Streamlines are paths that are always tangential to a vector field. In the case of a steady field, it's identical to the path of a massless particle that moves with the "flow".
geom_streamline(
mapping = NULL,
data = NULL,
stat = "streamline",
position = "identity",
...,
L = 5,
min.L = 0,
res = 1,
S = NULL,
dt = NULL,
xwrap = NULL,
ywrap = NULL,
skip = 1,
skip.x = skip,
skip.y = skip,
n = NULL,
nx = n,
ny = n,
jitter = 1,
jitter.x = jitter,
jitter.y = jitter,
arrow.angle = 6,
arrow.length = 0.5,
arrow.ends = "last",
arrow.type = "closed",
arrow = grid::arrow(arrow.angle, grid::unit(arrow.length, "lines"), ends = arrow.ends,
type = arrow.type),
lineend = "butt",
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE
)
stat_streamline(
mapping = NULL,
data = NULL,
geom = "streamline",
position = "identity",
...,
L = 5,
min.L = 0,
res = 1,
S = NULL,
dt = NULL,
xwrap = NULL,
ywrap = NULL,
skip = 1,
skip.x = skip,
skip.y = skip,
n = NULL,
nx = n,
ny = n,
jitter = 1,
jitter.x = jitter,
jitter.y = jitter,
arrow.angle = 6,
arrow.length = 0.5,
arrow.ends = "last",
arrow.type = "closed",
arrow = grid::arrow(arrow.angle, grid::unit(arrow.length, "lines"), ends = arrow.ends,
type = arrow.type),
lineend = "butt",
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.
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.
typical length of a streamline in x and y units
minimum length of segments to show
resolution parameter (higher numbers increases the resolution)
optional numeric number of timesteps for integration
optional numeric size "timestep" for integration
vector of length two used to wrap the circular dimension.
numeric specifying number of gridpoints not to draw in the x and y direction
optional numeric indicating the number of points to draw in the
x and y direction (replaces skip if not NULL)
amount of jitter of the starting points
parameters passed to grid::arrow
specification for arrow heads, as created by grid::arrow().
Line end style (round, butt, square).
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
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().
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")
Streamlines are computed by simple integration with a forward Euler method.
By default, stat_streamline() computes dt and S from L, res,
the resolution of the grid and the mean magnitude of the field. S is
then defined as the number of steps necessary to make a streamline of length
L under an uniform mean field and dt is chosen so that each step is no
larger than the resolution of the data (divided by the res parameter). Be
aware that this rule of thumb might fail in field with very skewed distribution
of magnitudes.
Alternatively, L and/or res are ignored if S and/or dt are specified
explicitly. This not only makes it possible to fine-tune the result but also
divorces the integration parameters from the properties of the data and makes
it possible to compare streamlines between different fields.
The starting grid is a semi regular grid defined, either by the resolution of the
field and the skip.x and skip.y parameters o the nx and ny parameters,
jittered by an amount proportional to the resolution of the data and the
jitter.x and jitter.y parameters.
It might be important that the units of the vector field are compatible to the units
of the x and y dimensions. For example, passing dx and dy in m/s on a
longitude-latitude grid will might misleading results (see spherical).
Missing values are not permitted and the field must be defined on a regular grid, for now.
stat_streamline understands the following aesthetics (required aesthetics are in bold)
x
y
dx
dy
alpha
colour
linetype
size
step in the simulation
dx at each location of the streamline
dy at each location of the streamline
Other ggplot2 helpers:
DivideTimeseries(),
MakeBreaks(),
WrapCircular(),
geom_arrow(),
geom_contour2(),
geom_contour_fill(),
geom_label_contour(),
geom_relief(),
guide_colourstrip(),
map_labels,
reverselog_trans(),
scale_divergent,
scale_longitude,
stat_na(),
stat_subset()
if (FALSE) {
library(data.table)
library(ggplot2)
data(geopotential)
geopotential <- copy(geopotential)[date == date[1]]
geopotential[, gh.z := Anomaly(gh), by = .(lat)]
geopotential[, c("u", "v") := GeostrophicWind(gh.z, lon, lat)]
(g <- ggplot(geopotential, aes(lon, lat)) +
geom_contour2(aes(z = gh.z), xwrap = c(0, 360)) +
geom_streamline(aes(dx = dlon(u, lat), dy = dlat(v)), L = 60,
xwrap = c(0, 360)))
# The circular parameter is particularly important for polar coordinates
g + coord_polar()
# If u and v are not converted into degrees/second, the resulting
# streamlines have problems, specially near the pole.
ggplot(geopotential, aes(lon, lat)) +
geom_contour(aes(z = gh.z)) +
geom_streamline(aes(dx = u, dy = v), L = 50)
# The step variable can be mapped to size or alpha to
# get cute "drops". It's important to note that after_stat(dx) (the calculated variable)
# is NOT the same as dx (from the data).
ggplot(geopotential, aes(lon, lat)) +
geom_streamline(aes(dx = dlon(u, lat), dy = dlat(v), alpha = after_stat(step),
color = sqrt(after_stat(dx^2) + after_stat(dy^2)),
size = after_stat(step)),
L = 40, xwrap = c(0, 360), res = 2, arrow = NULL,
lineend = "round") +
scale_size(range = c(0, 0.6))
# Using topographic information to simulate "rivers" from slope
topo <- GetTopography(295, -55+360, -30, -42, res = 1/20) # needs internet!
topo[, c("dx", "dy") := Derivate(h ~ lon + lat)]
topo[h <= 0, c("dx", "dy") := 0]
# See how in this example the integration step is too coarse in the
# western montanous region where the slope is much higher than in the
# flatlands of La Pampa at in the east.
ggplot(topo, aes(lon, lat)) +
geom_relief(aes(z = h), interpolate = TRUE, data = topo[h >= 0]) +
geom_contour(aes(z = h), breaks = 0, color = "black") +
geom_streamline(aes(dx = -dx, dy = -dy), L = 10, skip = 3, arrow = NULL,
color = "#4658BD") +
coord_quickmap()
}