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.
Usage
sub_values(
data,
columns = everything(),
rows = everything(),
values = NULL,
pattern = NULL,
fn = NULL,
replacement = NULL,
escape = TRUE
)
Arguments
- data
The gt table data object
obj:<gt_tbl>
// requiredThis 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 includestarts_with()
,ends_with()
,contains()
,matches()
,one_of()
,num_range()
, andeverything()
.- 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 defaulteverything()
results in all rows incolumns
being formatted. Alternatively, we can supply a vector of row IDs withinc()
, a vector of row indices, or a select helper function. Examples of select helper functions includestarts_with()
,ends_with()
,contains()
,matches()
,one_of()
,num_range()
, andeverything()
. We can also use expressions to filter down to the rows we need (e.g.,[colname_1] > 100 & [colname_2] < 50
).- values
Values to match on
scalar<character|numeric|integer>
// default:NULL
(optional
)The specific value or values that should be replaced with a
replacement
value. Ifpattern
is also supplied thenvalues
will be ignored.- pattern
Regex pattern to match with
scalar<character>
// default:NULL
(optional
)A regex pattern that can target solely those values in
character
-based columns. Ifvalues
is also supplied,pattern
will take precedence.- fn
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 ofx
(i.e., number of rows in the input table). If either ofvalues
orpattern
is also supplied,fn
will take precedence.- replacement
Replacement value for matches
scalar<character|numeric|integer>
// default:NULL
(optional
)The replacement value for any cell values matched by either
values
orpattern
. Must be a character or numeric vector of length 1.- escape
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 toFALSE
would be useful in the case where replacement text is crafted for a specific output format in mind.
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 NA
s 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 create an input table with three columns. This contains an assortment
of values that could potentially undergo some substitution via
sub_values()
.
tbl <-
dplyr::tibble(
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]
)
tbl
#> # 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")
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")
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() |>
sub_values(
fn = function(x) x >= 0 & x < 50,
replacement = "Between 0 and 50"
)
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()