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ggplot2 support for multivariate functional data
(tf_mv columns, see tf::tfd_mv()): map them
with aes(tf = ...) in tf_ggplot(), with
type = "facet" (value-vs-arg curves, one panel per output
dimension) or type = "trajectory" (planar curves x(t) vs
y(t) for 2-component objects).autoplot.tf_mv() and autolayer.tf_mv()
methods for quick plots and layers of tf_mv objects.tf_unnest.tf_mv() method returning a “wide” long
table with one value column per output dimension:
(id, arg, <component 1>, ..., <component d>).x aesthetic is no
longer silently overwritten by tf_y’s arg
grid, so aes(tf_x = fx, tf_y = fy) now correctly draws
planar curves x(t) vs y(t).geom_spaghetti()/geom_meatballs(),
gglasagna(), geom_fboxplot()) now fail early
with an informative error for tf_mv inputs instead of dying
with obscure internal errors.chf_df with current tf
constructors before saving so the packaged dataset no longer carries
stale namespace references from pre-tf releases.tfd, tfb) as data
frame columns via the tf package.tidyverse-compatible data wrangling:
tf_gather, tf_spread, tf_nest,
tf_unnest.ggplot2 geoms for functional data:
geom_spaghetti, geom_meatballs,
geom_capellini, geom_errorband,
gglasagna.tf_ggplot() for tf-aware ggplot construction with
standard geoms.geom_fboxplot.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.