Package ‘ggpmisc’ focuses mainly on plot annotations. The new version adds features that I hope will be found useful.
Package documentation web site at: https://docs.r4photobiology.info/ggpmisc/
Changes from version 0.3.6 the most recent CRAN release, are:
New
Filtering based on 1D density
Three new statistics stat_dens1d_filter()
, stat_dens1d_filter_g()
and stat_dens1d_labels()
are 1D versions of the existing 2D functions which received a minor update.
- Add
stat_dens1d_filter()
,stat_dens1d_filter_g()
andstat_dens1d_labels()
, to complement existingstat_dens2d_filter()
,stat_dens2d_filter_g()
andstat_dens2d_labels()
. - Update
stat_dens2d_filter()
,stat_dens2d_filter_g()
andstat_dens2d_labels()
adding formal parameters keep.sparse and invert.selection, as available in the new 1D versions. - Update
stat_dens2d_labels()
to accept not only character strings but also functions as argument tolabel.fill
as the newstat_dens1d_labels()
does.
Table insets and their formatting
Updates to statistics stat_fmt_tb()
and stat_fit_tb()
add flexibility.
- Update
stat_fit_tb()
to support renaming of terms/parameter names in the table (Suggested by Big Old Dave and Z. Lin). In addition implement selection, reordering and renaming of columns and terms/parameters using positional indexes and pattern matching of truncated names in addition to whole names. Improve formatting of small P-values. - Update
stat_fmt_tb()
to support the same expanded syntax asstat_fit_tb()
.
Here is an example for an ANOVA table inset.
formula <- y ~ x ggplot(PlantGrowth, aes(group, weight)) + stat_summary(fun.data = "mean_se") + stat_fit_tb(method = "lm", method.args = list(formula = formula), tb.type = "fit.anova", tb.vars = c(Term = "term", "df", "M.S." = "meansq", "italic(F)" = "statistic", "italic(p)" = "p.value"), tb.params = c("Group" = 1, "Error" = 2), table.theme = ttheme_gtbw(parse = TRUE)) + labs(x = "Group", y = "Dry weight of plants") + theme_classic()
Acknowlegement: This update was encouraged by recent questions at stackoverflow. The tag [ggpmisc]
is in use at stackoverflow for questions related to this package.
Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, and vignettes in HTML format.
NOTE: The new version of the package is on its way to CRAN.
Please raise issues concerning bugs or enhancements to this package through Bitbucket https://github.com/aphalo/ggpmisc/issues.