Tables

Introduction

The visOmopResults package provides user-friendly tools for creating well-formatted tables and plots that are publication-ready. In this vignette, we focus specifically on the table formatting functionalities. The package supports four table formats: <tibble>, <gt>, <flextable>, and <datatables>. While <tibble> is an <data.frame> R object, <gt> and <flextable> are designed to create publication-ready tables that can be exported to different formats (e.g., PNG, Word, PDF, HTML), and <datatables> display tables on HTML pages. These last three can be used in ShinyApps, RMarkdown, Quarto, and more.

Although the primary aim of the package is to simplify the handling of the <summarised_result> class (see omopgenerics for more details), its functionalities can be applied to any <data.frame> if certain requirements are met.

Types of Table Functions

There are two main categories of table functions in the package:

This vignette will guide you through the usage of these functions.

Main Functions

These functions are built on top of the format functions, providing a quick and straightforward way to format tables.

visTable()

visTable() is a flexible function designed to format any <data.frame>.

Let’s demonstrate its usage with a dataset from the palmerpenguins package.

library(visOmopResults)
library(palmerpenguins)
library(dplyr)
library(tidyr)
x <- penguins |> 
  filter(!is.na(sex) & year == 2008) |> 
  select(!"body_mass_g") |>
  summarise(across(ends_with("mm"), ~mean(.x)), .by = c("species", "island", "sex"))
head(x)
#> # A tibble: 6 × 6
#>   species island    sex    bill_length_mm bill_depth_mm flipper_length_mm
#>   <fct>   <fct>     <fct>           <dbl>         <dbl>             <dbl>
#> 1 Adelie  Biscoe    female           36.6          17.2              187.
#> 2 Adelie  Biscoe    male             40.8          19.0              193.
#> 3 Adelie  Torgersen female           36.6          17.4              190 
#> 4 Adelie  Torgersen male             40.9          18.8              194.
#> 5 Adelie  Dream     female           36.3          17.8              189 
#> 6 Adelie  Dream     male             40.1          18.9              195

We can format this data into a <gt> table using visTable() as follows:

visTable(
  result = x,
  groupColumn = c("sex"),
  rename = c("Bill length (mm)" = "bill_length_mm",
             "Bill depth (mm)" = "bill_depth_mm",
             "Flipper length (mm)" = "flipper_length_mm"),
  type = "gt",
  hide = "year"
)
Species Island Bill length (mm) Bill depth (mm) Flipper length (mm)
female
Adelie Biscoe 36.6444444444444 17.2222222222222 186.555555555556
Torgersen 36.6125 17.4 190
Dream 36.275 17.7875 189
Gentoo Biscoe 45.2954545454545 14.1318181818182 213
Chinstrap Dream 46 17.3 192.666666666667
male
Adelie Biscoe 40.7555555555556 19.0333333333333 192.555555555556
Torgersen 40.925 18.8375 193.5
Dream 40.1125 18.8875 195
Gentoo Biscoe 48.5391304347826 15.704347826087 222.086956521739
Chinstrap Dream 51.4 19.6 202.777777777778

To use the arguments estimateName and header, the <data.frame> must have the estimates arranged into three columns: estimate_name, estimate_type, and estimate_value. Let’s reshape the example dataset accordingly and demonstrate creating a <flextable> object:

# Transforming the dataset to include estimate columns
x <- x |>
  pivot_longer(
    cols = ends_with("_mm"), 
    names_to = "estimate_name", 
    values_to = "estimate_value"
  ) |>
  mutate(estimate_type = "numeric")

# Creating a formatted flextable
visTable(
  result = x,
  estimateName = c(
    "Bill length - Bill depth (mm)" = "<bill_length_mm> - <bill_depth_mm>",
    "Flipper length (mm)" = "<flipper_length_mm>"
  ),
  header = c("species", "island"),
  groupColumn = "sex",
  type = "flextable",
  hide = c("year", "estimate_type")
)

Estimate name

Species

Adelie

Gentoo

Chinstrap

Island

Biscoe

Torgersen

Dream

Biscoe

Dream

female

Bill length - Bill depth (mm)

36.64 - 17.22

36.61 - 17.40

36.27 - 17.79

45.30 - 14.13

46.00 - 17.30

Flipper length (mm)

186.56

190.00

189.00

213.00

192.67

male

Bill length - Bill depth (mm)

40.76 - 19.03

40.92 - 18.84

40.11 - 18.89

48.54 - 15.70

51.40 - 19.60

Flipper length (mm)

192.56

193.50

195.00

222.09

202.78

visOmopTable()

visOmopTable() extends the functionality of visTable() with additional features tailored specifically for handling <summarised_result> objects, making it easier to work with standardized result formats.

