tablespan

Create satisficing tables in R the formula way.

The core objective of tablespan is to:

1. provide a “good enough” approach to easily create tables by leveraging R’s formulas.
2. provide direct exports to other, more flexible table packages such as gt, flextable, and huxtable.

The following shows an example, where we define a relatively complex table header setup with a single formula. The details of the syntax will be explained below.

library(dplyr)
library(tablespan)
data("mtcars")

summarized_table <- mtcars |>
  summarise(N = n(),
            mean_hp = mean(hp),
            sd_hp = sd(hp),
            mean_wt = mean(wt),
            sd_wt = sd(wt),
            .by = c(cyl, vs))

tbl <- tablespan(data = summarized_table,
                 formula = Cylinder:cyl + Engine:vs ~
                   N +
                   (`Horse Power` = Mean:mean_hp + SD:sd_hp) +
                   (`Weight` = Mean:mean_wt + SD:sd_wt),
                 title = "Motor Trend Car Road Tests",
                 subtitle = "A table created with tablespan",
                 footnote = "Data from the infamous mtcars data set.")
#> Warning in tablespan(data = summarized_table, formula = Cylinder:cyl +
#> Engine:vs ~ : Tablespan uses tibble internally. Translating data to tibble
tbl |>
  print(use_hux = FALSE)
#> Motor Trend Car Road Tests
#> A table created with tablespan
#>                                                         
#>  |                 |     Horse Power       Weight      |
#>  | Cylinder Engine | N   Mean        SD    Mean   SD   |
#>  | -------- ------ - --  ----------- ----- ------ ---- |
#>  | 6        0      | 3   131.67      37.53 2.76   0.13 |
#>  | 4        1      | 10  81.8        21.87 2.3    0.6  |
#>  | 6        1      | 4   115.25      9.18  3.39   0.12 |
#>  | ...      ...    | ... ...         ...   ...    ...  |
#> Data from the infamous mtcars data set.

This basic table can now be easily exported to other table formats.

Table Conversion and Export

tablespan builds on the awesome packages gt, openxlsx , flextable, huxtable, and googlesheets4. This allows tablespan tables to be exported to a large number of formats:

Package	Conversion Function	Description	Export Formats
–	as_string()	Exporting the table to string can be very useful for copy-pasting the table (e.g., to send in a message or use as input to an LLM).	String
gt	as_gt()	The gt package (Great Tables) provides a grammar for creating publication-ready tables with fine-grained control over styling, formatting, and layout.	HTML, PDF, PNG, LaTeX, RTF, Word (docs)
openxlsx	as_excel()	The openxlsx package offers Excel file creation and manipulation. Great for reporting tables to end-users.	Excel (docs)
googlesheets4	as_googlesheet_request()	Using googlesheets4 (docs), tablespan tables can be directly exported into google sheets requests that can be executed with googlesheets4::request_make. Experimental; use at your own risk	google sheets
flextable	as_flextable()	flextable specializes in creating complex, publication-quality tables with extensive formatting capabilities. Integrates well with Word/PowerPoint workflows.	HTML, Word, RTF, PowerPoint, PDF, PNG (docs)
huxtable	as_huxtable()	huxtable provides a unified interface for creating tables in multiple output formats. Also provides styled printing to the console.	LaTeX, Typst, HTML, Word, Excel, RTF, markdown, console (docs)

Installation

To install tablespan from CRAN use:

install.packages("tablespan")

The development version of tablespan can be installed from GitHub with:

library(remotes)
remotes::install_github("jhorzek/tablespan")

Introduction

R has a large set of great packages that allow you to create and export tables that look exactly like you envisioned. However, sometimes you may just need a good-enough table that is easy to create and share with others. This is where tablespan can be of help.

