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  )

# 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  )

# 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 ggplotggplot()+# use a path plot for all (to connect the data points by depth!)geom_point()+geom_path()+# we still want the default stackplot themetheme_stackplot()+# depth is commonly plotted in reversescale_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  )

# 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 ggplotggplot()+# use a line plot for allgeom_line()+# we want the default stackplot themetheme_stackplot()+# add custom theme modifications, such as text sizetheme(text =element_text(size =14))+# make the shared axis a date axisscale_x_date("year")+# include y=0 for all plots to contextualize data betterexpand_limits(y =0),# add plot specific elementsadd =list(pce =# show pce in trillions of dollarsscale_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 millionsscale_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 linegeom_point(),unemploy =# unemploy in millionsscale_y_continuous("unemployed persons",sec.axis =dup_axis(),labels =function(x)sprintf("%.0f M", x/1000)          )+# show data points in addition to linegeom_point()      )  )