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If you think I'm into this for the money you're dead wrong because I'm not doing this for the money. I'm doing it because it lives inside of me. -- GG Allin
A grab bag ofggplot2 extensions and hacks.
-- Steven E. Pav,shabbychef@gmail.com
This package can be installedfrom CRAN (not yet),viadrat, orfrom github:
# via CRAN: (not yet)# install.packages("ggallin")# via drat:if (require(drat)) {drat:::add("shabbychef") install.packages("ggallin")}# get snapshot from github (may be buggy)if (require(devtools)) { install_github('shabbychef/ggallin')}
Thisgeom acts nearly as a drop-in replacement forgeom_errorbar,convertingymin andymax into 'clouds' of uncertainty with alphaproportional to normal density.
library(ggplot2)library(ggallin)library(dplyr)nobs<-1000set.seed(2134)mydat<-data.frame(grp=sample(c(0,1),nobs,replace=TRUE),colfac=sample(letters[1:2],nobs,replace=TRUE),rowfac=sample(letters[10+ (1:3)],nobs,replace=TRUE)) %>% mutate(x=seq(0,1,length.out=nobs)+0.33*grp) %>% mutate(y=0.25*rnorm(nobs)+2*grp) %>% mutate(grp=factor(grp)) %>% mutate(se=sqrt(x)) %>% mutate(ymin=y-se,ymax=y+se)offs<-2ph<-mydat %>% mutate(y=y+offs,ymin=ymin+offs,ymax=ymax+offs) %>% ggplot(aes(x=x,y=y,ymin=ymin,ymax=ymax,color=grp,fill=grp))+ facet_grid(rowfac~colfac)+ scale_y_sqrt()+ geom_line()+ geom_cloud(aes(fill=grp),steps=15,max_alpha=0.85,color=NA)+ labs(title='geom cloud')print(ph)
The square root transform is a good compromise between raw and logarithmicscales, showing detail across different scales without over-emphasizing verysmall variation. However, it does not work for negative numbers. Thusa signed square root transform is useful. Along similar lines, thepseudo-log transformaccepts negative numbers while providing a good view across magnitudes.Some illustrations:
library(ggplot2)library(ggallin)library(dplyr)nobs<-100# this is a silly example, don't blame meset.seed(1234)mydat<-data.frame(x=rnorm(nobs),z=rnorm(nobs)) %>% mutate(y=sign(z)* exp(x+z-2))ph<-mydat %>% ggplot(aes(x=x,y=y))+ geom_line()+ scale_y_continuous(trans=ssqrt_trans)print(ph)
ph<-mydat %>% ggplot(aes(x=x,y=y))+ geom_line()+ scale_y_continuous(trans=pseudolog10_trans)print(ph)
Scale transforms are useful for 'straightening out' crooked data graphically.Sometimes these transforms can not be expressed functionally but instead relyon data. In this case we can imagine that we have some paired data thatprovide the transformationx -> y. We provide a scale transformation thatsupports linear interpolation.We also provide another scale transformation that acceptsx and positive 'weights'w, and computesy by taking the cumulative sum of weights, called a 'warp'transformation.
Here we illustrate the warp transformation by plotting the cumulative return ofthe 'UMD' factor against a time scale that is uniform in cumulative daily VIX(whatever that means):
library(ggplot2)library(ggallin)library(dplyr)library(aqfb.data)library(scales)data(dvix)data(dff4)rr_to_nav<-function(x) { exp(cumsum(log(1+x)))}rets<-dff4 %>% as.data.frame() %>%tibble::rownames_to_column(var='date') %>% inner_join(dvix %>% as.data.frame() %>% setNames(c('VIX')) %>%tibble::rownames_to_column(var='date'),by='date') %>% mutate(date=as.Date(date,format='%Y-%m-%d')) %>% mutate(UMD_nav=rr_to_nav(0.01*UMD),SMB_nav=rr_to_nav(0.01*SMB),HML_nav=rr_to_nav(0.01*HML))ph<-rets %>% ggplot(aes(x=date,y=UMD_nav))+ geom_line()+ labs(y='UMD cumulative return')+ labs(x='regular date scale') print(ph)
# select breaks automagicallyph<-rets %>% ggplot(aes(x=date,y=UMD_nav))+ geom_line()+ scale_x_continuous(trans=warp_trans(x=rets$date,w=rets$VIX))+ labs(y='UMD cumulative return')+ labs(x='warped date scale') print(ph)
# force decade breaks:ph<-rets %>% ggplot(aes(x=date,y=UMD_nav))+ geom_line()+ scale_x_continuous(trans=warp_trans(x=rets$date,w=rets$VIX,breaks=scales::date_breaks('10 years'),format=scales::date_format('%Y')))+labs(y='UMD cumulative return')+ labs(x='warped date scale')print(ph)
# reverse scale as well (see composition of transforms)ph<-rets %>% ggplot(aes(x=date,y=UMD_nav))+ geom_line()+ scale_x_continuous(trans=scales::reverse_trans() %of% warp_trans(x=rets$date,w=rets$VIX))+ labs(y='UMD cumulative return')+ labs(x='reversed, warped date scale')print(ph)
The%of% binary operator supports composition of scale transformations. Thisis most useful for composing reverse scales with other transforms:
library(ggplot2)library(ggallin)# reverse and log scaleset.seed(1234)ph<- ggplot(data.frame(x=rnorm(100),y=exp(rnorm(100,mean=-2,sd=4))),aes(x=x,y=y))+ geom_point()+ scale_y_continuous(trans=scales::reverse_trans() %of%scales::log10_trans())+ labs(title='reversed and log scaled y')print(ph)
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