This `geom` calculates sampling quantiles and draws a ribbon for the quantile range plus a line for the median (50% quantile).
geom_flquantiles(
mapping = NULL,
data = NULL,
stat = "FLQuantiles",
position = "identity",
show.legend = NA,
inherit.aes = TRUE,
na.rm = FALSE,
probs = c(0.1, 0.5, 0.9),
alpha = 0.5,
...
)
stat_flquantiles(
mapping = NULL,
data = NULL,
geom = "line",
position = "identity",
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE,
...
)
Set of aesthetic mappings created by aes()
. If specified and
inherit.aes = TRUE
(the default), it is combined with the default mapping
at the top level of the plot. You must supply mapping
if there is no plot
mapping.
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot()
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify()
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame
, and
will be used as the layer data. A function
can be created
from a formula
(e.g. ~ head(.x, 10)
).
The statistical transformation to use on the data for this
layer, either as a ggproto
Geom
subclass or as a string naming the
stat stripped of the stat_
prefix (e.g. "count"
rather than
"stat_count"
)
Position adjustment, either as a string naming the adjustment
(e.g. "jitter"
to use position_jitter
), or the result of a call to a
position adjustment function. Use the latter if you need to change the
settings of the adjustment.
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders()
.
If FALSE
, the default, missing values are removed with
a warning. If TRUE
, missing values are silently removed.
Quantiles to compute and draw, defaults to c(0.10, 0.90).
Transparency for quantile ribbon.
Other arguments passed on to layer()
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
colour = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.
The geometric object to use to display the data, either as a
ggproto
Geom
subclass or as a string naming the geom stripped of the
geom_
prefix (e.g. "point"
rather than "geom_point"
)
As this `geom` outputs two layers, although based on different `geoms`, interactions between common parameters need to be considered. The `fill` parameter will only affect the quantile range `ribbon`, but `colour` will be passed to both the `ribbon` and median `line` layers. The defaults are no lines on the quantiles and "black" for the median line. The `alpha` value has been hard coded to 1 for the median line, so only affects the quantile `ribbon`. To change this, call `stat_flquantiles` directly, as in the examples below.
`stat_flquantiles` will return between one and three `y` values depending on the number of quantiles requested. If two quantiles are to be calculated, it will return the corresponding `ymin` and `ymax`, to be used with, for example, `geom_ribbon`. If only one quantile is to be calculated, it will be returned as `y`, to be used typically by `geom_line`. Finally, if three values are passed in the `probs` argument, all of the above will be returned, in the right order.
`geom_flquantiles` understands the following aesthetics (required aesthetics are in bold): - `*x*` - `*y*` - `alpha` - `colour` - `fill` - `group` - `linetype` - `linewidth` where some of them apply to the ribbons and some of them to the lines.
quantile, if only one requested or central one when if three
lower quantile, if two or three requested
upper quantile, if two or three requested
data(ple4)
flq <- rnorm(250, catch(ple4), 200000)
ggplot(flq, aes(x=date, y=data)) +
geom_flquantiles(probs=c(0.25, 0.50, 0.75), fill="red", alpha=0.25)
# Draw two quantiles with two calls to geom_flquantiles
ggplot(flq, aes(x=date, y=data)) +
geom_flquantiles(probs=c(0.25, 0.50, 0.75), alpha=0.25, fill="red") +
geom_flquantiles(probs=c(0.10, 0.90), alpha=0.15, fill="red")
# Use it on an FLQuants, colouring by their name
flqs <- FLQuants(A=rnorm(250, catch(ple4), 200000),
B=rnorm(250, stock(ple4), 200000))
ggplot(flqs, aes(x=date, y=data, colour=qname)) +
geom_flquantiles(probs=c(0.10, 0.50, 0.90), aes(fill=qname), alpha=c(0.30))
# Or facet them
ggplot(flqs, aes(x=date, y=data)) +
geom_flquantiles(probs=c(0.10, 0.50, 0.90), fill="red", alpha=c(0.30)) +
facet_grid(qname~.)
# For greater control, call stat_flquantiles directly with a geom
ggplot(flq, aes(x=year, y=data)) +
stat_flquantiles(probs=c(0.10, 0.90), geom = "ribbon",
fill="yellowgreen", alpha=0.30) +
stat_flquantiles(probs=c(0.01), geom = "line",
colour = "green4", linetype=3) +
stat_flquantiles(probs=c(0.99), geom = "line",
colour = "green4", linetype=3) +
stat_flquantiles(probs=c(0.25, 0.75), geom = "ribbon",
fill="green4", alpha=0.30) +
stat_flquantiles(probs=c(0.50), geom = "line", linewidth=1.5,
colour = "lightgreen") +
stat_flquantiles(probs=c(0.50), geom = "line",
colour = "darkgreen")