sub_small_vals | R Documentation |
Wherever there is numerical data that are very small in value, replacement
text may be better for explanatory purposes. sub_small_vals()
allows for
this replacement through specification of a threshold
, a small_pattern
,
and the sign of the values to be considered. The substitution will occur for
those values found to be between 0
and the threshold value. This is
possible for small positive and small negative values (this can be explicitly
set by the sign
option). Note that the interval does not include the 0
or
the threshold
value. Should you need to include zero values, use sub_zero()
.
sub_small_vals(
data,
columns = everything(),
rows = everything(),
threshold = 0.01,
small_pattern = if (sign == "+") "<{x}" else md("<*abs*(-{x})"),
sign = "+"
)
data |
The gt table data object
This is the gt table object that is commonly created through use of the
|
columns |
Columns to target
The columns to which substitution operations are constrained. Can either
be a series of column names provided in |
rows |
Rows to target
In conjunction with |
threshold |
Threshold value
The threshold value with which values should be considered small enough for replacement. |
small_pattern |
Pattern specification for small values
The pattern text to be used in place of the suitably small values in the rendered table. |
sign |
Consider positive or negative values?
The sign of the numbers to be considered in the replacement. By default, we
only consider positive values ( |
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 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.
Let's generate a simple, single-column tibble that contains an assortment of values that could potentially undergo some substitution.
tbl <- dplyr::tibble(num = c(10^(-4:2), 0, NA)) tbl #> # A tibble: 9 x 1 #> num #> <dbl> #> 1 0.0001 #> 2 0.001 #> 3 0.01 #> 4 0.1 #> 5 1 #> 6 10 #> 7 100 #> 8 0 #> 9 NA
The tbl
contains a variety of smaller numbers and some might be small
enough to reformat with a threshold value. With sub_small_vals()
we can
do just that:
tbl |> gt() |> fmt_number(columns = num) |> sub_small_vals()
Small and negative values can also be handled but they are handled specially
by the sign
parameter. Setting that to "-"
will format only the small,
negative values.
tbl |> dplyr::mutate(num = -num) |> gt() |> fmt_number(columns = num) |> sub_small_vals(sign = "-")
You don't have to settle with the default threshold
value or the default
replacement pattern (in small_pattern
). This can be changed and the
"{x}"
in small_pattern
(which uses the threshold
value) can even be
omitted.
tbl |> gt() |> fmt_number(columns = num) |> sub_small_vals( threshold = 0.0005, small_pattern = "smol" )
3-33
v0.6.0
(May 24, 2022)
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_passthrough()
,
fmt_percent()
,
fmt_roman()
,
fmt_scientific()
,
fmt_spelled_num()
,
fmt_tf()
,
fmt_time()
,
fmt_units()
,
fmt_url()
,
sub_large_vals()
,
sub_missing()
,
sub_values()
,
sub_zero()
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