Refactor functions to align with ggplot2
version (3.5.0).
ggplot2
Collapse internal files to run the module and gadget from same base script.
Reorganize BS Modals to have columns by object type.
Change plot.ggedit be a print.ggedit method.
shinyBS
js library dependencies loaded with package, this lets ggedit run on attachment. i.e. ggedit::ggedit(p)
margins can be edited in the theme UI
+.gg
functionality added to gglist class to manipulate to multiple plots in layout
as.gglist(list(pList[[1]],pList[[3]])) +
geom_hline(yintercept=3:4,linetype=2) +
theme_minimal()+
theme(legend.position = 'top')
gg_session retreives all functions that create ggroto layers or stats in current loaded namespace and returns unique mapping to their position
,geom
,stat
.
gg_vetting returns the columns from gg_session relevant to a compiled plot
ggedit_opts functionality to control session levels options (like knitr::opts_chunk
). This can be used to manipulate the package defaults like the theme tips seen in BS modals. It is also used to store an updated output of gg_session, so ggedit can identify the correct mapping with gg_extension pacakges (eg ggalt
).
manipulate gg_extension pacakges (still in development, but works for a lot of the ggplot2 extension packages)
dput.ggedit returns dput for ggplot2 object in script form and not a structure.
pList$pointSmooth #original compiled plot
this.gg <- dput.ggedit(pList$pointSmooth) #dput the plot
writeLines(this.gg) #show the output
eval(parse(text=this.gg)) #recompile the plot
summary.ggedit method for ggedit class return script that created compiled gg object.
out <- ggedit(pList[1:2])
#assuming out is returned from ggedit
summary(out)
# point
# ggplot(mapping=aes(x=Sepal.Length,y=Sepal.Width),[data.frame])+
# geom_point(aes(colour=Species),size=6)
#
# pointWrap
# ggplot(mapping=aes(x=Sepal.Length,y=Sepal.Width),[data.frame])+
# geom_point(aes(colour=Species),size=6)+
# facet_wrap(facets=~Species,shrink=TRUE)
mutate_each
with mutate_all
to be compatible with new dplyr
release- can pass functions into stat_summary, eg `stat_summary(fun.y=mean_sd, geom='point')`
- data.frames in layers are embedded as structures in verbose outputs, e.g. `geom_point(data=mtcars, aes(cyl, mpg))` will return
```r
[1] "geom_point(mapping=aes(x=cyl,y=mpg), data=structure(list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,.. <truncated>
```
- aesthetic can be set to NULL, ie aes(group=NULL)