sub_values: Substitute targeted values in the table body

View source: R/substitution.R

sub_valuesR Documentation

Substitute targeted values in the table body


Should you need to replace specific cell values with custom text, the sub_values() function can be good choice. We can target cells for replacement though value, regex, and custom matching rules.


  columns = everything(),
  rows = everything(),
  values = NULL,
  pattern = NULL,
  fn = NULL,
  replacement = NULL,
  escape = TRUE



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).


Values to match on

⁠scalar<character|numeric|integer>⁠ // default: NULL (optional)

The specific value or values that should be replaced with a replacement value. If pattern is also supplied then values will be ignored.


Regex pattern to match with

⁠scalar<character>⁠ // default: NULL (optional)

A regex pattern that can target solely those values in character-based columns. If values is also supplied, pattern will take precedence.


Function to return logical values

⁠<function>⁠ // default: NULL (optional)

A supplied function that operates on x (the data in a column) and should return a logical vector that matches the length of x (i.e., number of rows in the input table). If either of values or pattern is also supplied, fn will take precedence.


Replacement value for matches

⁠scalar<character|numeric|integer>⁠ // default: NULL (optional)

The replacement value for any cell values matched by either values or pattern. Must be a character or numeric vector of length 1.


Text escaping

⁠scalar<logical>⁠ // default: TRUE

An option to escape replacement text according to the final output format of the table. For example, if a LaTeX table is to be generated then LaTeX escaping would be performed on the replacements during rendering. By default this is set to TRUE but setting to FALSE would be useful in the case where replacement text is crafted for a specific output format in mind.


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.


Let's create an input table with three columns. This contains an assortment of values that could potentially undergo some substitution via sub_values().

tbl <-
    num_1 = c(-0.01, 74, NA, 0, 500, 0.001, 84.3),
    int_1 = c(1L, -100000L, 800L, 5L, NA, 1L, -32L),
    lett = LETTERS[1:7]

#> # A tibble: 7 x 3
#>     num_1   int_1 lett 
#>     <dbl>   <int> <chr>
#> 1  -0.01        1 A    
#> 2  74     -100000 B    
#> 3  NA         800 C    
#> 4   0           5 D    
#> 5 500          NA E    
#> 6   0.001       1 F    
#> 7  84.3       -32 G

Values in the table body cells can be replaced by specifying which values should be replaced (in values) and what the replacement value should be. It's okay to search for numerical or character values across all columns and the replacement value can also be of the numeric or character types.

tbl |>
  gt() |>
  sub_values(values = c(74, 500), replacement = 150) |>
  sub_values(values = "B", replacement = "Bee") |>
  sub_values(values = 800, replacement = "Eight hundred")
This image of a table was generated from the first code example in the `sub_values()` help file.

We can also use the pattern argument to target cell values for replacement in character-based columns.

tbl |>
  gt() |>
  sub_values(pattern = "A|C|E", replacement = "Ace")
This image of a table was generated from the second code example in the `sub_values()` help file.

For the most flexibility, it's best to use the fn argument. With that you need to ensure that the function you provide will return a logical vector when invoked on a column of cell values, taken as x (and, the length of that vector must match the length of x).

tbl |>
  gt() |>
    fn = function(x) x >= 0 & x < 50,
    replacement = "Between 0 and 50"
This image of a table was generated from the third code example in the `sub_values()` help file.

Function ID


Function Introduced

v0.8.0 (November 16, 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_missing(), sub_small_vals(), sub_zero()

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