}
```

--------------------------------

### Summarize and display person table

Source: https://ohdsi.github.io/OmopSketch/reference/tablePerson.html

Generates a summary of the person table from a CDM reference and renders it as a table.

```R
library(OmopSketch)
library(omock)

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

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)
```

--------------------------------

### Summarise trend by quarterly intervals

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_trend.html

Configures the temporal granularity to quarters to observe trends over shorter time periods.

```R
summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  interval = "quarters",
  output = "record"
)

summarisedResult |>
  select(additional_level, estimate_value)
#> # A tibble: 884 × 2
#>    additional_level         estimate_value
#>    <chr>                    <chr>         
#>  1 1908-10-01 to 1908-12-31 1             
#>  2 1908-10-01 to 1908-12-31 0.00          
#>  3 1909-04-01 to 1909-06-30 2             
#>  4 1909-04-01 to 1909-06-30 0.00          
#>  5 1909-07-01 to 1909-09-30 2             
#>  6 1909-07-01 to 1909-09-30 0.00          
#>  7 1910-01-01 to 1910-03-31 4             
#>  8 1910-01-01 to 1910-03-31 0.01          
#>  9 1910-04-01 to 1910-06-30 4             
#> 10 1910-04-01 to 1910-06-30 0.01          
#> # ℹ 874 more rows
```

--------------------------------

### Inspect Summarised Result

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_trend.html

Use settings and glimpse to inspect the structure and metadata of a summarised result object.

```R
summarisedResult |>
  settings() |>
  glimpse()
```

--------------------------------

### Function signature for tableTopConceptCounts

Source: https://ohdsi.github.io/OmopSketch/reference/tableTopConceptCounts.html

The standard function signature defining parameters for generating concept count tables.

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

--------------------------------

### Include concept counts

Source: https://ohdsi.github.io/OmopSketch/articles/database_characteristics.html

Enable concept-level summaries by setting conceptIdCounts to TRUE.

```R
result <- databaseCharacteristics(
  cdm = cdm,
  conceptIdCounts = TRUE
)
```

--------------------------------

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

Source: https://ohdsi.github.io/OmopSketch/reference/tableConceptIdCounts.html

Creates a visual table from a summarised_result object generated by summariseConceptIdCounts().

```APIDOC
## tableConceptIdCounts(result, display = "overall", type = "reactable")

### Description
Creates a visual table from a summariseConceptIdCounts() result.

### Arguments
- **result** (summarised_result) - Required - A summarised_result object (output of summariseConceptIdCounts()).
- **display** (character) - Optional - A character string indicating which subset of the data to display. Options are: "overall", "standard", "source", or "missing standard".
- **type** (character) - Optional - Type of formatting output table, either "reactable" or "datatable".

### Value
A formatted table visualisation.
```

--------------------------------

### Summarise and Format Trends

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_trend.html

Generate a trend summary using summariseTrend and format the output into a table with tableTrend.

```R
result <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  episode = "observation_period",
  output = "age",
  interval = "years"
)
tableTrend(result = result)
```

--------------------------------

### Summarise clinical records with concept summary

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_clinical_tables_records.html

Set conceptSummary = TRUE to include details on domains, standard/non-standard concept usage, and associated vocabularies.

```R
summarisedResult <- summariseClinicalRecords(
  cdm = cdm,
  omopTableName = "drug_exposure",
  recordsPerPerson = NULL, 
  conceptSummary = TRUE,
  missingData = FALSE,
  quality = FALSE
)
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> No analyses were selected.
#> ℹ Summarising domains in drug_exposure.
#> ℹ Summarising standard concepts in drug_exposure.
#> ℹ Summarising source vocabularies in drug_exposure.
#> ℹ Summarising concept types in drug_exposure.
#> ℹ Summarising concept class in drug_exposure.

summarisedResult |>
  select(variable_name, variable_level, estimate_name, estimate_value) 
#> # A tibble: 41 × 4
#>    variable_name       variable_level      estimate_name estimate_value
#>    <chr>               <chr>               <chr>         <chr>         
#>  1 Number subjects     NA                  count         2694          
#>  2 Number records      NA                  count         67707         
#>  3 Number subjects     NA                  percentage    100           
#>  4 Domain              Drug                count         67707         
#>  5 Standard concept    Standard            count         67707         
#>  6 Standard vocabulary CVX                 count         25710         
#>  7 Standard vocabulary RxNorm              count         41997         
#>  8 Source vocabulary   No matching concept count         35            
#>  9 Source vocabulary   CVX                 count         25710         
#> 10 Source vocabulary   NDC                 count         2694          
#> # ℹ 31 more rows
```

--------------------------------

### Select output measure for visualisation

Source: https://ohdsi.github.io/OmopSketch/articles/summarise_trend.html

Specify the output measure to visualise when the trend result contains multiple metrics.

```R
result <- summariseTrend(cdm,
  event = "measurement",
  interval = "quarters",
  output = c("sex", "record"),
  dateRange = as.Date(c("2010-01-01", "2019-12-31"))
)
#> → The observation period in the cdm ends in 2019-07-03
plotTrend(
  result = result,
  output = "sex"
)
```
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