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Installation

# install.packages("pak")pak::pak("KopfLab/ggstackplot")

Show me some code

library(ggstackplot)# using R's built-in mtcars datasetmtcars|>   ggstackplot(# define shared x axisx=mpg,# define multiple y axesy= c("weight"=wt,"horsepower"=hp),# set colorscolor= c("#E41A1C","#377EB8"),# set to complete overlapoverlap=1  )

Show me some climate data

# download a recent dataset from the public climate data repository PANGAEAdataset<-pangaear::pg_data(doi="10.1594/PANGAEA.967047")[[1]]# show what some of these data look likedataset$data[  c("Depth ice/snow [m] (Top Depth)","Age [ka BP]","[SO4]2- [ng/g] (Ion chromatography)")]|>  head()|>knitr::kable()

# visualize the data with ggstackplotdataset$data|>   ggstackplot(x="Age [ka BP]",y= c(# vertical stack of the measurements through time"sulfate [ng/g]"="[SO4]2- [ng/g] (Ion chromatography)","δ34S [‰]"="δ34S [SO4]2- [‰ CDT] (Multi-collector ICP-MS (MC-IC...)","Δ33S [‰]"="Δ33S [SO4]2- [‰ CDT] (Multi-collector ICP-MS (MC-IC...)"    ),# color palettepalette="Dark2",# partial overlap of the panelsoverlap=0.4  )

What about horizontal stacks?

# download some more data from PANGAEAdataset2<-pangaear::pg_data(doi="10.1594/PANGAEA.933277")[[1]]# show what some of these data look likedataset2$data[  c("Depth sed [m]","Comp","δ13C [‰ PDB] (mean, vs. VPDB)")]|>  head()|>knitr::kable()

library(dplyr)library(ggplot2)# use a custom template for this plotmy_template<-# it's a ggplot  ggplot()+# use a path plot for all (to connect the data points by depth!)  geom_point()+ geom_path()+# we still want the default stackplot theme  theme_stackplot()+# depth is commonly plotted in reverse  scale_y_reverse()# now make the horizontal stack through depth for 2 of the variablesdataset2$data|>   filter(Comp=="C19")|>  arrange(`Depth sed [m]`)|>  ggstackplot(x= c("δ13C carb [‰ PDB]","δ13C n-C19 [‰]"="δ13C [‰ PDB] (mean, vs. VPDB)"    ),y="Depth sed [m]",palette="Dark2",overlap=1,template=my_template  )

# or show them side by side (note that this could also be achieved with ggplot# facets except for the fine-control and coloring of the different x-axes)dataset2$data|>   filter(Comp=="C19")|>  arrange(`Depth sed [m]`)|>  ggstackplot(x= c("δ13C carb [‰ PDB]","δ13C n-C19 [‰]"="δ13C [‰ PDB] (mean, vs. VPDB)"    ),y="Depth sed [m]",palette="Dark2",# no more overlapoverlap=0,# fine-tune the axes to be on top and bottomboth_axes=TRUE,template=my_template  )

Show me more

# using the built-in economics dataset in ggplot2 to create a vertical stack# of double axis plots using many of the customization features available# with ggstackplot and ggplot2ggplot2::economics|>  ggstackplot(# define shared x axisx=date,# define the stacked y axesy= c(pce,pop,psavert,unemploy),# pick the RColorBrewer Dark2 palette (good color contrast)palette="Dark2",# overlay the pce & pop plots (1), then make a full break (0) to the once# again overlaye psavert & unemploy plots (1)overlap= c(1,0,1),# switch axes so unemploy and psavert are on the side where they are# highest, respectively - not doing this here by changing the order of y# because we want pop and unemploy on the same sideswitch_axes=TRUE,# make shared axis space a bit smallershared_axis_size=0.15,# provide a base plot with shared graphics eelements among all plotstemplate=# it's a ggplot      ggplot()+# use a line plot for all      geom_line()+# we want the default stackplot theme      theme_stackplot()+# add custom theme modifications, such as text size      theme(text= element_text(size=14))+# make the shared axis a date axis      scale_x_date("year")+# include y=0 for all plots to contextualize data better      expand_limits(y=0),# add plot specific elementsadd=list(pce=# show pce in trillions of dollars          scale_y_continuous("personal consumption expenditures",# always keep the secondary axis duplicated so ggstackplot can# manage axis placement for yousec.axis= dup_axis(),# labeling function for the dollar unitslabels=function(x) sprintf("$%.1f T",x/1000),          ),pop=# show population in millions          scale_y_continuous("population",sec.axis= dup_axis(),labels=function(x) sprintf("%.0f M",x/1000)          ),psavert=# savings is in %          scale_y_continuous("personal savings rate",sec.axis= dup_axis(),labels=function(x) paste0(x,"%"),          )+# show data points in addition to line          geom_point(),unemploy=# unemploy in millions          scale_y_continuous("unemployed persons",sec.axis= dup_axis(),labels=function(x) sprintf("%.0f M",x/1000)          )+# show data points in addition to line          geom_point()      )  )

What else can I do with ggstackplot?