Substitute large values in the table body

Wherever there are numerical data that are very large in value, replacement text may be better for explanatory purposes. The sub_large_vals() function allows for this replacement through specification of a threshold, a large_pattern, and the sign (positive or negative) of the values to be considered.

Usage

sub_large_vals(
  data,
  columns = everything(),
  rows = everything(),
  threshold = 1e+12,
  large_pattern = ">={x}",
  sign = "+"
)

Arguments

data

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

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

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).

threshold

Threshold value

scalar<numeric|integer> // default: 1E12

The threshold value with which values should be considered large enough for replacement.

large_pattern

Pattern specification for large values

scalar<character> // default: ">={x}"

The pattern text to be used in place of the suitably large values in the rendered table.

sign

Consider positive or negative values?

scalar<character> // default: "+"

The sign of the numbers to be considered in the replacement. By default, we only consider positive values ("+"). The other option ("-") can be used to consider only negative values.

Value

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.

Examples

Let's generate a simple, single-column tibble that contains an assortment of values that could potentially undergo some substitution.

tbl <- dplyr::tibble(num = c(0, NA, 10^(8:14)))

tbl
#> # A tibble: 9 x 1
#>     num
#>   <dbl>
#> 1  0   
#> 2 NA   
#> 3  1e 8
#> 4  1e 9
#> 5  1e10
#> 6  1e11
#> 7  1e12
#> 8  1e13
#> 9  1e14

The tbl object contains a variety of larger numbers and some might be larger enough to reformat with a threshold value. With sub_large_vals() we can do just that:

tbl |>
  gt() |>
  fmt_number(columns = num) |>
  sub_large_vals()

This image of a table was generated from the first code example in the `sub_large_vals()` help file.

Large negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the large values that are negative. Notice that with the default large_pattern value of ">={x}" the ">=" is automatically changed to "<=".

tbl |>
  dplyr::mutate(num = -num) |>
  gt() |>
  fmt_number(columns = num) |>
  sub_large_vals(sign = "-")

This image of a table was generated from the second code example in the `sub_large_vals()` help file.

You don't have to settle with the default threshold value or the default replacement pattern (in large_pattern). This can be changed and the "{x}" in large_pattern (which uses the threshold value) can even be omitted.

tbl |>
  gt() |>
  fmt_number(columns = num) |>
  sub_large_vals(
    threshold = 5E10,
    large_pattern = "hugemongous"
  )

This image of a table was generated from the third code example in the `sub_large_vals()` help file.

Function ID

3-30

Function Introduced

v0.6.0 (May 24, 2022)