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 three table formats: <tibble>,
<gt>,
and <flextable>.
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 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 customizing styles.Additional Table Formatting Functions: The
formatfunction set provides more granular control over specific table elements, enabling advanced customization 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",
"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 (mm)" = "<bill_length_mm>",
"Bill depth (mm)" = "<bill_depth_mm>",
"Flipper length (mm)" = "<flipper_length_mm>"
),
header = c("species", "island"),
groupColumn = "sex",
type = "flextable",
hide = c("year", "estimate_type")
)Sex |
Estimate name |
Species |
||||
|---|---|---|---|---|---|---|
Adelie |
Gentoo |
Chinstrap |
||||
Island | ||||||
Biscoe |
Torgersen |
Dream |
Biscoe |
Dream |
||
female |
||||||
Bill length (mm) |
36.64 |
36.61 |
36.27 |
45.30 |
46.00 |
|
Bill depth (mm) |
17.22 |
17.40 |
17.79 |
14.13 |
17.30 |
|
Flipper length (mm) |
186.56 |
190.00 |
189.00 |
213.00 |
192.67 |
|
male |
||||||
Bill length (mm) |
40.76 |
40.92 |
40.11 |
48.54 |
51.40 |
|
Bill depth (mm) |
19.03 |
18.84 |
18.89 |
15.70 |
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 | 3,203,857 | 4,023,282 |
| age | - | Mean (SD) | 22.56 (3.89) | 30.08 (4.61) | |
| Medications | Amoxiciline | N% | 54,248 (92.00) | 54,460 (40.07) | |
| Ibuprofen | N% | 21,843 (56.69) | 41,836 (25.30) | ||
| cohort1; Female | |||||
| mock | number subjects | - | N | 1,956,698 | 4,035,381 |
| age | - | Mean (SD) | 63.65 (3.15) | 47.90 (1.75) | |
| Medications | Amoxiciline | N% | 27,860 (21.32) | 44,670 (67.18) | |
| Ibuprofen | N% | 66,887 (91.88) | 50,765 (86.74) | ||
| cohort2; Male | |||||
| mock | number subjects | - | N | 7,353,196 | 1,959,567 |
| age | - | Mean (SD) | 68.02 (0.65) | 49.88 (3.54) | |
| Medications | Amoxiciline | N% | 37,697 (71.83) | 55,991 (24.13) | |
| Ibuprofen | N% | 1,148 (16.46) | 88,825 (66.32) | ||
| cohort2; Female | |||||
| mock | number subjects | - | N | 9,805,397 | 7,415,215 |
| age | - | Mean (SD) | 64.17 (8.25) | 66.03 (2.74) | |
| Medications | Amoxiciline | N% | 85,708 (54.70) | 38,481 (83.48) | |
| Ibuprofen | N% | 99,635 (85.66) | 50,019 (92.65) | ||
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.
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 | 3,203,857 | 7,353,196 |
| age | - | Mean (SD) | 22.56 (3.89) | 68.02 (0.65) |
| Medications | Amoxiciline | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) |
| Ibuprofen | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) | |
| >=40; Male | ||||
| number subjects | - | N | 4,023,282 | 1,959,567 |
| age | - | Mean (SD) | 30.08 (4.61) | 49.88 (3.54) |
| Medications | Amoxiciline | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) |
| Ibuprofen | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) | |
| <40; Female | ||||
| number subjects | - | N | 1,956,698 | 9,805,397 |
| age | - | Mean (SD) | 63.65 (3.15) | 64.17 (8.25) |
| Medications | Amoxiciline | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) |
| Ibuprofen | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) | |
| >=40; Female | ||||
| number subjects | - | N | 4,035,381 | 7,415,215 |
| age | - | Mean (SD) | 47.90 (1.75) | 66.03 (2.74) |
| Medications | Amoxiciline | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) |
| Ibuprofen | N% | <1,000,000 (<1,000,000) | <1,000,000 (<1,000,000) | |
Styling tables
Tables displayed in visOmopResults() follow a default
style, but customization 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 <gt> and
<flextable>
To inspect the code for the default styles of <gt>
and <flextable>, use these functions:
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())
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 customize 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 1956698
#> 2 age NA mean numeric 63.6465614894405
#> 3 age NA sd numeric 3.15241752425209
#> 4 Medications Amoxiciline count integer 27860
#> 5 Medications Amoxiciline percentage percentage 21.3172711199149
#> 6 Medications Ibuprofen count integer 66887
#> 7 Medications Ibuprofen percentage percentage 91.88032145611941.1) Estimate values
The formatEstimateValue() function allows you to specify
the number of decimals for different estimate_types or
estimate_names, as well as customize 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 1,956,698
#> 2 age NA mean numeric 63.6466
#> 3 age NA sd numeric 3.1524
#> 4 Medications Amoxiciline count integer 27,860
#> 5 Medications Amoxiciline percentage percentage 21.32
#> 6 Medications Ibuprofen count integer 66,887
#> 7 Medications Ibuprofen percentage percentage 91.88As you can see, the estimates now reflect the specified formatting rules.
