The fmt()
function provides a way to execute custom formatting
functionality with raw data values in a way that can consider all output
contexts.
Along with the columns
and rows
arguments that provide some precision in
targeting data cells, the fns
argument allows you to define one or more
functions for manipulating the raw data.
If providing a single function to fns
, the recommended format is in the
form: fns = function(x) ...
. This single function will format the targeted
data cells the same way regardless of the output format (e.g., HTML, LaTeX,
RTF).
If you require formatting of x
that depends on the output format, a list of
functions can be provided for the html
, latex
, rtf
, and default
contexts. This can be in the form of fns = list(html = function(x) ..., latex = function(x) ..., default = function(x) ...)
. In this
multiple-function case, we recommended including the default
function as a
fallback if all contexts aren't provided.
Usage
fmt(data, columns = everything(), rows = everything(), compat = NULL, fns)
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()
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 undergo formatting. The defaulteverything()
results in all rows incolumns
being formatted. Alternatively, we can supply a vector of row captions 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
).- compat
Formatting compatibility
vector<character>
// default:NULL
(optional
)An optional vector that provides the compatible classes for the formatting. By default this is
NULL
.- fns
Formatting functions
function|list of functions
// requiredEither a single formatting function or a named list of functions.
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 formatting function
will be skipped over, like character
values and numeric fmt_*()
functions. So it's safe to select all columns with a particular formatting
function (only those values that can be formatted will be formatted), but,
you may not want that. One strategy is to format the bulk of cell values with
one formatting function and then constrain the columns for later passes with
other types of formatting (the last formatting 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
formatting on values in the column or another column, or, you'd like to use a
more complex predicate expression.
Examples
Use the exibble
dataset to create a gt table. Using the fmt()
function, we'll format the numeric values in the num
column with a function
supplied to the fns
argument. This supplied function will take values in
the column (x
), multiply them by 1000, and exclose them in single quotes.
exibble |>
dplyr::select(-row, -group) |>
gt() |>
fmt(
columns = num,
fns = function(x) {
paste0("'", x * 1000, "'")
}
)
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()
,
sub_large_vals()
,
sub_missing()
,
sub_small_vals()
,
sub_values()
,
sub_zero()