Let’s demonstrate visOmopTable() with a mock <summarised_result>:

# Creating a mock summarised result
result <- mockSummarisedResult() |>
  filter(strata_name == "age_group &&& sex")

# Displaying the first few rows
head(result)
#> # A tibble: 6 × 13
#>   result_id cdm_name group_name  group_level strata_name       strata_level   
#>       <int> <chr>    <chr>       <chr>       <chr>             <chr>          
#> 1         1 mock     cohort_name cohort1     age_group &&& sex <40 &&& Male   
#> 2         1 mock     cohort_name cohort1     age_group &&& sex >=40 &&& Male  
#> 3         1 mock     cohort_name cohort1     age_group &&& sex <40 &&& Female 
#> 4         1 mock     cohort_name cohort1     age_group &&& sex >=40 &&& Female
#> 5         1 mock     cohort_name cohort2     age_group &&& sex <40 &&& Male   
#> 6         1 mock     cohort_name cohort2     age_group &&& sex >=40 &&& Male  
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>

# Creating a formatted gt table
visOmopTable(
  result = result,
  estimateName = c(
    "N%" = "<count> (<percentage>)",
    "N" = "<count>",
    "Mean (SD)" = "<mean> (<sd>)"
  ),
  header = c("package_name", "age_group"),
  groupColumn = c("cohort_name", "sex"),
  settingsColumn = "package_name",
  type = "gt"
)
Package name
visOmopResults
CDM name Variable name Variable level Estimate name
Age group
<40 >=40
cohort1; Male
mock number subjects - N 3,721,239 5,728,534
age - Mean (SD) 77.74 (1.08) 93.47 (7.24)
Medications Amoxiciline N% 9,947 (33.38) 31,627 (47.64)
Ibuprofen N% 5,893 (59.88) 64,229 (97.62)
cohort1; Female
mock number subjects - N 9,082,078 2,016,819
age - Mean (SD) 21.21 (4.11) 65.17 (8.21)
Medications Amoxiciline N% 51,863 (89.22) 66,201 (86.43)
Ibuprofen N% 87,627 (73.18) 77,891 (35.67)
cohort2; Male
mock number subjects - N 2,059,746 1,765,568
age - Mean (SD) 86.97 (0.23) 34.03 (4.77)
Medications Amoxiciline N% 65,087 (40.00) 25,802 (32.54)
Ibuprofen N% 65,472 (44.63) 35,320 (64.01)
cohort2; Female
mock number subjects - N 6,870,228 3,841,037
age - Mean (SD) 48.21 (7.32) 59.96 (6.93)
Medications Amoxiciline N% 47,855 (75.71) 76,631 (20.27)
Ibuprofen N% 27,026 (99.18) 99,268 (49.56)

The workflow is quite similar to visTable(), but it includes specific enhancements for <summarised_result> objects:

In the next example, visOmopTable() generates a <gt> table while displaying suppressed estimates (those with counts below 1,000,000) with the specified minimum cell count.

result |>
  suppress(minCellCount = 1000000) |>
  visOmopTable(
    estimateName = c(
      "N%" = "<count> (<percentage>)",
      "N" = "<count>",
      "Mean (SD)" = "<mean> (<sd>)"
    ),
    header = c("group"),
    groupColumn = c("strata"),
    hide = c("cdm_name"),
    showMinCellCount = TRUE,
    type = "reactable"
  )

Customise tables

Tables displayed in visOmopResults() follow a default style, but customisation is possible through the style and .options arguments. These allow users to modify various formatting aspects using options from the format functions (see the format Functions section to learn more).

