Visualizing the Output of datsteps()
To get a general impression of the data set and the possibilities of
visualization, we explore and recommend different plot methods below.
The simplest and fastest way to plot the output with base R is the
plot() function in combination with the
density() function:
Using ggplot2
A somewhat crowded overview containing the Locations of all
Inscriptions can easily be achieved when using ggplot2 and
its geom_density()-method, which is based on the same
procedure as the density()-function used above. A result
may look like this:
ggplot(data = inscr_steps, aes(x = DAT_step, fill = variable,
weight = weight)) +
geom_density(alpha = 0.3)
Note that the output without the weights calculated by
datsteps() is very different:
The weights calculated by datplot (internally using
the get.weights()-function) are a way of assigning an
importance to the objects in question. They relate to the range an
object is dated to (more detailed information on the process is
available in (Weissova and Steinmann
2021)). Therefore, any objects dated to large time spans as for
example “Roman Imperial Period” ranging from 31 BC to 395 AD in our data
set, are contributing to the curve significantly less than any object
dated to one exact year. At times it can be very useful to look at both
outputs and discuss them separately. We will look at a case study of a
single city later on to clarify this.
Using ggplot2 and ggridges
Since the graph is very crowded in this layout, we actually recommend
using geom_density_ridges from the
ggridges-package. Since the package has no built in support
for assigning weight, we have to use stat = "density",
which will reference the density()-function to get the
appropriate calculations:
ggplot(data = inscr_steps,
aes(x = DAT_step,
y = fct_rev(as_factor(variable)),
fill = variable,
weight = weight)) +
geom_density_ridges(aes(height = after_stat(density)),
stat = "density", alpha = 0.9) +
scale_fill_discrete(guide = FALSE)
#> Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
#> ggplot2 3.3.4.
#> i Please use "none" instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
Styling will help to make the plots more readable:
bluegreen <- colorRampPalette(c("#8dae25", "#17365c"))
ggplot(data = inscr_steps,
aes(x = DAT_step,
y = fct_rev(as_factor(variable)),
fill = variable,
weight = weight)) +
geom_density_ridges(aes(height = after_stat(density)), stat = "density", alpha = 0.9) +
scale_x_continuous(breaks = seq(from = -800, to = 800, by = 100),
limits = c(-800,800), name = "") +
geom_vline(xintercept = 0, alpha = 0.5, lwd = 1) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5),
panel.background = element_blank(),
panel.grid.major.x = element_line(linetype = "dashed",
color = "gray30"),
panel.grid.minor.x = element_line(linetype = "dotted",
color = "gray80")) +
scale_fill_manual(guide=FALSE,
values = bluegreen(length(unique(inscr_steps$variable)))) +
labs(title = "Epigraphic Evidence from Bithynia",
subtitle = "Spatio-temporal distribution",
y = "administrative centres",
caption = attributes(inscriptions)$source)
Using ggplot2 and facet_wrap()
Another option is to separate the variables with
facet_wrap, which is not as condensed and does not support
the option to scale the densities in the graph for maximum visibility,
as geom_density_ridges() automatically does (see
Documentation of ggridges). In our case, this leads to a
worse visibility of smaller density curves:
ggplot(data = inscr_steps, aes(x = DAT_step,
fill = variable, weight = weight)) +
geom_density(alpha = 0.9) +
scale_fill_discrete(guide = FALSE) +
facet_wrap(variable ~ ., ncol = 1)
Styling also helps very much to improve the graphs readability:
ggplot(data = inscr_steps, aes(x = DAT_step,
fill = variable, weight = weight)) +
geom_density(alpha = 0.9) +
theme(panel.background = element_blank()) +
scale_fill_manual(guide=FALSE,
values = bluegreen(length(unique(inscr_steps$variable)))) +
scale_x_continuous(breaks = seq(from = -800, to = 800, by = 100),
limits = c(-800,800), name = "") +
facet_wrap(variable ~ ., ncol = 1) +
theme(strip.text.x = element_text(size=8),
strip.background = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank(),
panel.grid.major.x = element_line(linetype = "dashed",
color = "gray30"),
panel.grid.minor.x = element_line(linetype = "dotted",
color = "gray80")) +
labs(title = "Epigraphic Evidence from Bithynia",
subtitle = "Spatio-temporal distribution",
caption = attributes(inscriptions)$source)
