# Plotting temporal data

Many packages exist to visualize temporal data (e.g., geological or evolutionary biological data). deeptime similarly has a few novel ways to help you plot your temporal data in useful ways. We’ll first load some packages and example data so we can demonstrate some of this functionality.

    # Load deeptime
    library(deeptime)
    # Load other packages
    library(ggplot2)
    library(dplyr)
    # Load palaeoverse for tetrapod occurrence data
    library(palaeoverse)
    data(tetrapods)

## Plot occurrences through time

Do you have a bunch of temporal occurrences of taxa or some sort of geological phenomenon, but you don’t want to go through all of the pain of figuring out how to visualize those occurrences and their temporal ranges? And it needs to be customizable and have a pretty geological or stratigraphic timescale on the side? Well, then `geom_points_range()` is your friend!

`geom_points_range()` is like `ggplot2::geom_pointrange()` in that it plots points and their ranges. However, the “raw” data that goes into `ggplot2::geom_pointrange()` is the lower, upper, and the coordinates for an individual point for each group. First, we’re too lazy to calculate our own ranges! Second, only one point per group? But we have a whole bunch of occurrences for each group that need to be plotted! The difference with `geom_points_range()` is that the raw data it takes in is all of your grouped temporal data. It then does all of the work for you to plot those individual occurrences AND the ranges of those occurrences for each group. Let’s try it out with some Permian vertebrate occurrence data from the `palaeoverse`:

    # sort the occurrences from most common genera to least common genera
    # assume the age is just the mean of the max and min
    occdf <- tetrapods |>
      filter(accepted_rank == "genus") |>
      select(occurrence_no, accepted_name, max_ma, min_ma) |>
      mutate(accepted_name = reorder(accepted_name, accepted_name, length)) |>
      arrange(desc(accepted_name)) |>
      mutate(age = (max_ma + min_ma) / 2)
    # get a reasonable subset of those occurrences
    occdf <- occdf[1:300, ]

    # plot those occurrences
    ggplot(data = occdf) +
      geom_points_range(aes(x = age, y = accepted_name)) +
      theme_classic()

And then, of course, we want to add a timescale:

    ggplot(data = occdf) +
      geom_points_range(aes(x = age, y = accepted_name)) +
      scale_x_reverse() +
      coord_geo(pos = list("bottom", "bottom"), dat = list("stages", "periods"),
                abbrv = list(TRUE, FALSE), expand = TRUE, size = "auto") +
      theme_classic()

What if we aren’t certain about some of our data points? Maybe we don’t necessarily know if they are assigned to the correct genus or perhaps we are uncertain about their age? Fortunately, we have all of the tools of ggplot available to us! First we’ll simulate some binary “certainty” values, then we’ll plot certainty as additional aesthetics:

    occdf$certainty <- factor(sample(0:1, nrow(occdf), replace = TRUE))

    ggplot(data = occdf) +
      geom_points_range(aes(x = age, y = accepted_name,
                            fill = certainty, linetype = certainty), shape = 21) +
      scale_x_reverse() +
      scale_fill_manual(values = c("white", "black")) +
      scale_linetype_manual(values = c("dashed", "solid")) +
      coord_geo(pos = list("bottom", "bottom"), dat = list("stages", "periods"),
                abbrv = list(TRUE, FALSE), expand = TRUE, size = "auto") +
      theme_classic()

Finally, we can sort the taxa so that they are arranged in order of their earliest occurrence:

    occdf$accepted_name <- reorder(occdf$accepted_name, occdf$age, max,
                                   decreasing = TRUE)
    ggplot(data = occdf) +
      geom_points_range(aes(x = age, y = accepted_name,
                            fill = certainty, linetype = certainty), shape = 21) +
      scale_x_reverse() +
      scale_fill_manual(values = c("white", "black")) +
      scale_linetype_manual(values = c("dashed", "solid")) +
      coord_geo(pos = list("bottom", "bottom"), dat = list("stages", "periods"),
                abbrv = list(TRUE, FALSE), expand = TRUE, size = "auto") +
      theme_classic()

