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Visualise the results of summarisePerson() into a table

Visualise the output of summarisePerson()

Usage

tablePerson(result, type = NULL, style = NULL)

tablePerson(result, type = NULL, style = NULL)

Arguments

result

A summarised_result object (output of summarisePerson()).

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:

  1. Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.

  2. YAML file path: Provide the path to an existing .yml file defining a new style.

  3. 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.

A formatted table visualisation.

Examples

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#>  Reading GiBleed tables.
#>  Adding drug_strength table.
#>  Creating local <cdm_reference> object.
#>  Inserting <cdm_reference> into duckdb.

result <- summarisePerson(cdm = cdm)

tablePerson(result = result)
Summary of person table
Variable name Variable level Estimate name
CDM name
GiBleed
Number subjects N 2,694
Number subjects not in observation N (%) 0 (0.00%)
Sex Female N (%) 1,373 (50.97%)
Male N (%) 1,321 (49.03%)
None N (%) 0 (0.00%)
Sex source F N (%) 1,373 (50.97%)
M N (%) 1,321 (49.03%)
Race No matching concept N (%) 451 (16.74%)
Missing N (%) 2,243 (83.26%)
Race source asian N (%) 212 (7.87%)
black N (%) 338 (12.55%)
hispanic N (%) 435 (16.15%)
native N (%) 14 (0.52%)
other N (%) 2 (0.07%)
white N (%) 1,693 (62.84%)
Ethnicity No matching concept N (%) 2,259 (83.85%)
Missing N (%) 435 (16.15%)
Ethnicity source african N (%) 119 (4.42%)
american N (%) 79 (2.93%)
american_indian N (%) 14 (0.52%)
arab N (%) 2 (0.07%)
asian_indian N (%) 81 (3.01%)
central_american N (%) 75 (2.78%)
chinese N (%) 131 (4.86%)
dominican N (%) 105 (3.90%)
english N (%) 218 (8.09%)
french N (%) 129 (4.79%)
french_canadian N (%) 74 (2.75%)
german N (%) 130 (4.83%)
greek N (%) 19 (0.71%)
irish N (%) 438 (16.26%)
italian N (%) 295 (10.95%)
mexican N (%) 42 (1.56%)
polish N (%) 107 (3.97%)
portuguese N (%) 93 (3.45%)
puerto_rican N (%) 258 (9.58%)
russian N (%) 34 (1.26%)
scottish N (%) 48 (1.78%)
south_american N (%) 60 (2.23%)
swedish N (%) 29 (1.08%)
west_indian N (%) 114 (4.23%)
Year of birth Missing (%) 0 (0.00%)
Median [Q25 - Q75] 1,961 [1,950 - 1,970]
90% Range [Q05 to Q95] 1,922 to 1,979
Range [min to max] 1,908 to 1,986
Month of birth Missing (%) 0 (0.00%)
Median [Q25 - Q75] 7 [4 - 10]
90% Range [Q05 to Q95] 1 to 12
Range [min to max] 1 to 12
Day of birth Missing (%) 0 (0.00%)
Median [Q25 - Q75] 16 [8 - 23]
90% Range [Q05 to Q95] 2 to 29
Range [min to max] 1 to 31
Location Missing (%) 2,694 (100.00%)
Zero count (%) 0 (0.00%)
Distinct values 1
Provider Missing (%) 2,694 (100.00%)
Zero count (%) 0 (0.00%)
Distinct values 1
Care site Missing (%) 2,694 (100.00%)
Zero count (%) 0 (0.00%)
Distinct values 1
# } # \donttest{ library(OmopSketch) library(omock) cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb") #> Reading GiBleed tables. #> Adding drug_strength table. #> Creating local <cdm_reference> object. #> Inserting <cdm_reference> into duckdb. result <- summarisePerson(cdm = cdm) tablePerson(result = result)
Summary of person table
Variable name Variable level Estimate name
CDM name
GiBleed
Number subjects N 2,694
Number subjects not in observation N (%) 0 (0.00%)
Sex Female N (%) 1,373 (50.97%)
Male N (%) 1,321 (49.03%)
None N (%) 0 (0.00%)
Sex source F N (%) 1,373 (50.97%)
M N (%) 1,321 (49.03%)
Race No matching concept N (%) 451 (16.74%)
Missing N (%) 2,243 (83.26%)
Race source asian N (%) 212 (7.87%)
black N (%) 338 (12.55%)
hispanic N (%) 435 (16.15%)
native N (%) 14 (0.52%)
other N (%) 2 (0.07%)
white N (%) 1,693 (62.84%)
Ethnicity No matching concept N (%) 2,259 (83.85%)
Missing N (%) 435 (16.15%)
Ethnicity source african N (%) 119 (4.42%)
american N (%) 79 (2.93%)
american_indian N (%) 14 (0.52%)
arab N (%) 2 (0.07%)
asian_indian N (%) 81 (3.01%)
central_american N (%) 75 (2.78%)
chinese N (%) 131 (4.86%)
dominican N (%) 105 (3.90%)
english N (%) 218 (8.09%)
french N (%) 129 (4.79%)
french_canadian N (%) 74 (2.75%)
german N (%) 130 (4.83%)
greek N (%) 19 (0.71%)
irish N (%) 438 (16.26%)
italian N (%) 295 (10.95%)
mexican N (%) 42 (1.56%)
polish N (%) 107 (3.97%)
portuguese N (%) 93 (3.45%)
puerto_rican N (%) 258 (9.58%)
russian N (%) 34 (1.26%)
scottish N (%) 48 (1.78%)
south_american N (%) 60 (2.23%)
swedish N (%) 29 (1.08%)
west_indian N (%) 114 (4.23%)
Year of birth Missing (%) 0 (0.00%)
Median [Q25 - Q75] 1,961 [1,950 - 1,970]
90% Range [Q05 to Q95] 1,922 to 1,979
Range [min to max] 1,908 to 1,986
Month of birth Missing (%) 0 (0.00%)
Median [Q25 - Q75] 7 [4 - 10]
90% Range [Q05 to Q95] 1 to 12
Range [min to max] 1 to 12
Day of birth Missing (%) 0 (0.00%)
Median [Q25 - Q75] 16 [8 - 23]
90% Range [Q05 to Q95] 2 to 29
Range [min to max] 1 to 31
Location Missing (%) 2,694 (100.00%)
Zero count (%) 0 (0.00%)
Distinct values 1
Provider Missing (%) 2,694 (100.00%)
Zero count (%) 0 (0.00%)
Distinct values 1
Care site Missing (%) 2,694 (100.00%)
Zero count (%) 0 (0.00%)
Distinct values 1
cdmDisconnect(cdm = cdm) # }