Let’s assume that we want to share the following table:

library(dplyr)
data("mtcars")

summarized_table <- mtcars |>
  summarise(N = n(),
            mean_hp = mean(hp),
            sd_hp = sd(hp),
            mean_wt = mean(wt),
            sd_wt = sd(wt),
            .by = c(cyl, vs))

print(summarized_table, use_hux = FALSE)
#>   cyl vs  N  mean_hp    sd_hp  mean_wt     sd_wt
#> 1   6  0  3 131.6667 37.52777 2.755000 0.1281601
#> 2   4  1 10  81.8000 21.87236 2.300300 0.5982073
#> 3   6  1  4 115.2500  9.17878 3.388750 0.1162164
#> 4   8  0 14 209.2143 50.97689 3.999214 0.7594047
#> 5   4  0  1  91.0000       NA 2.140000        NA

We don’t want to share the table as is - the variable names are all a bit technical and the table could need some spanners summarizing columns. So, we want to share a table that looks something like this:

|                   | Horse Power |   Weight  |
| Cylinder | Engine | Mean  |  SD | Mean | SD |
| -------- | ------ | ----- | --- | ---- | -- |
|                   |                         |

tablespan allows us to create this table with a single formula.

Creating a Basic Table

In tablespan, the table headers are defined with a formula. For example, cyl ~ mean_hp + sd_hp defines a table with cyl as the row names and mean_hp and sd_hp as columns:

library(tablespan)
tablespan(data = summarized_table,
          formula = cyl ~ mean_hp + sd_hp) |>
  print(use_hux = FALSE)
#> Warning in tablespan(data = summarized_table, formula = cyl ~ mean_hp + :
#> Tablespan uses tibble internally. Translating data to tibble
#>                         
#>  | cyl | mean_hp sd_hp |
#>  | --- - ------- ----- |
#>  | 6   | 131.67  37.53 |
#>  | 4   | 81.8    21.87 |
#>  | 6   | 115.25  9.18  |
#>  | ... | ...     ...   |

Note that the row names (cyl) are in a separate block to the left.

Adding Spanners

Spanners are defined using braces and spanner names. For example, the following defines a spanner for mean_hp and sd_hp with the name Horsepower: cyl ~ (Horsepower = mean_hp + sd_hp):

tablespan(data = summarized_table,
          formula = cyl ~ (Horsepower = mean_hp + sd_hp)) |>
  print(use_hux = FALSE)
#> Warning in tablespan(data = summarized_table, formula = cyl ~ (Horsepower =
#> mean_hp + : Tablespan uses tibble internally. Translating data to tibble
#>                            
#>  |     | Horsepower       |
#>  | cyl | mean_hp    sd_hp |
#>  | --- - ---------- ----- |
#>  | 6   | 131.67     37.53 |
#>  | 4   | 81.8       21.87 |
#>  | 6   | 115.25     9.18  |
#>  | ... | ...        ...   |

Spanners can also be nested:

tablespan(data = summarized_table,
          formula = cyl ~ (Horsepower = (Mean = mean_hp) + (SD  = sd_hp))) |>
  print(use_hux = FALSE)
#> Warning in tablespan(data = summarized_table, formula = cyl ~ (Horsepower =
#> (Mean = mean_hp) + : Tablespan uses tibble internally. Translating data to
#> tibble
#>                            
#>  |     | Horsepower       |
#>  |     | Mean       SD    |
#>  | cyl | mean_hp    sd_hp |
#>  | --- - ---------- ----- |
#>  | 6   | 131.67     37.53 |
#>  | 4   | 81.8       21.87 |
#>  | 6   | 115.25     9.18  |
#>  | ... | ...        ...   |

Renaming Columns

Variable names in an R data.frame are often very technical (e.g., mean_hp and sd_hp). When sharing the table, we may want to replace those names. In the example above, we may want to replace mean_hp and sd_hp with “Mean” and “SD”. In tablespan renaming variables is achieved with new_name:old_name. For example, cyl ~ (Horsepower = Mean:mean_hp + SD:sd_hp) renames mean_hp to Mean and sd_hp to SD:

tablespan(data = summarized_table,
          formula = cyl ~ (Horsepower = Mean:mean_hp + SD:sd_hp)) |>
  print(use_hux = FALSE)
#> Warning in tablespan(data = summarized_table, formula = cyl ~ (Horsepower =
#> Mean:mean_hp + : Tablespan uses tibble internally. Translating data to tibble
#>                            
#>  |     | Horsepower       |
#>  | cyl | Mean       SD    |
#>  | --- - ---------- ----- |
#>  | 6   | 131.67     37.53 |
#>  | 4   | 81.8       21.87 |
#>  | 6   | 115.25     9.18  |
#>  | ... | ...        ...   |

