frames_graph: Create frames of movement-environment interaction graphs for...
In moveVis: Movement Data Visualization

Description Usage Arguments Details Value Author(s) See Also Examples

frames_graph creates a list of ggplot2 graphs displaying movement-environment interaction. Each object represents a single frame. Each frame can be viewed or modified individually. The returned list of frames can be animated using animate_frames.

frames_graph(
  m,
  r_list,
  r_times,
  r_type = "gradient",
  fade_raster = FALSE,
  crop_raster = TRUE,
  return_data = FALSE,
  graph_type = "flow",
  path_size = 1,
  path_legend = TRUE,
  path_legend_title = "Names",
  val_min = NULL,
  val_max = NULL,
  val_by = 0.1,
  verbose = T
)

`m`	`move` or `moveStack` of uniform time scale and time lag, e.g. prepared with `align_move` (recommended). May contain a column named `colour` to control path colours (see `details`).
`r_list`	list of `raster` or `rasterStack`. Each list element referrs to the times given in `r_times`. Use single-layer `raster` objects for gradient or discrete data (see `r_type`). Use a `rasterStack` containing three bands for RGB imagery (in the order red, green, blue).
`r_times`	list of `POSIXct` times. Each list element represents the time of the corresponding element in `r_list`. Must be of same length as `r_list`.
`r_type`	character, either `"gradient"` or `"discrete"`. Ignored, if `r_list` contains `rasterStacks` of three bands, which are treated as RGB.
`fade_raster`	logical, if `TRUE`, `r_list` is interpolated over time based on `r_times`. If `FALSE`, `r_list` elements are assigned to those frames closest to the equivalent times in `r_times`.
`crop_raster`	logical, whether to crop rasters in `r_list` to plot extent before plotting or not.
`return_data`	logical, if `TRUE`, instead of a list of frames, a `data.frame` containing the values extracted from `r_list` per individual, location and time is returned. This `data.frame` can be used to create your own multi- or monotemporal `ggplot2` movement-environemnt interaction graphs.
`graph_type`	character, defines the type of multi-temporal graph that should be drawn as frames. Currently supported graphs are: `"flow"`, a time flow graph with frame time on the x axis and values of the visited cell at x on the y axis per individual track `"hist"`, a cumulative histogram with cell values on the x axis and time-cumulative counts of visits on the y axis per individual track.
`path_size`	numeric, size of each path.
`path_legend`	logical, wether to add a path legend from `m` or not. Legend tracks and colours will be ordered by the tracks' temporal apperances, not by their order in `m`.
`path_legend_title`	character, path legend title. Default is `"Names"`.
`val_min`	numeric, minimum value of the value axis. If undefined, the minimum is collected automatically.
`val_max`	numeric, maximum value of the value axis. If undefined, the maximum is collected automatically.
`val_by`	numeric, increment of the value axis sequence. Default is 0.1. If `graph_type = "discrete"`, this value should be an integer of 1 or greater.
`verbose`	logical, if `TRUE`, messages and progress information are displayed on the console (default).

To later on side-by-side join spatial frames created using frames_spatial with frames created with frames_graph for animation, equal inputs must have been used for both function calls for each of the arguments m, r_list, r_times and fade_raster.

List of ggplot2 objects, each representing a single frame. If return_data is TRUE, a data.frame is returned (see return_data).

Jakob Schwalb-Willmann

frames_spatial join_frames animate_frames

library(moveVis)
library(move)
library(ggplot2)

data("move_data", "basemap_data")
# align movement
m <- align_move(move_data, res = 4, unit = "mins")

r_list <- basemap_data[[1]]
r_times <- basemap_data[[2]]


# use the same inputs to create a non-spatial graph, e.g. a flow graph:
frames.gr <- frames_graph(m, r_list = r_list, r_times = r_times, r_type = "gradient",
                          fade_raster = TRUE, graph_type = "flow")
# take a look
frames.gr[[100]]

# make a histogram graph:
frames.gr <- frames_graph(m, r_list = r_list, r_times = r_times, r_type = "gradient",
                          fade_raster = TRUE, graph_type = "hist")
# change the value interval:
frames.gr <- frames_graph(m, r_list = r_list, r_times = r_times, r_type = "gradient",
                          fade_raster = TRUE, graph_type = "hist", val_by = 0.01)
                          
frames.gr[[100]]
# manipulate the labels, since now they are very dense:
# just replace the current scale
frames.gr <- add_gg(frames.gr, expr(scale_x_continuous(breaks=seq(0,1,0.1),
                                    labels=seq(0,1,0.1), expand = c(0,0))))
frames.gr[[100]]

# the same can be done for discrete data, histogram will then be shown as bin plots

# to make your own graphs, use frames_graph to return data instead of frames
frames.gr <- frames_graph(m, r_list = r_list, r_times = r_times, r_type = "gradient",
                        fade_raster = TRUE, return_data = TRUE)

# then simply animate the frames using animate_frames 
# see all add_ functions on how to customize your frames created with frames_spatial
# or frames_graph

# see ?animate_frames on how to animate your list of frames