Style

By default, the visOmopResults default style is applied for all tables. At the moment, besides from the default style, the package also supports the “darwin” style.

To inspect the code for the default styles of the different table types supported, you can use the tableStyleCode() function as showed next.

tableStyleCode(type = "gt", style = "default")
#> list(header = list(gt::cell_fill(color = "#c8c8c8"), gt::cell_text(weight = "bold", 
#>     align = "center")), header_name = list(gt::cell_fill(color = "#d9d9d9"), 
#>     gt::cell_text(weight = "bold", align = "center")), header_level = list(gt::cell_fill(color = "#e1e1e1"), 
#>     gt::cell_text(weight = "bold", align = "center")), column_name = list(gt::cell_text(weight = "bold", 
#>     align = "center")), group_label = list(gt::cell_fill(color = "#e9e9e9"), 
#>     gt::cell_text(weight = "bold")), title = list(gt::cell_text(weight = "bold", 
#>     size = 15, align = "center")), subtitle = list(gt::cell_text(weight = "bold", 
#>     size = 12, align = "center")), body = list())

If you want all your tables to use the same type and style, use setGlobalTableOptions(). This will apply the desired settings to all subsequent tables, unless you specify a different style in a specific function call.

Further Options

The main vis table functions are built on top of specific formatting functions described in the next section. These core table functions do not directly expose all customization arguments in their signature. Instead, additional tweaks can be made via the .options argument.

To view the full list of customization options and their default values, use:

tableOptions()
#> $decimals
#>    integer percentage    numeric proportion 
#>          0          2          2          2 
#> 
#> $decimalMark
#> [1] "."
#> 
#> $bigMark
#> [1] ","
#> 
#> $keepNotFormatted
#> [1] TRUE
#> 
#> $useFormatOrder
#> [1] TRUE
#> 
#> $delim
#> [1] "\n"
#> 
#> $includeHeaderName
#> [1] TRUE
#> 
#> $includeHeaderKey
#> [1] TRUE
#> 
#> $na
#> [1] "-"
#> 
#> $title
#> NULL
#> 
#> $subtitle
#> NULL
#> 
#> $caption
#> NULL
#> 
#> $groupAsColumn
#> [1] FALSE
#> 
#> $groupOrder
#> NULL
#> 
#> $merge
#> [1] "all_columns"

As mentioned, all these arguments originate from specific formatting functions. The table below shows, for each argument, the function it belongs to and its purpose:

Argument Description
formatEstimateValue()
decimals Number of decimals to display, which can be specified per estimate type (integer, numeric, percentage, proportion), per estimate name, or applied to all estimates.
decimalMark Symbol to use as the decimal separator.
bigMark Symbol to use as the thousands and millions separator.
formatEstimateName()
keepNotFormatted Whether to retain rows with estimate names that are not explicitly formatted.
useFormatOrder Whether to display estimate names in the order provided in `estimateName` (TRUE) or in the order of the input data frame (FALSE).
formatHeader()
delim Delimiter to use when separating header components.
includeHeaderName Whether to include the column name as part of the header.
includeHeaderKey Whether to prefix header elements with their type (e.g., header, header_name, header_level).
formatTable()
style Named list specifying styles for table components (e.g., title, subtitle, header, body). Use `'default'` for the default `visOmopResults` style or `NULL` for the package default (either `gt` or `flextable`). Use `tableStyleCode()` to preview the pre-defined styles.
na Value to display for missing data.
title Title of the table. Use `NULL` for no title.
subtitle Subtitle of the table. Use `NULL` for no subtitle.
caption Caption in markdown format. Use `NULL` for no caption. For example, *Your caption here* renders in italics.
groupAsColumn Whether to display group labels as a separate column (`TRUE`) or as row headers (`FALSE`).
groupOrder Order in which to display group labels.
merge Columns to merge vertically when consecutive cells have the same value. Use `'all_columns'` to merge all, or `NULL` for no merging.

Formatting Functions

The format set of functions can be used in a pipeline to transform and format a <data.frame> or a <summarised_result> object. Below, we’ll demonstrate how to utilize these functions in a step-by-step manner.

