fmt_passthrough | R Documentation |
We can format values with fmt_passthrough()
, which does little
more than: (1) coercing to character
(as all the fmt_*()
functions do),
and (2) applying decorator text via the pattern
argument (the default is to
apply nothing). This formatting function is useful when don't want to modify
the input data other than to decorate it within a pattern.
fmt_passthrough(
data,
columns = everything(),
rows = everything(),
escape = TRUE,
pattern = "{x}"
)
data |
The gt table data object
This is the gt table object that is commonly created through use of the
|
columns |
Columns to target
Can either be a series of column names provided in |
rows |
Rows to target
In conjunction with |
escape |
Text escaping
An option to escape 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 during rendering. By default this is set to |
pattern |
Specification of the formatting pattern
A formatting pattern that allows for decoration of the formatted value. The
formatted value is represented by the |
An object of class gt_tbl
.
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.
from_column()
helper functionfrom_column()
can be used with certain arguments of fmt_passthrough()
to
obtain varying parameter values from a specified column within the table.
This means that each row could be formatted a little bit differently. These
arguments provide support for from_column()
:
escape
pattern
Please note that for both of the aforementioned arguments, a from_column()
call needs to reference a column that has data of the correct type (this is
different for each argument). Additional columns for parameter values can be
generated with cols_add()
(if not already present). Columns that contain
parameter data can also be hidden from final display with cols_hide()
.
Finally, there is no limitation to how many arguments the from_column()
helper is applied so long as the arguments belong to this closed set.
Let's use the exibble
dataset to create a single-column gt table
(with only the char
column). Now we can pass the data in that column
through the 'non-formatter' that is fmt_passthrough()
. While the the
function doesn't do any explicit formatting it has a feature common to all
other formatting functions: the pattern
argument. So that's what we'll use
in this example, applying a simple pattern to the non-NA
values that adds
an "s"
character.
exibble |> dplyr::select(char) |> gt() |> fmt_passthrough( rows = !is.na(char), pattern = "{x}s" )
3-28
v0.2.0.5
(March 31, 2020)
Other data formatting functions:
data_color()
,
fmt()
,
fmt_auto()
,
fmt_bins()
,
fmt_bytes()
,
fmt_chem()
,
fmt_country()
,
fmt_currency()
,
fmt_date()
,
fmt_datetime()
,
fmt_duration()
,
fmt_email()
,
fmt_engineering()
,
fmt_flag()
,
fmt_fraction()
,
fmt_icon()
,
fmt_image()
,
fmt_index()
,
fmt_integer()
,
fmt_markdown()
,
fmt_number()
,
fmt_partsper()
,
fmt_percent()
,
fmt_roman()
,
fmt_scientific()
,
fmt_spelled_num()
,
fmt_tf()
,
fmt_time()
,
fmt_units()
,
fmt_url()
,
sub_large_vals()
,
sub_missing()
,
sub_small_vals()
,
sub_values()
,
sub_zero()
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