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:
Main Table Functions: Comprehensive functions like
visOmopTable()andvisTable()allow users to fully format tables, including specifying headers, grouping columns, and customising styles.Additional Table Formatting Functions: The
formatfunction set provides more granular control over specific table elements, enabling advanced customisation beyond the main functions.
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 195We 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:
Automatic splitting: The result object is always processed using the
splitAll()function. Thereby, column names to use in other arguments must be based on the split result.settingsColumnargument: Use this argument to specify which settings should be displayed in the main table. The columns specified here can also be referenced in other arguments such asheader,rename, andgroupColumn.headerargument: accepts specific<summarised_result>inputs, in addition to its typical usage as invisTable(). For example, use “strata” in the header to display all variables instrata_name, or use “settings” to show all settings specified insettingsColumns.Hidden columns: result_id and estimate_type columns are always hidden as they serve as helper columns for internal processes.
Suppressed estimates: if the result object has been processed with suppress(), obscured estimates can be displayed as the default
navalue or as “<{minCellCount}” with the corresponding minimum count value used. This can be controlled using theshowMinCellCountargument.
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.
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 (only for gt and
flextable).
To inspect the code for the default styles of the different table
types supported, you can use the tableStyle() function as
showed next. To know more about styling with these options, please refer
to each of the table packages.
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)
tableStyle(type = "reactable")
#> list(defaultColDef = reactable::colDef(sortable = TRUE, filterable = TRUE,
#> resizable = TRUE), defaultColGroup = NULL, defaultSortOrder = "asc",
#> defaultSorted = NULL, defaultPageSize = 10, defaultExpanded = TRUE,
#> highlight = TRUE, outlined = FALSE, bordered = FALSE, borderless = FALSE,
#> striped = TRUE, theme = NULL)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 `gtStyle()` or `flextableStyle()` to preview the default 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 73.17925118841231.1) 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 73.18As 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 withincludeHeaderName = FALSE.header_level: Labels derived from values within columns set inheader.
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%) |
|