1) Format Estimates

The formatEstimateName() and formatEstimateValue() functions enable you to customise the naming and display of estimates in your table.

To illustrate their usage, we’ll continue with the result dataset. Let’s first take a look at some of the estimates before any formatting is applied:

result |> 
  filterGroup(cohort_name == "cohort1") |>  # visOmopResult filter function
  filterStrata(age_group == "<40", sex == "Female") |>  # visOmopResult filter function
  select(variable_name, variable_level, estimate_name, estimate_type, estimate_value)
#> # A tibble: 7 × 5
#>   variable_name   variable_level estimate_name estimate_type estimate_value  
#>   <chr>           <chr>          <chr>         <chr>         <chr>           
#> 1 number subjects <NA>           count         integer       9082078         
#> 2 age             <NA>           mean          numeric       21.2142521282658
#> 3 age             <NA>           sd            numeric       4.11274429643527
#> 4 Medications     Amoxiciline    count         integer       51863           
#> 5 Medications     Amoxiciline    percentage    percentage    89.2198335845023
#> 6 Medications     Ibuprofen      count         integer       87627           
#> 7 Medications     Ibuprofen      percentage    percentage    73.1792511884123

1.1) Suppressed estimates

The function formatMinCellCount() indicates which estimates have been suppressed due to the minimum cell count specified in the study.

Estimates are suppressed using omopgenerics::suppress(), which replaces the estimate with the symbol “-”. When reporting results, we want to distinguish suppressed estimates from missing values (NA), the formatMinCellCount() function can be used as follows:

result <- result |> formatMinCellCount()

1.2) Estimate values

The formatEstimateValue() function allows you to specify the number of decimals for different estimate_types or estimate_names, as well as customise decimal and thousand separators.

Let’s see how the previous estimates are updated afterwars:

# Formatting estimate values
result <- result |>
  formatEstimateValue(
    decimals = c(integer = 0, numeric = 4, percentage = 2),
    decimalMark = ".",
    bigMark = ","
  )

# Displaying the formatted subset
result |> 
  filterGroup(cohort_name == "cohort1") |>  
  filterStrata(age_group == "<40", sex == "Female") |> 
  select(variable_name, variable_level, estimate_name, estimate_type, estimate_value)
#> # A tibble: 7 × 5
#>   variable_name   variable_level estimate_name estimate_type estimate_value
#>   <chr>           <chr>          <chr>         <chr>         <chr>         
#> 1 number subjects <NA>           count         integer       9,082,078     
#> 2 age             <NA>           mean          numeric       21.2143       
#> 3 age             <NA>           sd            numeric       4.1127        
#> 4 Medications     Amoxiciline    count         integer       51,863        
#> 5 Medications     Amoxiciline    percentage    percentage    89.22         
#> 6 Medications     Ibuprofen      count         integer       87,627        
#> 7 Medications     Ibuprofen      percentage    percentage    73.18

As you can see, the estimates now reflect the specified formatting rules.

1.3) Estimate names

Next, we will format the estimate names using the formatEstimateName() function. This function allows us to combine counts and percentages as “N (%)”, among other estimate combinations

# Formatting estimate names
result <- result |> 
  formatEstimateName(
    estimateName = c(
      "N (%)" = "<count> (<percentage>%)", 
      "N" = "<count>",
      "Mean (SD)" = "<mean> (<sd>)"
    ),
    keepNotFormatted = TRUE,
    useFormatOrder = FALSE
  )

# Displaying the formatted subset with new estimate names
result |> 
  filterGroup(cohort_name == "cohort1") |>  
  filterStrata(age_group == "<40", sex == "Female") |> 
  select(variable_name, variable_level, estimate_name, estimate_type, estimate_value)
#> # A tibble: 4 × 5
#>   variable_name   variable_level estimate_name estimate_type estimate_value  
#>   <chr>           <chr>          <chr>         <chr>         <chr>           
#> 1 number subjects <NA>           N             character     9,082,078       
#> 2 age             <NA>           Mean (SD)     character     21.2143 (4.1127)
#> 3 Medications     Amoxiciline    N (%)         character     51,863 (89.22%) 
#> 4 Medications     Ibuprofen      N (%)         character     87,627 (73.18%)

Now, the estimate names are displayed as specified, such as “N (%)” for counts and percentages. The keepNotFormatted argument ensures that unformatted rows remain in the dataset, while useFormatOrder allows control over the display order of the estimates.