1.2) 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 1,956,698
#> 2 age NA Mean (SD) character 63.6466 (3.1524)
#> 3 Medications Amoxiciline N (%) character 27,860 (21.32%)
#> 4 Medications Ibuprofen N (%) character 66,887 (91.88%)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 3,203,857
#> 2 cohort2 number subjec… NA N 7,353,196
#> 3 cohort1 age NA Mean (SD) 22.5563 (3.8918)
#> 4 cohort2 age NA Mean (SD) 68.0163 (0.6522)
#> 5 cohort1 Medications Amoxiciline N (%) 54,248 (92.00%)
#> 6 cohort2 Medications Amoxiciline N (%) 37,697 (71.83%)
#> # 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 | 7,353,196 | 1,959,567 | 9,805,397 | 7,415,215 |
| age | - | Mean (SD) | 68.0163 (0.6522) | 49.8846 (3.5421) | 64.1679 (8.2520) | 66.0284 (2.7382) |
| Medications | Amoxiciline | N (%) | 37,697 (71.83%) | 55,991 (24.13%) | 85,708 (54.70%) | 38,481 (83.48%) |
| Ibuprofen | N (%) | 1,148 (16.46%) | 88,825 (66.32%) | 99,635 (85.66%) | 50,019 (92.65%) | |
| cohort1 | ||||||
| number subjects | - | N | 3,203,857 | 4,023,282 | 1,956,698 | 4,035,381 |
| age | - | Mean (SD) | 22.5563 (3.8918) | 30.0831 (4.6119) | 63.6466 (3.1524) | 47.9025 (1.7468) |
| Medications | Amoxiciline | N (%) | 54,248 (92.00%) | 54,460 (40.07%) | 27,860 (21.32%) | 44,670 (67.18%) |
| Ibuprofen | N (%) | 21,843 (56.69%) | 41,836 (25.30%) | 66,887 (91.88%) | 50,765 (86.74%) | |
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 customize the appearance of different parts of the table to
better suit your needs.
Customizing 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 | 7,353,196 | 1,959,567 | 9,805,397 | 7,415,215 |
| age | - | Mean (SD) | 68.0163 (0.6522) | 49.8846 (3.5421) | 64.1679 (8.2520) | 66.0284 (2.7382) |
| Medications | Amoxiciline | N (%) | 37,697 (71.83%) | 55,991 (24.13%) | 85,708 (54.70%) | 38,481 (83.48%) |
| Ibuprofen | N (%) | 1,148 (16.46%) | 88,825 (66.32%) | 99,635 (85.66%) | 50,019 (92.65%) | |
| cohort1 | ||||||
| number subjects | - | N | 3,203,857 | 4,023,282 | 1,956,698 | 4,035,381 |
| age | - | Mean (SD) | 22.5563 (3.8918) | 30.0831 (4.6119) | 63.6466 (3.1524) | 47.9025 (1.7468) |
| Medications | Amoxiciline | N (%) | 54,248 (92.00%) | 54,460 (40.07%) | 27,860 (21.32%) | 44,670 (67.18%) |
| Ibuprofen | N (%) | 21,843 (56.69%) | 41,836 (25.30%) | 66,887 (91.88%) | 50,765 (86.74%) | |
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. | |||||||
cohort_name |
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 |
7,353,196 |
1,959,567 |
9,805,397 |
7,415,215 |
|
age |
- |
Mean (SD) |
68.0163 (0.6522) |
49.8846 (3.5421) |
64.1679 (8.2520) |
66.0284 (2.7382) |
|
Medications |
Amoxiciline |
N (%) |
37,697 (71.83%) |
55,991 (24.13%) |
85,708 (54.70%) |
38,481 (83.48%) |
|
Ibuprofen |
N (%) |
1,148 (16.46%) |
88,825 (66.32%) |
99,635 (85.66%) |
50,019 (92.65%) |
||
cohort1 |
|||||||
number subjects |
- |
N |
3,203,857 |
4,023,282 |
1,956,698 |
4,035,381 |
|
age |
- |
Mean (SD) |
22.5563 (3.8918) |
30.0831 (4.6119) |
63.6466 (3.1524) |
47.9025 (1.7468) |
|
Medications |
Amoxiciline |
N (%) |
54,248 (92.00%) |
54,460 (40.07%) |
27,860 (21.32%) |
44,670 (67.18%) |
|
Ibuprofen |
N (%) |
21,843 (56.69%) |
41,836 (25.30%) |
66,887 (91.88%) |
50,765 (86.74%) |
||