Formatting tables"

In visOmopResults: Graphs and Tables for OMOP Results

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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() and visTable() allow users to fully format tables, including specifying headers, grouping columns, and customizing styles.

• Additional Table Formatting Functions: The format function 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)

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"
)

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")
)

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)

# 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"),
  settingsColumns = "package_name",
  type = "gt"
)

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.

• settingsColumns argument: 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 as header, rename, and groupColumn.

• header argument: accepts specific <summarised_result> inputs, in addition to its typical usage as in visTable(). For example, use "strata" in the header to display all variables in strata_name, or use "settings" to show all settings specified in settingsColumns.

• 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 na value or as "<{minCellCount}" with the corresponding minimum count value used. This can be controlled using the showMinCellCount argument.

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"
  )

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:

bind_rows(
  tibble(
    Function = "formatEstimateValue()",
    Argument = c("decimals", "decimalMark", "bigMark"),
    Description = c(
      "Number of decimals per estimate type (integer, numeric, percentage, proportion), estimate name, or all estimate values (introduce the number of decimals).",
      "Decimal separator mark.",
      "Thousand and millions separator mark."
    )
  ) ,
  tibble(
    Function = "formatEstimateName()",
    Argument = c("keepNotFormatted", "useFormatOrder"),
    Description = c(
      "Whether to keep rows not formatted.",
      "Whether to use the order in which estimate names appear in the estimateName (TRUE), or use the order in the input dataframe (FALSE)."
    )
  ),
  tibble(
    Function = "formatHeader()",
    Argument = c("delim", "includeHeaderName", "includeHeaderKey"),
    Description = c(
      "Delimiter to use to separate headers.",
      "Whether to include the column name as header.",
      "Whether to include the header key (header, header_name, header_level) before each header type in the column names."
    )
  ),
  tibble(
    Function = "formatTable()",
    Argument = c("style", "na", "title", "subtitle", "caption", "groupAsColumn", "groupOrder", "merge"),
    Description = c(
      "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",
      "How to display missing values.",
      "Title of the table, or NULL for no title.",
      "Subtitle of the table, or NULL for no subtitle.",
      "Caption for the table, or NULL for no caption. Text in markdown formatting style (e.g. ⁠*Your caption here*⁠ for caption in italics)",
      "Whether to display the group labels as a column (TRUE) or rows (FALSE).",
      "Order in which to display group labels.",
      "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."
    )
  )  
) |>
  formatTable(groupColumn = "Function")

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

tableOptions()

Styling <gt> and <flextable>

To inspect the code for the default styles of <gt> and <flextable>, use these functions:

tableStyle(type = "gt")

tableStyle(type = "flextable")

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)

1.1) 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)

As 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)

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()

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)

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)

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"
  )

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"
  )

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"
  )

visOmopResults documentation built on Sept. 24, 2024, 1:08 a.m.