2) Format Header

formatHeader() is used to create complex multi-level headers for tables, making it easy to present grouped data clearly.

Header levels

There are 3 different levels of headers, each identified with the following keys:

  • header: Custom labels that do not correspond to column names or table values.

  • header_name: Labels derived from column names. Can be omitted with includeHeaderName = FALSE.

  • header_level: Labels derived from values within columns set in header.

These keys, together with a delimiter between header levels (delim) are used in formatTable() to format and style gt or flextable tables.

Let’s create a multi-level header for the strata columns, including all three keys. This will show how the column names are transformed:

result |>
  mutate(across(c("strata_name", "strata_level"), ~ gsub("&&&", "and", .x))) |>
  formatHeader(
    header = c("Stratifications", "strata_name", "strata_level"),
    delim = "\n",
    includeHeaderName = TRUE,
    includeHeaderKey = TRUE
  ) |> 
  colnames()
#>  [1] "result_id"                                                                                                                                   
#>  [2] "cdm_name"                                                                                                                                    
#>  [3] "group_name"                                                                                                                                  
#>  [4] "group_level"                                                                                                                                 
#>  [5] "variable_name"                                                                                                                               
#>  [6] "variable_level"                                                                                                                              
#>  [7] "estimate_name"                                                                                                                               
#>  [8] "estimate_type"                                                                                                                               
#>  [9] "additional_name"                                                                                                                             
#> [10] "additional_level"                                                                                                                            
#> [11] "[header]Stratifications\n[header_name]strata_name\n[header_level]age_group and sex\n[header_name]strata_level\n[header_level]<40 and Male"   
#> [12] "[header]Stratifications\n[header_name]strata_name\n[header_level]age_group and sex\n[header_name]strata_level\n[header_level]>=40 and Male"  
#> [13] "[header]Stratifications\n[header_name]strata_name\n[header_level]age_group and sex\n[header_name]strata_level\n[header_level]<40 and Female" 
#> [14] "[header]Stratifications\n[header_name]strata_name\n[header_level]age_group and sex\n[header_name]strata_level\n[header_level]>=40 and Female"

For the table we are formatting, we won’t include the header_name labels. Let’s see how it looks when we exclude them:

result <- result |>
  mutate(across(c("strata_name", "strata_level"), ~ gsub("&&&", "and", .x))) |>
  formatHeader(
    header = c("Stratifications", "strata_name", "strata_level"),
    delim = "\n",
    includeHeaderName = FALSE,
    includeHeaderKey = TRUE
  )  

colnames(result)
#>  [1] "result_id"                                                                              
#>  [2] "cdm_name"                                                                               
#>  [3] "group_name"                                                                             
#>  [4] "group_level"                                                                            
#>  [5] "variable_name"                                                                          
#>  [6] "variable_level"                                                                         
#>  [7] "estimate_name"                                                                          
#>  [8] "estimate_type"                                                                          
#>  [9] "additional_name"                                                                        
#> [10] "additional_level"                                                                       
#> [11] "[header]Stratifications\n[header_level]age_group and sex\n[header_level]<40 and Male"   
#> [12] "[header]Stratifications\n[header_level]age_group and sex\n[header_level]>=40 and Male"  
#> [13] "[header]Stratifications\n[header_level]age_group and sex\n[header_level]<40 and Female" 
#> [14] "[header]Stratifications\n[header_level]age_group and sex\n[header_level]>=40 and Female"

3) Format Table

formatTable() function is the final step in the formatting pipeline, where the formatted <data.frame> is converted to either a <gt> or <flextable>.

