coord_geo behaves similarly to ggplot2::coord_trans() in that it occurs
after statistical transformation and will affect the visual appearance of
geoms. The main difference is that it also adds a geological timescale to the
specified side(s) of the plot.
coord_geo(
pos = "bottom",
dat = "periods",
xlim = NULL,
ylim = NULL,
xtrans = identity_trans(),
ytrans = identity_trans(),
clip = "on",
expand = FALSE,
fill = NULL,
alpha = 1,
height = unit(2, "line"),
bord = c("left", "right", "top", "bottom"),
lwd = 0.25,
color = "black",
lab = TRUE,
lab_color = NULL,
rot = 0,
family = "sans",
fontface = "plain",
size = 5,
skip = c("Quaternary", "Holocene", "Late Pleistocene"),
abbrv = TRUE,
neg = FALSE,
center_end_labels = FALSE,
dat_is_discrete = FALSE,
fittext_args = list()
)Which side to add the scale to (left, right, top, or bottom). First letter may also be used.
Either A) a string indicating a built-in dataframe with interval data from the ICS ("periods", "epochs", "stages", "eons", or "eras"), B) a string indicating a timescale from macrostrat (see list here: https://macrostrat.org/api/defs/timescales?all), or C) a custom data.frame of time interval boundaries (see Details).
Limits for the x and y axes.
Transformers for the x and y axes. For more information
see ggplot2::coord_trans().
Should drawing be clipped to the extent of the plot panel? A
setting of "on" (the default) means yes, and a setting of "off"
means no. In most cases, the default of "on" should not be changed,
as setting clip = "off" can cause unexpected results. It allows
drawing of data points anywhere on the plot, including in the plot margins. If
limits are set via xlim and ylim and some data points fall outside those
limits, then those data points may show up in places such as the axes, the
legend, the plot title, or the plot margins.
If FALSE, the default, limits are taken exactly from the data
or xlim/ylim. If TRUE, adds a small expansion factor to the limits to
ensure that data and axes don't overlap.
The fill color of the boxes. The default is to use the color
column included in dat. If a custom dataset is provided with dat
without a color column and without fill, a greyscale will be used.
Custom fill colors can be provided with this option (overriding the color
column) and will be recycled if/as necessary.
The transparency of the fill colors.
The height (or width if pos is left or right) of the
scale.
A vector specifying on which sides of the scale to add borders
(same options as pos).
Line width.
The outline color of the interval boxes.
Whether to include labels.
The color of the labels. The default is to use the
lab_color column included in dat. If a custom dataset is provided with
dat without a lab_color column and without fill, all labels will be
black. Custom label colors can be provided with this option (overriding the
lab_color column) and will be recycled if/as necessary.
The amount of counter-clockwise rotation to add to the labels (in degrees).
The font family to use for the labels. There are only three fonts that are guaranteed to work everywhere: “sans” (the default), “serif”, or “mono”.
The font face to use for the labels. The standard options are "plain" (default), "bold", "italic", and "bold.italic".
Label size. Either a number as you would specify in
ggplot2::geom_text() or "auto" to use ggfittext::geom_fit_text().
A vector of interval names indicating which intervals should not
be labeled. If abbrv is TRUE, this can also include interval
abbreviations.
If including labels, should the labels be abbreviated? If
TRUE, the abbr column will be used for the labels. If FALSE, the
name column will be used for the labels. If "auto", the abbreviate()
function will be used to abbreviate the values in the name column. Note
that the built-in data and data retrieved via get_scale_data() already
have built-in abbreviations. However, using the "auto" option here will
create new unique abbreviations based on only the intervals that are being
plotted. In many cases, this may result in abbreviations that are shorter
in length because there are fewer similar interval names to abbreviate.
Set this to TRUE if your x-axis is using negative values.
Should labels be centered within the visible range of intervals at the ends of the axis?
Are the ages in dat already converted for a discrete
scale?
A list of named arguments to provide to
ggfittext::geom_fit_text(). Only used if size is set to "auto".
Transforming the side with the scale is not currently implemented.
If a custom data.frame is provided (with dat), it should consist of at
least 3 columns of data. See data(periods) for an example.
The name column lists the names of each time interval. These will
be used as labels if no abbreviations are provided.
The max_age column lists the oldest boundary of each time interval.
The min_age column lists the youngest boundary of each time
interval.
The abbr column is optional and lists abbreviations that may be
used as labels.
The color column is also optional and lists a
color for the background for each time interval.
The lab_color column is also optional and lists a
color for the label for each time interval.
If the axis of the time scale is discrete, max_age and min_age will
automatically be converted to the discrete scale. In this case, the
categories of the discrete axis should match the values in the name column.
If the ages within dat are already discretized, you can set
dat_is_discrete to TRUE to prevent this automatic conversion. This can be
useful for adding a time scale where categories and time intervals are not
1:1.
pos may also be a list of sides (including duplicates) if multiple time
scales should be added to the plot. In this case, dat, fill, alpha,
height, bord, lwd, color, lab, lab_color, rot, family,
fontface, size, skip, abbrv, neg, center_end_labels, and
dat_is_discrete can also be lists. If these lists are not as long as
pos, the elements will be recycled.
If individual values (or vectors) are used for these parameters, they will be
applied to all time scales (and recycled as necessary).
library(ggplot2)
# single scale on bottom
ggplot() +
geom_point(aes(y = runif(1000, 0, 8), x = runif(1000, 0, 1000))) +
scale_x_reverse() +
coord_geo(xlim = c(1000, 0), ylim = c(0, 8)) +
theme_classic()
# stack multiple scales
ggplot() +
geom_point(aes(y = runif(1000, 0, 8), x = runif(1000, 0, 100))) +
scale_x_reverse() +
coord_geo(
xlim = c(100, 0), ylim = c(0, 8), pos = as.list(rep("bottom", 3)),
dat = list("stages", "epochs", "periods"),
height = list(unit(4, "lines"), unit(4, "lines"), unit(2, "line")),
rot = list(90, 90, 0), size = list(2.5, 2.5, 5), abbrv = FALSE
) +
theme_classic()
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