Creating the Full Table

The combination of row names, spanners, and renaming of variables allows creating the full table:

library(dplyr)
library(tablespan)
data("mtcars")

summarized_table <- mtcars |>
  summarise(N = n(),
            mean_hp = mean(hp),
            sd_hp = sd(hp),
            mean_wt = mean(wt),
            sd_wt = sd(wt),
            .by = c(cyl, vs))

tbl <- tablespan(data = summarized_table,
                 formula = Cylinder:cyl + Engine:vs ~
                   N +
                   (`Horse Power` = Mean:mean_hp + SD:sd_hp) +
                   (`Weight` = Mean:mean_wt + SD:sd_wt),
                 title = "Motor Trend Car Road Tests",
                 subtitle = "A table created with tablespan",
                 footnote = "Data from the infamous mtcars data set.")
#> Warning in tablespan(data = summarized_table, formula = Cylinder:cyl +
#> Engine:vs ~ : Tablespan uses tibble internally. Translating data to tibble

print(tbl, use_hux = FALSE)
#> Motor Trend Car Road Tests
#> A table created with tablespan
#>                                                         
#>  |                 |     Horse Power       Weight      |
#>  | Cylinder Engine | N   Mean        SD    Mean   SD   |
#>  | -------- ------ - --  ----------- ----- ------ ---- |
#>  | 6        0      | 3   131.67      37.53 2.76   0.13 |
#>  | 4        1      | 10  81.8        21.87 2.3    0.6  |
#>  | 6        1      | 4   115.25      9.18  3.39   0.12 |
#>  | ...      ...    | ... ...         ...   ...    ...  |
#> Data from the infamous mtcars data set.

Tables without row names

Using 1 on the left hand side of the formula creates a table without row names. For example, 1 ~ (Horsepower = Mean:mean_hp + SD:sd_hp) defines:

tablespan(data = summarized_table,
          formula = 1 ~ (Horsepower = Mean:mean_hp + SD:sd_hp)) |>
  print(use_hux = FALSE)
#> Warning in tablespan(data = summarized_table, formula = 1 ~ (Horsepower =
#> Mean:mean_hp + : Tablespan uses tibble internally. Translating data to tibble
#>                      
#>  | Horsepower       |
#>  | Mean       SD    |
#>  | ---------- ----- |
#>  | 131.67     37.53 |
#>  | 81.8       21.87 |
#>  | 115.25     9.18  |
#>  | ...        ...   |

Exporting to Excel

Tables created with tablespan can now be translated to xlsx tables with openxlsx using the as_excel function (alternatively, tables can also be exported to huxtable and then exported to openxlsx; see ?huxtable::as_Workbook):

# as_excel creates an openxlsx workbook
wb <- as_excel(tbl = tbl)

# Save the workbook as an xlsx file:
# openxlsx::saveWorkbook(wb,
#                        file = "cars.xlsx", 
#                        overwrite = TRUE)

Exporting to HTML, PDF, PNG, LaTeX, RTF, Word, PowerPoint, Markdown, or Console

Tables created with tablespan can also be exported to a variety of other, more flexible table packages. gt which allows saving as HTML, LaTeX, or RTF file. To this end, we simply have to call as_gt on our table:

# Translate to gt:
gt_tbl <- as_gt(tbl = tbl)
gt_tbl

Styling

tablespan allows adding styles to tables that are automatically exported to all exported tables (gt, openxlsx, flextable, and huxtable). The workflow is heavily inspired by gt.

All functions used to style tablespan tables start with style_:

• style_title: Adapt the style of the title
• style_subtitle: Adapt the style of the subtitle
• style_header: Adapt the style of the header
• style_header_cells: Stlye the header cells in openxlsx. Used to create the borders around header cells (only relevant for openxlsx exports)
• style_column: Add styling to the body of the table
• style_footnote: Adapt the style of the footnotes
• style_vline: Adapt the style of the vertical lines in the table. Only relevant for openxlsx
• style_hline: Adapt the style of the horizontal lines in the table. Only relevant for openxlsx

Styling the title and subtitle

Use the style_title and style_subtitle options to style title and subtitle:

tbl |> 
  style_title(background_color = "#000000",
              text_color = "#ffffff",
              bold = TRUE,
              italic = TRUE) |> 
  style_subtitle(italic = TRUE) |> 
  as_gt()

All of the styles applied in the following would also be exported to .xlsx files when using as_excel and saving the workbook with openxlsx.

