With numeric values in a gt table we can transform those to index values, usually based on letters. These characters can be derived from a specified locale and they are intended for ordering (often leaving out characters with diacritical marks).
Usage
fmt_index(
data,
columns = everything(),
rows = everything(),
case = c("upper", "lower"),
index_algo = c("repeat", "excel"),
pattern = "{x}",
locale = NULL
)
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
).- case
Use uppercase or lowercase letters
singl-kw:[upper|lower]
--- default:"upper"
Should the resulting index characters be rendered as uppercase (
"upper"
) or lowercase ("lower"
) letters? By default, this is set to"upper"
.- index_algo
Indexing algorithm
singl-kw:[repeat|excel]
--- default:"repeat"
The indexing algorithm handles the recycling of the index character set. By default, the
"repeat"
option is used where characters are doubled, tripled, and so on, when moving past the character set limit. The alternative is the"excel"
option, where Excel-based column naming is adapted and used here (e.g.,[..., Y, Z, AA, AB, ...]
).- pattern
Specification of the formatting pattern
scalar<character>
--- default:"{x}"
A formatting pattern that allows for decoration of the formatted value. The formatted value is represented by the
{x}
(which can be used multiple times, if needed) and all other characters will be interpreted as string literals.- locale
Locale identifier
scalar<character>
--- default:NULL
(optional
)An optional locale identifier that can be used for formatting values according the locale's rules. Examples include
"en"
for English (United States) and"fr"
for French (France). We can use theinfo_locales()
function as a useful reference for all of the locales that are supported. A locale ID can be also set in the initialgt()
function call (where it would be used automatically by any function with alocale
argument) but alocale
value provided here will override that global locale.
Compatibility of formatting function with data values
The fmt_index()
formatting function is compatible with body cells that are
of the "numeric"
or "integer"
types. Any other types of body cells are
ignored during formatting. This is to say that cells of incompatible data
types may be targeted, but there will be no attempt to format them.
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
Using a summarized version of the towny
dataset, let's create a gt
table. Here, the fmt_index()
function is used to transform incremental
integer values into capitalized letters (in the ranking
column). With
cols_merge()
that formatted column of "A"
to "E"
values is merged with
the census_div
column to create an indexed listing of census subdivisions,
here ordered by increasing total municipal population.
towny |>
dplyr::select(name, csd_type, census_div, population_2021) |>
dplyr::group_by(census_div) |>
dplyr::summarize(
population = sum(population_2021),
.groups = "drop_last"
) |>
dplyr::arrange(population) |>
dplyr::slice_head(n = 5) |>
dplyr::mutate(ranking = dplyr::row_number()) |>
dplyr::select(ranking, dplyr::everything()) |>
gt() |>
fmt_integer() |>
fmt_index(columns = ranking, pattern = "{x}.") |>
cols_merge(columns = c(ranking, census_div)) |>
cols_align(align = "left", columns = ranking) |>
cols_label(
ranking = md("Census \nSubdivision"),
population = md("Population \nin 2021")
) |>
tab_header(title = md("The smallest \ncensus subdivisions")) |>
tab_options(table.width = px(325))
See also
The vector-formatting version of this function: vec_fmt_index()
.
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_image()
,
fmt_integer()
,
fmt_markdown()
,
fmt_number()
,
fmt_partsper()
,
fmt_passthrough()
,
fmt_percent()
,
fmt_roman()
,
fmt_scientific()
,
fmt_spelled_num()
,
fmt_time()
,
fmt_url()
,
fmt()
,
sub_large_vals()
,
sub_missing()
,
sub_small_vals()
,
sub_values()
,
sub_zero()