[This article was first published on R – R-statistics blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Guest post by Jonathan Sidi, Metrum Research Group
Last week the updated version of ggedit was presented in RStudio::conf2017. First, a BIG thank you to the whole RStudio team for a great conference and being so awesome to answer the insane amount of questions I had (sorry!). For a quick intro to the package see the previous post. To install the package:devtools::install_github("metrumresearchgroup/ggedit",subdir="ggedit")
Highlights of the updated version.
- verbose script handling during updating in the gagdet (see video below)
- verbose script output for updated layers and theme to parse and evaluate in console or editor
- colourpicker control for both single colours/fills and and palletes
- output for scale objects eg scale*grandient,scale*grandientn and scale*manual
- verbose script output for scales eg scale*grandient,scale*grandientn and scale*manual to parse and evaluate in console or editor
- input plot objects can have the data in the layer object and in the base object.
ggplot(data=iris,aes(x=Sepal.Width,y=Sepal.Length,colour=Species))+geom_point()ggplot(data=iris,aes(x=Sepal.Width,y=Sepal.Length))+geom_point(aes(colour=Species))ggplot()+geom_point(data=iris,aes(x=Sepal.Width,y=Sepal.Length,colour=Species))
- plot.theme(): S3 method for class ‘theme’
- visualizing theme objects in single output
- visual comparison of two themes objects in single output
- will be expanded upon in upcoming post
RStudio::conf2017 Presentation
#devtools::install_github("metrumresearchgroup/ggedit",subdir="ggedit")
rm(list=ls())
library(ggedit)
#?ggedit
p0=list(
Scatter=iris%>%ggplot(aes(x =Sepal.Length,y=Sepal.Width))+
geom_point(aes(colour=Species),size=6),
ScatterFacet=iris%>%ggplot(aes(x =Sepal.Length,y=Sepal.Width))+
geom_point(aes(colour=Species),size=6)+
geom_line(linetype=2)+
facet_wrap(~Species,scales='free')+
labs(title='Some Title')
)
#a=ggedit(p.in = p0,verbose = T) #run ggedit
dat_url <- paste0("https://raw.githubusercontent.com/metrumresearchgroup/ggedit/master/RstudioExampleObj.rda")
load(url(dat_url)) #pre-run example
ldply(a,names)
## .id V1 V2
## 1 UpdatedPlots Scatter ScatterFacet
## 2 UpdatedLayers Scatter ScatterFacet
## 3 UpdatedLayersElements Scatter ScatterFacet
## 4 UpdatedLayerCalls Scatter ScatterFacet
## 5 updatedScales Scatter ScatterFacet
## 6 UpdatedScalesCalls Scatter ScatterFacet
## 7 UpdatedThemes Scatter ScatterFacet
## 8 UpdatedThemeCalls Scatter ScatterFacet
plot(a)
comparePlots=c(p0,a$UpdatedPlots) names(comparePlots)[c(3:4)]=paste0(names(comparePlots)[c(3:4)],"Updated")
Initial Comparison Plot
plot(as.ggedit(comparePlots))
Apply updated theme of first plot to second plot
comparePlots$ScatterFacetNewTheme=p0$ScatterFacet+a$UpdatedThemes$Scatter
plot(as.ggedit(comparePlots[c("ScatterFacet","ScatterFacetNewTheme")]),
plot.layout = list(list(rows=1,cols=1),list(rows=2,cols=1))
)
(comparePlots$ScatterMistake=p0$Scatter+a$UpdatedLayers$ScatterFacet[[1]])
Remove
(comparePlots$ScatterNoLayer=p0$Scatter%>% rgg(oldGeom = 'point'))
Replace Geom_Point layer on Scatter Plot
(comparePlots$ScatterNewLayer=p0$Scatter%>%
rgg(oldGeom = 'point',
oldGeomIdx = 1,
newLayer = a$UpdatedLayers$ScatterFacet[[1]]))
Remove and Replace Geom_Point layer and add the new theme
(comparePlots$ScatterNewLayerTheme=p0$Scatter%>%
rgg(oldGeom = 'point',
newLayer = a$UpdatedLayers$ScatterFacet[[1]])+
a$UpdatedThemes$Scatter)
Cloning Layers
A geom_point layer
(l=p0$Scatter$layers[[1]]) ## mapping: colour = Species ## geom_point: na.rm = FALSE ## stat_identity: na.rm = FALSE ## position_identity
Clone the layer
(l1=cloneLayer(l)) ## mapping: colour = Species ## geom_point: na.rm = FALSE ## stat_identity: na.rm = FALSE ## position_identity all.equal(l,l1) ## [1] TRUE
Verbose copy of layer
(l1.txt=cloneLayer(l,verbose = T)) ## [1] "geom_point(mapping=aes(colour=Species),na.rm=FALSE,size=6,data=NULL,position=\"identity\",stat=\"identity\",show.legend=NA,inherit.aes=TRUE)"
Parse the text
(l2=eval(parse(text=l1.txt))) ## mapping: colour = Species ## geom_point: na.rm = FALSE ## stat_identity: na.rm = FALSE ## position_identity
all.equal(l,l2) ## [1] TRUE
Back to our example
#Original geom_point layer parse(text=cloneLayer(p0$ScatterFacet$layers[[1]],verbose = T)) ## expression(geom_point(mapping = aes(colour = Species), na.rm = FALSE, ## size = 6, data = NULL, position = "identity", stat = "identity", ## show.legend = NA, inherit.aes = TRUE)) #new Layer parse(text=a$UpdatedLayerCalls$ScatterFacet[[1]]) ## expression(geom_point(mapping = aes(colour = Species), na.rm = FALSE, ## size = 3, shape = 22, fill = "#BD2020", alpha = 1, stroke = 0.5, ## data = NULL, position = "identity", stat = "identity", show.legend = NA, ## inherit.aes = TRUE))
Jonathan Sidi joined Metrum Researcg Group in 2016 after working for several years on problems in applied statistics, financial stress testing and economic forecasting in both industrial and academic settings. To learn more about additional open-source software packages developed by Metrum Research Group please visit the Metrum website. Contact: For questions and comments, feel free to email me at: yonis@metrumrg.com or open an issue in github.
To leave a comment for the author, please follow the link and comment on their blog: R – R-statistics blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.