Prepare data

Before using formatTable(), we’ll tidy the <data.frame> by splitting the group and additional name-level columns (see vignette on tidying <summarised_result>), and drop some unwanted columns:

result <- result |>
  splitGroup() |>
  splitAdditional() |>
  select(!c("result_id", "estimate_type", "cdm_name"))
head(result)
#> # A tibble: 6 × 8
#>   cohort_name variable_name  variable_level estimate_name [header]Stratificati…¹
#>   <chr>       <chr>          <chr>          <chr>         <chr>                 
#> 1 cohort1     number subjec… <NA>           N             3,721,239             
#> 2 cohort2     number subjec… <NA>           N             2,059,746             
#> 3 cohort1     age            <NA>           Mean (SD)     77.7445 (1.0794)      
#> 4 cohort2     age            <NA>           Mean (SD)     86.9691 (0.2333)      
#> 5 cohort1     Medications    Amoxiciline    N (%)         9,947 (33.38%)        
#> 6 cohort2     Medications    Amoxiciline    N (%)         65,087 (40.00%)       
#> # ℹ abbreviated name:
#> #   ¹​`[header]Stratifications\n[header_level]age_group and sex\n[header_level]<40 and Male`
#> # ℹ 3 more variables:
#> #   `[header]Stratifications\n[header_level]age_group and sex\n[header_level]>=40 and Male` <chr>,
#> #   `[header]Stratifications\n[header_level]age_group and sex\n[header_level]<40 and Female` <chr>,
#> #   `[header]Stratifications\n[header_level]age_group and sex\n[header_level]>=40 and Female` <chr>

Use formatTable()

Now that the data is cleaned and organized, formatTable() can be used to create a well-structured <gt>, <flextable>, datatable, or `reactable`` objects.

result |>
  formatTable(
    type = "gt",
    delim = "\n",
    style = "default",
    na = "-",
    title = "My formatted table!",
    subtitle = "Created with the `visOmopResults` R package.",
    caption = NULL,
    groupColumn = "cohort_name",
    groupAsColumn = FALSE,
    groupOrder = c("cohort2", "cohort1"),
    merge = "variable_name"
  )
My formatted table!
Created with the `visOmopResults` R package.
Stratifications
variable_name variable_level estimate_name
age_group and sex
<40 and Male >=40 and Male <40 and Female >=40 and Female
cohort2
number subjects - N 2,059,746 1,765,568 6,870,228 3,841,037
age - Mean (SD) 86.9691 (0.2333) 34.0349 (4.7723) 48.2080 (7.3231) 59.9566 (6.9273)
Medications Amoxiciline N (%) 65,087 (40.00%) 25,802 (32.54%) 47,855 (75.71%) 76,631 (20.27%)
Ibuprofen N (%) 65,472 (44.63%) 35,320 (64.01%) 27,026 (99.18%) 99,268 (49.56%)
cohort1
number subjects - N 3,721,239 5,728,534 9,082,078 2,016,819
age - Mean (SD) 77.7445 (1.0794) 93.4705 (7.2371) 21.2143 (4.1127) 65.1674 (8.2095)
Medications Amoxiciline N (%) 9,947 (33.38%) 31,627 (47.64%) 51,863 (89.22%) 66,201 (86.43%)
Ibuprofen N (%) 5,893 (59.88%) 64,229 (97.62%) 87,627 (73.18%) 77,891 (35.67%)

In the examples above, we used the default style defined in the visOmopResults package (use tableStyle() to see these styles). However, it’s possible to customise the appearance of different parts of the table to better suit your needs.

customising Table Styles

Let’s start by applying a custom style to a <gt> table:

