To visualize time series over space.

Usage

stlines(
  stdata,
  spatial,
  group = NULL,
  nmax.group = NULL,
  xscale = 1,
  yscale = 1,
  colour = NULL,
  ...
)

stpoints(
  stdata,
  spatial,
  group = NULL,
  nmax.group = NULL,
  xscale = 1,
  yscale = 1,
  colour = NULL,
  ...
)

Arguments

stdata: matrix with the data, each column is a location.
spatial: an object with one of class defined in the sp package.
group: an integer vector indicating to which spatial unit each time series belongs to. Default is NULL and them it is assumed that each time series belongs o each spatial unit.
nmax.group: an integer indicating the maximum number of time series to be plotted over each spatial unit. Default is NULL, so all will be drawn.
xscale: numeric to define a scaling factor in the horizontal direction.
yscale: numeric to define a scaling factor in the vertical direction.
colour: color (may be a vector, one for each time series). Default is NULL and it will generate colors considering the average of each time series. These automatic colors are defined using the rgb() function with alpha=0.5. It considers the relative rank of each time series mean, r. r is then used for red, 1-r is used for blue and a triangular function, 1-2*|1-r/2|, is considered for green. That is, time series with mean among the lowest time series averages are shown in blue and those among the highest temperatures are shown in red. The transition from blue to red goes so that the intermediate ones are shown in light green.
...: further arguments to be passed for the lines function.

Value

add lines to an existing plot

Details

Scaling the times series is needed before drawing it over the map. The area of the bounding box for the spatial object divided by the number of locations is the standard scaling factor. This is further multiplied by the user given xcale and yscale.

Functions

stlines(): each time series over the map centered at the location.
stpoints(): each time series over the map centered at the location.

Warning

if there are too many geographical locations, it will not look good