Streamlines

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
)

Arguments

mapping	Set of aesthetic mappings created by `aes()` or `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.
data	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)`).
stat	The statistical transformation to use on the data for this layer, as a string.
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
...	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.
L,	typical length of a streamline in x and y units
min.L	minimum length of segments to show
res,	resolution parameter (higher numbers increases the resolution)
S	optional numeric number of timesteps for integration
dt	optional numeric size "timestep" for integration
xwrap, ywrap	vector of length two used to wrap the circular dimension.
skip	numeric specifying number of gridpoints not to draw in the x and y direction
skip.x	numeric specifying number of gridpoints not to draw in the x and y direction
skip.y	numeric specifying number of gridpoints not to draw in the x and y direction
n, nx, ny	optional numeric indicating the number of points to draw in the x and y direction (replaces `skip` if not `NULL`)
jitter, jitter.x, jitter.y	amount of jitter of the starting points
arrow.angle	parameters passed to grid::arrow
arrow.length	parameters passed to grid::arrow
arrow.ends	parameters passed to grid::arrow
arrow.type	parameters passed to grid::arrow
arrow	specification for arrow heads, as created by arrow().
lineend	Line end style (round, butt, square).
na.rm	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
show.legend	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.
inherit.aes	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()`.
geom	The geometric object to use display the data

Details

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.

Aesthetics

stat_streamline understands the following aesthetics (required aesthetics are in bold)

x
y
dx
dy
alpha
colour
linetype
size

Computed variables

step: step in the simulation
dx: dx at each location of the streamline
dy: dy at each location of the streamline

Examples

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 ..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 = ..step..,
                        color = sqrt(..dx..^2 + ..dy..^2), size = ..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()
 }

Arguments

Details

Aesthetics

Computed variables

See also

Examples