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",
             "Body mass (g)" = "body_mass_g"),
  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("My visOmopTable", "group"),
    groupColumn = c("strata"),
    hide = c("cdm_name"),
    showMinCellCount = TRUE,
    type = "gt"
  )
My visOmopTable
Variable name Variable level Estimate name Cohort name
cohort1 cohort2
<40; Male
number subjects - N 3,721,239 2,059,746
age - Mean (SD) 77.74 (1.08) 86.97 (0.23)
Medications Amoxiciline N% <1,000,000 <1,000,000
Ibuprofen N% <1,000,000 <1,000,000
>=40; Male
number subjects - N 5,728,534 1,765,568
age - Mean (SD) 93.47 (7.24) 34.03 (4.77)
Medications Amoxiciline N% <1,000,000 <1,000,000
Ibuprofen N% <1,000,000 <1,000,000
<40; Female
number subjects - N 9,082,078 6,870,228
age - Mean (SD) 21.21 (4.11) 48.21 (7.32)
Medications Amoxiciline N% <1,000,000 <1,000,000
Ibuprofen N% <1,000,000 <1,000,000
>=40; Female
number subjects - N 2,016,819 3,841,037
age - Mean (SD) 65.17 (8.21) 59.96 (6.93)
Medications Amoxiciline N% <1,000,000 <1,000,000
Ibuprofen N% <1,000,000 <1,000,000

Styling tables

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

The table below details which format function each styling option belongs to, along with a description of each option:

Argument Description
formatEstimateValue()
decimals Number of decimals per estimate type (integer, numeric, percentage, proportion), estimate name, or all estimate values (introduce the number of decimals).
decimalMark Decimal separator mark.
bigMark Thousand and millions separator mark.
formatEstimateName()
keepNotFormatted Whether to keep rows not formatted.
useFormatOrder Whether to use the order in which estimate names appear in the estimateName (TRUE), or use the order in the input dataframe (FALSE).
formatHeader()
delim Delimiter to use to separate headers.
includeHeaderName Whether to include the column name as header.
includeHeaderKey Whether to include the header key (header, header_name, header_level) before each header type in the column names.
formatTable()
style Named list that specifies how to style the different parts of the gt or flextable table generated. Accepted style entries are: title, subtitle, header, header_name, header_level, column_name, group_label, and body. Alternatively, use 'default' to get visOmopResults style, or NULL for gt/flextable style. Keep in mind that styling code is different for gt and flextable. To see the 'deafult' gt style code use gtStyle(), and flextableStyle() for flextable default code style
na How to display missing values.
title Title of the table, or NULL for no title.
subtitle Subtitle of the table, or NULL for no subtitle.
caption Caption for the table, or NULL for no caption. Text in markdown formatting style (e.g. ⁠*Your caption here*⁠ for caption in italics)
groupAsColumn Whether to display the group labels as a column (TRUE) or rows (FALSE).
groupOrder Order in which to display group labels.
merge Names of the columns to merge vertically when consecutive row cells have identical values. Alternatively, use 'all_columns' to apply this merging to all columns, or use NULL to indicate no merging.

To view the default .options settings used in vis tables, use the following function:

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
#> 
#> $style
#> [1] "default"
#> 
#> $na
#> [1] "-"
#> 
#> $title
#> NULL
#> 
#> $subtitle
#> NULL
#> 
#> $caption
#> NULL
#> 
#> $groupAsColumn
#> [1] FALSE
#> 
#> $groupOrder
#> NULL
#> 
#> $merge
#> [1] "all_columns"

Styling tables

To inspect the code for the default styles of the different table types supported, you can use the following function:

tableStyle(type = "gt")
#> 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())

tableStyle(type = "flextable")
#> list(header = list(cell = officer::fp_cell(background.color = "#c8c8c8"), 
#>     text = officer::fp_text(bold = TRUE)), header_name = list(cell = officer::fp_cell(background.color = "#d9d9d9"), 
#>     text = officer::fp_text(bold = TRUE)), header_level = list(cell = officer::fp_cell(background.color = "#e1e1e1"), 
#>     text = officer::fp_text(bold = TRUE)), column_name = list(text = officer::fp_text(bold = TRUE)), 
#>     group_label = list(cell = officer::fp_cell(background.color = "#e9e9e9", 
#>         border = officer::fp_border(color = "gray")), text = officer::fp_text(bold = TRUE)), 
#>     title = list(text = officer::fp_text(bold = TRUE, font.size = 15)), 
#>     subtitle = list(text = officer::fp_text(bold = TRUE, font.size = 12)), 
#>     body = list())

tableStyle(type = "datatable")
#> list(caption = "caption-side: bottom; text-align: center;", scrollX = TRUE, 
#>     scrollY = 400, scroller = TRUE, deferRender = TRUE, scrollCollapse = TRUE, 
#>     fixedColumns = list(leftColumns = 1, rightColumns = 1), fixedHeader = TRUE, 
#>     pageLength = 10, lengthMenu = c(5, 10, 20, 50, 100), filter = "bottom", 
#>     searchHighlight = TRUE, rownames = FALSE)

format 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> or <flextable> object.

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 gtStyle() and flextableStyle() 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%)