Note that our uncertain lines (0) always overlap with our certain lines (1), so there is a continuous line spanning each taxon’s entire range. However, if we tweak some of the data, we can change that, causing a gap in the ranges. Note that in this engineered example, the uncertain and certain ranges for Diictodon no longer overlap, so there is a gap between them:

    oldest_certain <- occdf |>
      filter(accepted_name == "Diictodon", certainty == 1) |>
      pull(age) |>
      max()

    n_uncertain <- sum(occdf$accepted_name == "Diictodon" & occdf$certainty == 0)

    # make the uncertain points all much older
    occdf$age[occdf$accepted_name == "Diictodon" & occdf$certainty == 0] <-
      oldest_certain + runif(n_uncertain, 15, 30)

    ggplot(data = occdf) +
      geom_points_range(aes(x = age, y = accepted_name,
                            fill = certainty, linetype = certainty), shape = 21) +
      scale_x_reverse() +
      scale_fill_manual(values = c("white", "black")) +
      scale_linetype_manual(values = c("dashed", "solid")) +
      coord_geo(pos = list("bottom", "bottom"), dat = list("stages", "periods"),
                abbrv = list(TRUE, FALSE), expand = TRUE, size = "auto") +
      theme_classic()

However, if we want a line connecting these groups of points, we can fix this by using the `background_line` argument, which can be a list of aesthetic values to use for the background line segments:

    ggplot(data = occdf) +
      geom_points_range(aes(x = age, y = accepted_name,
                            fill = certainty, linetype = certainty), shape = 21,
                        background_line = list(linetype = "dashed")) +
      scale_x_reverse() +
      scale_fill_manual(values = c("white", "black")) +
      scale_linetype_manual(values = c("dashed", "solid")) +
      coord_geo(pos = list("bottom", "bottom"), dat = list("stages", "periods"),
                abbrv = list(TRUE, FALSE), expand = TRUE, size = "auto") +
      theme_classic()

Finally, while I’ve showcased this geom with the use case of plotting occurrence data, note that the potential usage for this function is much broader. Basically any set of data with a categorical and a continuous variable could be visualized like this (when appropriate).

## Geological timescale color scales for ggplot

You may also want to color your data based on its age. deeptime has `scale_color_geo()` and `scale_fill_geo()` for this very purpose! Note that currently these scales only work with discrete data. The default behavior is for the color/fill aesthetic values to match the names of the intervals in `dat`. Here, we’ll use the coral_div_dis data from the first vignette tutorial:

    ggplot(coral_div_dis, aes(x = n, y = diet, fill = period)) +
      geom_col() +
      scale_fill_geo(periods) +
      xlab("Coral Genera") +
      ylab("Diet") +
      theme_classic()

## Facetting with the geological timescale

Another way to split data by categories is by faceting. deeptime has a suite of facetting functions that allow you to facet by geological intervals (`facet_grid_geo()`, `facet_wrap_geo()`, `facet_nested_geo()`, and `facet_nested_wrap_geo()`). Here, we’ll use the same coral diversity data as above, but facet wrap it by period. Note that we need to convert the `period` column to an ordered factor so the facets appear in chronological order.

    coral_div_dis <- coral_div_dis |>
      mutate(period = factor(period, levels = rev(periods$name)))

    ggplot(coral_div_dis, aes(x = n, y = diet)) +
      geom_col() +
      scale_fill_geo(periods) +
      xlab("Coral Genera") +
      ylab("Diet") +
      theme_classic() +
      facet_wrap_geo(~period, colors = periods)

Just like with adding timescales to plots, you may want to show hierarchical timescale information. For example, we may want to show which of these periods are in the Mesozoic and which are in the Cenozoic. We can do this by adding a new column to our data that indicates the era of each period, then using both the period and era columns to make a nested facet:

    coral_div_dis <- coral_div_dis |>
      mutate(era = ifelse(period %in% c("Paleogene", "Neogene", "Quaternary"),
                          "Cenozoic", "Mesozoic")) |>
      mutate(era = factor(era, levels = c("Cenozoic", "Mesozoic")))

    ggplot(coral_div_dis, aes(x = n, y = diet)) +
      geom_col() +
      scale_fill_geo(periods) +
      xlab("Coral Genera") +
      ylab("Diet") +
      theme_classic() +
      facet_nested_wrap_geo(~era + period, colors = rbind(periods, eras), nrow = 2)