fmt_auto | R Documentation |
fmt_auto()
will automatically apply formatting of various types in a way
that best suits the data table provided. The function will attempt to format
numbers such that they are condensed to an optimal width, either with
scientific notation or large-number suffixing. Currency values are detected
by currency codes embedded in the column name and formatted in the correct
way. Although the functionality here is comprehensive it's still possible to
reduce the scope of automatic formatting with the scope
argument and also
by choosing a subset of columns and rows to which the formatting will be applied.
fmt_auto(
data,
columns = everything(),
rows = everything(),
scope = c("numbers", "currency"),
lg_num_pref = c("sci", "suf"),
locale = NULL
)
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 |
scope |
Scope of automatic formatting
By default, the function will format both |
lg_num_pref |
Large-number preference
When large numbers are present, there can be a fixed preference toward how
they are formatted. Choices are scientific notation for very small and very
large values ( |
locale |
Locale identifier
An optional locale identifier that can be used for formatting values
according the locale's rules. Examples include |
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.
Use the exibble
dataset to create a gt table. Format all of the
columns automatically with the fmt_auto()
function.
exibble |> gt() |> fmt_auto()
Let's now use the countrypops
dataset to create another gt table.
We'll again use fmt_auto()
to automatically format all columns but this
time the choice will be made to opt for large-number suffixing instead of
scientific notation. This is done by using the lg_num_pref = "suf"
option.
countrypops |> dplyr::select(country_code_3, year, population) |> dplyr::filter(country_code_3 %in% c("CHN", "IND", "USA", "PAK", "IDN")) |> dplyr::filter(year > 1975 & year %% 5 == 0) |> tidyr::spread(year, population) |> dplyr::arrange(desc(`2020`)) |> gt(rowname_col = "country_code_3") |> fmt_auto(lg_num_pref = "suf")
3-29
v0.9.0
(Mar 31, 2023)
Other data formatting functions:
data_color()
,
fmt()
,
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_passthrough()
,
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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