Generate a formatted table from a <data.table>

This function combines the functionalities of formatEstimateValue(), formatEstimateName(), formatHeader(), and formatTable() into a single function. While it does not require the input table to be a <summarised_result>, it does expect specific fields to apply some formatting functionalities.

Usage

visTable(
  result,
  estimateName = character(),
  header = character(),
  groupColumn = character(),
  rename = character(),
  type = NULL,
  hide = character(),
  style = NULL,
  .options = list()
)

Arguments

result

A table to format.

estimateName

A named list of estimate names to join, sorted by computation order. Use <...> to indicate estimate names.

header

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. The vector elements can be column names or labels for overall headers. The table must contain an estimate_value column to pivot the headers.

groupColumn

Columns to use as group labels, to see options use tableColumns(result). By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")).

*tidy: The tidy format applied to column names replaces "_" with a space and converts to sentence case. Use rename to customise specific column names.

rename

A named vector to customise column names, e.g., c("Database name" = "cdm_name"). The function renames all column names not specified here into a tidy* format.

type

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

hide

Columns to drop from the output table.

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 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 (see setGlobalTableOptions()) or an existing _brand.yml file (if found); otherwise, the default style is applied. For more details, see the Styles vignette on the package website.

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A formatted table of the class selected in "type" argument.

Examples

result <- mockSummarisedResult()
result |>
  visTable(
    estimateName = c("N%" = "<count> (<percentage>)",
                     "N" = "<count>",
                     "Mean (SD)" = "<mean> (<sd>)"),
    header = c("Estimate"),
    rename = c("Database name" = "cdm_name"),
    groupColumn = c("strata_name", "strata_level"),
    hide = c("additional_name", "additional_level", "estimate_type", "result_type")
  )


  
Result id
      
Database name
      
Group name
      
Group level
      
Variable name
      
Variable level
      
Estimate name
      
Estimate
    
overall; overall
    
1
mock
cohort_name
cohort1
number subjects
–
N
2,655,087



cohort2
number subjects
–
N
617,863



cohort1
age
–
Mean (SD)
38.00 (7.94)



cohort2
age
–
Mean (SD)
38.24 (7.89)



cohort1
Medications
Amoxiciline
N%
7,068 (34.67)



cohort2
Medications
Amoxiciline
N%
33,239 (71.25)



cohort1
Medications
Ibuprofen
N%
23,963 (92.41)



cohort2
Medications
Ibuprofen
N%
60,493 (10.32)
age_group &&& sex; <40 &&& Male
    
1
mock
cohort_name
cohort1
number subjects
–
N
3,721,239



cohort2
number subjects
–
N
2,059,746



cohort1
age
–
Mean (SD)
77.74 (1.08)



cohort2
age
–
Mean (SD)
86.97 (0.23)



cohort1
Medications
Amoxiciline
N%
9,947 (33.38)



cohort2
Medications
Amoxiciline
N%
65,087 (40.00)



cohort1
Medications
Ibuprofen
N%
5,893 (59.88)



cohort2
Medications
Ibuprofen
N%
65,472 (44.63)
age_group &&& sex; >=40 &&& Male
    
1
mock
cohort_name
cohort1
number subjects
–
N
5,728,534



cohort2
number subjects
–
N
1,765,568



cohort1
age
–
Mean (SD)
93.47 (7.24)



cohort2
age
–
Mean (SD)
34.03 (4.77)



cohort1
Medications
Amoxiciline
N%
31,627 (47.64)



cohort2
Medications
Amoxiciline
N%
25,802 (32.54)



cohort1
Medications
Ibuprofen
N%
64,229 (97.62)



cohort2
Medications
Ibuprofen
N%
35,320 (64.01)
age_group &&& sex; <40 &&& Female
    
1
mock
cohort_name
cohort1
number subjects
–
N
9,082,078



cohort2
number subjects
–
N
6,870,228



cohort1
age
–
Mean (SD)
21.21 (4.11)



cohort2
age
–
Mean (SD)
48.21 (7.32)



cohort1
Medications
Amoxiciline
N%
51,863 (89.22)



cohort2
Medications
Amoxiciline
N%
47,855 (75.71)



cohort1
Medications
Ibuprofen
N%
87,627 (73.18)



cohort2
Medications
Ibuprofen
N%
27,026 (99.18)
age_group &&& sex; >=40 &&& Female
    
1
mock
cohort_name
cohort1
number subjects
–
N
2,016,819



cohort2
number subjects
–
N
3,841,037



cohort1
age
–
Mean (SD)
65.17 (8.21)



cohort2
age
–
Mean (SD)
59.96 (6.93)



cohort1
Medications
Amoxiciline
N%
66,201 (86.43)



cohort2
Medications
Amoxiciline
N%
76,631 (20.27)



cohort1
Medications
Ibuprofen
N%
77,891 (35.67)



cohort2
Medications
Ibuprofen
N%
99,268 (49.56)
sex; Male
    
1
mock
cohort_name
cohort1
number subjects
–
N
8,983,897



cohort2
number subjects
–
N
7,698,414



cohort1
age
–
Mean (SD)
12.56 (6.47)



cohort2
age
–
Mean (SD)
49.35 (4.78)



cohort1
Medications
Amoxiciline
N%
40,683 (39.00)



cohort2
Medications
Amoxiciline
N%
8,425 (71.11)



cohort1
Medications
Ibuprofen
N%
79,731 (43.15)



cohort2
Medications
Ibuprofen
N%
63,349 (48.43)
sex; Female
    
1
mock
cohort_name
cohort1
number subjects
–
N
9,446,753



cohort2
number subjects
–
N
4,976,992



cohort1
age
–
Mean (SD)
26.72 (7.83)



cohort2
age
–
Mean (SD)
18.62 (8.61)



cohort1
Medications
Amoxiciline
N%
91,288 (77.73)



cohort2
Medications
Amoxiciline
N%
87,532 (12.17)



cohort1
Medications
Ibuprofen
N%
45,527 (14.82)



cohort2
Medications
Ibuprofen
N%
21,321 (17.34)
age_group; <40
    
1
mock
cohort_name
cohort1
number subjects
–
N
6,607,978



cohort2
number subjects
–
N
7,176,185



cohort1
age
–
Mean (SD)
38.61 (5.53)



cohort2
age
–
Mean (SD)
82.74 (4.38)



cohort1
Medications
Amoxiciline
N%
29,360 (96.06)



cohort2
Medications
Amoxiciline
N%
33,907 (24.55)



cohort1
Medications
Ibuprofen
N%
41,008 (1.31)



cohort2
Medications
Ibuprofen
N%
12,937 (75.48)
age_group; >=40
    
1
mock
cohort_name
cohort1
number subjects
–
N
6,291,140



cohort2
number subjects
–
N
9,919,061



cohort1
age
–
Mean (SD)
1.34 (5.30)



cohort2
age
–
Mean (SD)
66.85 (2.45)



cohort1
Medications
Amoxiciline
N%
45,907 (43.47)



cohort2
Medications
Amoxiciline
N%
83,944 (14.33)



cohort1
Medications
Ibuprofen
N%
81,087 (71.56)



cohort2
Medications
Ibuprofen
N%
47,812 (45.39)