result |>
  formatTable(
    type = "gt",
    delim = "\n",
    style = list(
      "header" = list(gt::cell_text(weight = "bold"), 
                      gt::cell_fill(color = "orange")),
      "header_level" = list(gt::cell_text(weight = "bold"), 
                      gt::cell_fill(color = "yellow")),
      "column_name" = gt::cell_text(weight = "bold"),
      "group_label" = list(gt::cell_fill(color = "blue"),
                           gt::cell_text(color = "white", weight = "bold")),
      "title" = list(gt::cell_text(size = 20, weight = "bold")),
      "subtitle" = list(gt::cell_text(size = 15)),
      "body" = gt::cell_text(color = "red")
    ),
    na = "-",
    title = "My formatted table!",
    subtitle = "Created with the `visOmopResults` R package.",
    caption = NULL,
    groupColumn = "cohort_name",
    groupAsColumn = FALSE,
    groupOrder = c("cohort2", "cohort1"),
    merge = "variable_name"
  )
My formatted table!
Created with the `visOmopResults` R package.
Stratifications
variable_name variable_level estimate_name
age_group and sex
<40 and Male >=40 and Male <40 and Female >=40 and Female
cohort2
number subjects - N 2,059,746 1,765,568 6,870,228 3,841,037
age - Mean (SD) 86.9691 (0.2333) 34.0349 (4.7723) 48.2080 (7.3231) 59.9566 (6.9273)
Medications Amoxiciline N (%) 65,087 (40.00%) 25,802 (32.54%) 47,855 (75.71%) 76,631 (20.27%)
Ibuprofen N (%) 65,472 (44.63%) 35,320 (64.01%) 27,026 (99.18%) 99,268 (49.56%)
cohort1
number subjects - N 3,721,239 5,728,534 9,082,078 2,016,819
age - Mean (SD) 77.7445 (1.0794) 93.4705 (7.2371) 21.2143 (4.1127) 65.1674 (8.2095)
Medications Amoxiciline N (%) 9,947 (33.38%) 31,627 (47.64%) 51,863 (89.22%) 66,201 (86.43%)
Ibuprofen N (%) 5,893 (59.88%) 64,229 (97.62%) 87,627 (73.18%) 77,891 (35.67%)

For creating a similarly styled <flextable>, the office R package is required to access specific formatting functions.

result |>
  formatTable(
    type = "flextable",
    delim = "\n",
    style = list(
      "header" = list(
        "cell" = officer::fp_cell(background.color = "orange"),
        "text" = officer::fp_text(bold = TRUE)),
      "header_level" = list(
        "cell" = officer::fp_cell(background.color = "yellow"),
        "text" = officer::fp_text(bold = TRUE)),
      "column_name" = list("text" = officer::fp_text(bold = TRUE)),
      "group_label" = list(
        "cell" = officer::fp_cell(background.color = "blue"),
        "text" = officer::fp_text(bold = TRUE, color = "white")),
      "title" = list("text" = officer::fp_text(bold = TRUE, font.size = 20)),
      "subtitle" = list("text" = officer::fp_text(font.size = 15)),
      "body" = list("text" = officer::fp_text(color = "red"))
    ),
    na = "-",
    title = "My formatted table!",
    subtitle = "Created with the `visOmopResults` R package.",
    caption = NULL,
    groupColumn = "cohort_name",
    groupAsColumn = FALSE,
    groupOrder = c("cohort2", "cohort1"),
    merge = "variable_name"
  )

My formatted table!

Created with the `visOmopResults` R package.

variable_name

variable_level

estimate_name

Stratifications

age_group and sex

<40 and Male

>=40 and Male

<40 and Female

>=40 and Female

cohort2

number subjects

-

N

2,059,746

1,765,568

6,870,228

3,841,037

age

-

Mean (SD)

86.9691 (0.2333)

34.0349 (4.7723)

48.2080 (7.3231)

59.9566 (6.9273)

Medications

Amoxiciline

N (%)

65,087 (40.00%)

25,802 (32.54%)

47,855 (75.71%)

76,631 (20.27%)

Ibuprofen

N (%)

65,472 (44.63%)

35,320 (64.01%)

27,026 (99.18%)

99,268 (49.56%)

cohort1

number subjects

-

N

3,721,239

5,728,534

9,082,078

2,016,819

age

-

Mean (SD)

77.7445 (1.0794)

93.4705 (7.2371)

21.2143 (4.1127)

65.1674 (8.2095)

Medications

Amoxiciline

N (%)

9,947 (33.38%)

31,627 (47.64%)

51,863 (89.22%)

66,201 (86.43%)

Ibuprofen

N (%)

5,893 (59.88%)

64,229 (97.62%)

87,627 (73.18%)

77,891 (35.67%)