ggstackplot

library(ggstackplot)

# using R's built-in mtcars dataset
mtcars |>
  ggstackplot(
    # define shared x axis
    x = mpg,
    # define multiple y axes
    y = c("weight" = wt, "horsepower" = hp),
    # set colors
    color = c("#E41A1C", "#377EB8"),
    # set to complete overlap
    overlap = 1
  )

# download a recent dataset from the public climate data repository PANGAEA
dataset <- pangaear::pg_data(doi = "10.1594/PANGAEA.967047")[[1]]

# show what some of these data look like
dataset$data[
  c("Depth ice/snow [m] (Top Depth)",
    "Age [ka BP]",
    "[SO4]2- [ng/g] (Ion chromatography)")] |>
  head() |> knitr::kable()

# visualize the data with ggstackplot
dataset$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 palette
    palette = "Dark2",
    # partial overlap of the panels
    overlap = 0.4
  )

# download some more data from PANGAEA
dataset2 <- pangaear::pg_data(doi = "10.1594/PANGAEA.933277")[[1]]

# show what some of these data look like
dataset2$data[
  c("Depth sed [m]", "Comp", "δ13C [‰ PDB] (mean, vs. VPDB)")] |>
  head() |> knitr::kable()

library(dplyr)
library(ggplot2)

# use a custom template for this plot
my_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 variables
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",
    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 overlap
    overlap = 0,
    # fine-tune the axes to be on top and bottom
    both_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 ggplot2
ggplot2::economics |>
  ggstackplot(
    # define shared x axis
    x = date,
    # define the stacked y axes
    y = 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 side
    switch_axes = TRUE,
    # make shared axis space a bit smaller
    shared_axis_size = 0.15,
    # provide a base plot with shared graphics eelements among all plots
    template =
      # 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 elements
    add =
      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 you
            sec.axis = dup_axis(),
            # labeling function for the dollar units
            labels = 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()
      )
  )