Default labels (e.g. mappings for x
, y
, etc.)
will be used to search data
for more meaningful
labels, taking first available from attributes
with names in search
. Likewise, if mappings for
colour (color), fill, size, etc. (see defaults for discrete
)
indicate columns that have these defined as attributes,
an attempt is made to add a corresponding discrete scale if
one does not exist already. Values are recycled if necessary
and are specific by ordinal position to the corresponding
level of the corresponding variable. Levels are defined
in increasing priority by
sort(unique(x))
,
any guide attribute,
any factor levels,
any codelist attribute, or
any plotmath attribute.
# S3 method for decorated_ggplot
print(
x,
...,
search = getOption("yamlet_decorated_ggplot_search", c("expression", "title", "label")),
discrete = getOption("yamlet_decorated_ggplot_discrete", c("colour", "fill", "size",
"shape", "linetype", "linewidth", "alpha")),
drop = getOption("yamlet_decorated_ggplot_drop", TRUE)
)
see print.ggplot
class 'decorated_ggplot' from ggplot.decorated
ignored
attribute names from which to seek label substitutes
discrete aesthetics to map from data decorations where available
should unused factor levels be omitted from data-driven discrete scales?
Other decorated_ggplot:
ggplot.decorated()
,
ggplot_build.decorated_ggplot()