sub_missing: Substitute missing values in the table body

View source: R/substitution.R

sub_missingR Documentation

Substitute missing values in the table body


Wherever there is missing data (i.e., NA values) customizable content may present better than the standard NA text that would otherwise appear. The sub_missing() function allows for this replacement through its missing_text argument (where an em dash serves as the default).


  columns = everything(),
  rows = everything(),
  missing_text = "---"



The gt table data object

⁠obj:<gt_tbl>⁠ // required

This is the gt table object that is commonly created through use of the gt() function.


Columns to target

⁠<column-targeting expression>⁠ // default: everything()

The columns to which substitution operations are constrained. Can either be a series of column names provided in c(), a vector of column indices, or a select helper function. Examples of select helper functions include starts_with(), ends_with(), contains(), matches(), one_of(), num_range(), and everything().


Rows to target

⁠<row-targeting expression>⁠ // default: everything()

In conjunction with columns, we can specify which of their rows should form a constraint for targeting operations. The default everything() results in all rows in columns being formatted. Alternatively, we can supply a vector of row IDs within c(), a vector of row indices, or a select helper function. Examples of select helper functions include starts_with(), ends_with(), contains(), matches(), one_of(), num_range(), and everything(). We can also use expressions to filter down to the rows we need (e.g., ⁠[colname_1] > 100 & [colname_2] < 50⁠).


Replacement text for NA values

⁠scalar<character>⁠ // default: "---"

The text to be used in place of NA values in the rendered table. We can optionally use the md() and html() functions to style the text as Markdown or to retain HTML elements in the text.


An object of class gt_tbl.

Targeting cells with columns and rows

Targeting of values is done through columns and additionally by rows (if nothing is provided for rows then entire columns are selected). The columns argument allows us to target a subset of cells contained in the resolved columns. We say resolved because aside from declaring column names in c() (with bare column names or names in quotes) we can use tidyselect-style expressions. This can be as basic as supplying a select helper like starts_with(), or, providing a more complex incantation like

where(~ is.numeric(.x) && max(.x, na.rm = TRUE) > 1E6)

which targets numeric columns that have a maximum value greater than 1,000,000 (excluding any NAs from consideration).

By default all columns and rows are selected (with the everything() defaults). Cell values that are incompatible with a given substitution function will be skipped over. So it's safe to select all columns with a particular substitution function (only those values that can be substituted will be), but, you may not want that. One strategy is to work on the bulk of cell values with one substitution function and then constrain the columns for later passes with other types of substitution (the last operation done to a cell is what you get in the final output).

Once the columns are targeted, we may also target the rows within those columns. This can be done in a variety of ways. If a stub is present, then we potentially have row identifiers. Those can be used much like column names in the columns-targeting scenario. We can use simpler tidyselect-style expressions (the select helpers should work well here) and we can use quoted row identifiers in c(). It's also possible to use row indices (e.g., c(3, 5, 6)) though these index values must correspond to the row numbers of the input data (the indices won't necessarily match those of rearranged rows if row groups are present). One more type of expression is possible, an expression that takes column values (can involve any of the available columns in the table) and returns a logical vector. This is nice if you want to base the substitution on values in the column or another column, or, you'd like to use a more complex predicate expression.


Use select columns from the exibble dataset to create a gt table. The NA values in different columns (here, we are using column indices in columns) will be given two variations of replacement text with two separate calls of sub_missing().

exibble |>
  dplyr::select(-row, -group) |>
  gt() |>
    columns = 1:2,
    missing_text = "missing"
  ) |>
    columns = 4:7,
    missing_text = "nothing"
This image of a table was generated from the first code example in the `sub_missing()` help file.

Function ID


Function Introduced

v0.6.0 (May 24, 2022)

See Also

Other data formatting functions: data_color(), fmt_auto(), fmt_bins(), fmt_bytes(), fmt_currency(), fmt_datetime(), fmt_date(), fmt_duration(), fmt_engineering(), fmt_flag(), fmt_fraction(), fmt_icon(), fmt_image(), fmt_index(), fmt_integer(), fmt_markdown(), fmt_number(), fmt_partsper(), fmt_passthrough(), fmt_percent(), fmt_roman(), fmt_scientific(), fmt_spelled_num(), fmt_time(), fmt_units(), fmt_url(), fmt(), sub_large_vals(), sub_small_vals(), sub_values(), sub_zero()

gt documentation built on June 22, 2024, 11:11 a.m.