Styling the header

To adapt the header, use style_header:

tbl |> 
  style_header(background_color = "#000000",
               text_color = "#ffffff",
               bold = TRUE,
               italic = TRUE) |> 
  as_gt()

Styling the body

The body is adapted with style_column. This function also allows styling only specific cells in the body or styling multiple columns at once. Columns are selected with tidy expressions. Please note that you will have to reference the columns with their respective names in the raw data set, not the names shown in the spanner:

tbl |> 
  style_column(
    columns = starts_with("mean_"),
    rows = 2:3,
    background_color = "#000000",
    text_color = "#ffffff",
    bold = TRUE,
    italic = TRUE) |> 
  as_gt()

Styling the footnote

tbl |> 
  style_footnote(
    background_color = "#000000",
    text_color = "#ffffff",
    bold = TRUE,
    italic = TRUE) |> 
  as_gt()

Formatting

In addition to applying specific styles to the table, you can also adapt the number formatting.

tbl |> 
  format_column(
        columns = dplyr::where(is.double),
        rows = 2:3,
        fmt = format_number(decimals = 1)
      ) |> 
  as_gt()

Adapting Great Tables

The gt package provides a wide range of functions to adapt the style of the table created with as_gt. For instance, opt_stylize adds a pre-defined style to the entire table:

gt_tbl |> 
  gt::opt_stylize(style = 6,
                  color = 'gray')

When adapting the gt object, there is an important detail to keep in mind: To ensure that each table spanner has a unique ID, tablespan will create IDs that differ from the text shown in the spanner. To demonstrate this, Let’s assume that we want to add a spanner above Horse Power and Weight:

gt_tbl |> 
  gt::tab_spanner(label = "New Spanner", 
                  spanners = c("Horse Power", "Weight"))
#> Error in `gt::tab_spanner()`:
#> ! One or more spanner ID(s) supplied in `spanners` (Horse Power and
#>   Weight), for the new spanner with the ID `New Spanner` doesn't belong to any
#>   existing spanners.

This will throw an error because the spanner IDs are different from the spanner labels. To get the spanner IDs, use gt::tab_info():

gt_tbl |> 
  gt::tab_info()

The IDs for the spanners can be found at the very bottom. To add another spanner above Horse Power and Weight, we have to use these IDs:

gt_tbl |> 
  gt::tab_spanner(label = "New Spanner", 
                  spanners = c("__BASE_LEVEL__Horse Power", 
                               "__BASE_LEVEL__Weight"))

References

• gt: Iannone R, Cheng J, Schloerke B, Hughes E, Lauer A, Seo J, Brevoort K, Roy O (2024). gt: Easily Create Presentation-Ready Display Tables. R package version 0.11.1.9000, https://github.com/rstudio/gt, https://gt.rstudio.com.
• openxlsx: Schauberger P, Walker A (2023). openxlsx: Read, Write and Edit xlsx Files. R package version 4.2.5.2, https://ycphs.github.io/openxlsx/.
• googlesheets4: Bryan, J. (2025). googlesheets4: Access Google Sheets using the Sheets API V4. R package version 1.1.2, https://CRAN.R-project.org/package=googlesheets4.
• flextable: Gohel D, Skintzos P (2025). flextable: Functions for Tabular Reporting. R package version 0.9.10, https://CRAN.R-project.org/package=flextable
• huxtable: Hugh-Jones D (2025). huxtable: Easily Create and Style Tables for LaTeX, HTML and Other Formats. R package version 5.8.0, https://CRAN.R-project.org/package=huxtable
• tables: Murdoch D (2024). tables: Formula-Driven Table Generation. R package version 0.9.31, https://dmurdoch.github.io/tables/.