gf_ridgeline(), gf_ridgeline_gradient(), gf_density_ridges(), gf_density_ridges_gradient().gf_dens() now uses geom = "geom_density_line". This allows setting fill.labelled, expss or Hmisc labeling functionsgf_sina(). (#134)sf::st_as_sf() to reflect updated names of in the resulting data frame. This is required to get CRAN builds to work without an error. (#137)gf_dist(). (#133)layer_factory() have been reimplemented to avoid making copies of ggplot2 functions at compile time. This should make things more stable when updating ggplot2 but not updating ggformula.gf_ellipse() and gf_ecdf()gf_rugx() and gf_label() have been modified a bit to make them easier to use.environment argument is used. After some delay, this completes the migration to ggplot2 version 2.2 (#96, #125)gf_dist() to avoid an error when using params. (#119)gf_errorbar() is now inherit = TRUE. The old default was required due to inconvenient naming of aesthetics in geom_errorbar(), but those names have been changed now. (#120)discrete_breaks().This release includes a few minor improvements, including
ggformula now requires ggplot2 (>= 3.0.0). This should take care of issues in 0.8.0 when ggplot2 was upgraded after upgrading ggformula.pkgdown website created at https://projectmosaic.github.io/ggformula/ggstance.vdiffr is used for testing plot outputThe internals of all of the gf_ functions have been modified to make them work with ggplot2 version 2.3. In future releases, a version ggplot2 at least this new will be required.
The default value for se in gf_smooth() has been changed from TRUE to FALSE.
gf_sf() added to support simple features data for maps.
improved documentation and examples
y ~ . can be used for plots that require a y aesthetic but no x aesthetic.gf_rugx() and gf_rugy(). Also additional examples using rugs are given in the documentation.gf_polygon() for simple maps. (Improved mapping capabilities should be coming when ggplot2 hits version 2.3.)gf_ functions makes it easier to work with data in the global environment.gf_props() and gf_percents() added to simplify creating bar graphs on a proportion or percent scale.xlab, ylab, title, subtitle, and caption added to gf_ functions. In the case of gf_props() and gf_percents() these are prepopulated with defaults of “proportion” and “percent” to give nicer labeling of the plot.weatherData has been removed since that package is no longer on CRANgf_abline(), gf_hline(), and gf_vline() expand the usable portion of the API.Minor update primarily to support vignette data sets moving from StatisticalModeling to mosaicModel.
gf_fitdistr().Mostly minor changes:
For version 0.5, the internals of ggformula have been largely redesigned to allow implementation of some new features. The new version relies much less on string parsing.
Some important changes to formula parsing include
attribute:value and attribute::expression are no longer supported within the main formula.gf_point(1:10 ~ 1:10) work, making it simpler to create on the fly plots without having to build a data frame first.y ~ 1 is equivalent to ~ y in functions that allow the ~ y formula shape. Example: gf_histogram(age ~ 1) and gf_histogram( ~ age) are equivalentSome new functions have been added
gf_dist() can plot distributionsgf_dhistogram() plots density histograms by defaultgf_ash() creates ASH plotsdf_stats() has been improved to handle one-sided formulas better.
Secondary layers are now able to inherit both data and formula-defined attributes from the primary layer. Use inherit = FALSE if you don’t want inheritance. (A few functions have inherit = FALSE as their default because it seems unlikely that inheriting will be desireable.)
Version 0.4.0 constitutes a stable beta release. Changes to the API are still possible, but more likely future changes will focus on expansion of the suite of functions supplied, changes to the internal implementation, and improved documentation.
ggformula from statisticalModeling.data may now be an expression (like data = KidsFeet %>% filter(sex == "G"))gf_histogram() accepts formulas with shape ~ x or y ~ x. This makes it possible to create density histograms with gf_histogram().:: to indicate mapping aesthetics. (: will autodetect, but only if the value is the name of a variable in the data set.) This should be considered experimental.gf_lims(), gf_labs(), gf_theme(), gf_facet_grid(), gf_facet_wrap()gf_refine() which can be used to pass by chaining anything that would have been “added” in `ggplot2gf_lm(), which is gf_smooth() with method = "lm"gf_dens() which is gf_line() with stat = "density".