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title: "03 Plotting with `quickPlot`"
- "Eliot J. B. McIntire"
date: '`r strftime(Sys.Date(), "%B %d %Y")`'
fig_width: 7
number_sections: yes
self_contained: yes
toc: yes
vignette: >
%\VignetteIndexEntry{03 Plotting with quickPlot}
%\VignetteDepends{data.table, ggplot2, grDevices, igraph, RColorBrewer, quickPlot, stats}
%\VignetteKeyword{visualization, plotting, mapping}
```{r initial-clearPlot, eval=TRUE, echo=FALSE, fig.keep='none', message=FALSE}
# Plotting in `quickPlot`
Most visualization approaches in R (*e.g.*, `base`, `RgoogleVis`, `ggplot2`, `rgl`) are not optimized for speed or modularity.
Rather, they are designed to build publication-quality or interactive figures that do not need to be redrawn hundreds or more times in rapid succession.
Plotting requirements for many other uses, including simulation modeling, are different.
Changes to data, maps, objects can happen rapidly and repeatedly.
Visualizing this can be critical, especially during the model building phase.
Furthermore, no plotting method has been designed for modularity -- the ability to plot something without being aware of what is already there.
`quickPlot` comes with a plotting function to fulfill these two gaps.
The main plotting function, `Plot` (*i.e.*, with a capital `P`), is built using the `grid` package.
We have specifically built a plotting system that allows for relatively fast plotting of rasters (in the `raster` package), `SpatialPoints`, `SpatialPolygons`, `SpatialLines` (all within the `sp` package), `ggplot` objects, histogram, and igraph (package `igraph`) objects with the ability to make multi-frame plots without knowing which plots are already plotted.
In other words, the main plotting function can handle modular plotting, where each call to `Plot` adds plots, without each knowing what the current state of the active plotting device is.
This means that plotting can be treated as modular.
The `Plot` function is therefore intended to be used as a way to interact visually especially during development of rapid, repeated visualizations, such as simulation modeling.
If fine tuning and customization are desired, other plotting tools may be more suited (*e.g.*, `ggplot2`, or a dedicated GIS program).
To demonstrate plotting, we first load some maps.
These maps are randomly generated maps that come with the `quickPlot` package.
In the code snippet below, we create the list of files to load, which is every file in the `maps/` subdirectory of the package.
Then we load that list of files.
```{r load_files, eval=TRUE, echo=TRUE, message=FALSE, fig.height=2}
# Make list of maps from package database to load, and what functions to use to load them
# omit forestAge and percentPine maps for simplicity
files <- system.file("maps", package = "quickPlot") %>%
dir(., full.names = TRUE, pattern = "tif")
filelist <- data.frame(file = files[-c(2, 5) ], stringsAsFactors = FALSE)
# Load files to memory (using rasterToMemory), assign to a simList we call maps
maps <- lapply(filelist$file, function(x) raster(x))
names(maps) <- sapply(basename(filelist$file), function(x) {
strsplit(x, split = "\\.")[[1]][1]
# put into a single stack object in the simulation environment for ease of use below
landscape <- stack(maps$DEM, maps$forestCover, maps$habitatQuality)
# The `Plot` function
There are several features of `Plot` that are worth highlighting.
A primary purpose of `Plot` is to plot spatial type objects; however, as noted below, other object types can also be plotted.
Spatial objects can be layered on top of one another; non-spatial objects can not.
## Spatial layer types
Specifically, it currently can plot `RasterLayer`, `RasterStack`, `SpatialPoints*`, `SpatialPolygons*`, and `SpatialLines*` spatial objects.
Because `Plot` uses the `grid` graphics system, changing plot parameters is done with the `gp=gpar()` designation.
See `?gpar` for options.
```{r first_plot, eval=TRUE, echo=TRUE, fig.height=2}
Plot(landscape, new = TRUE)
# make a SpatialPoints object
caribou <- SpatialPoints(coords = cbind(x = stats::runif(1e2, -50, 50),
y = stats::runif(1e2, -50, 50)))
Plot(caribou, addTo = "landscape$habitatQuality")
# from SpatialPolygons help file
sr1 <- Polygon(cbind(c(2, 4, 4, 1, 2), c(2, 3, 5, 4, 2)) * 20 - 50)
sr2 <- Polygon(cbind(c(5, 4, 2, 5), c(2, 3, 2, 2)) * 20 - 50)
srs1 <- Polygons(list(sr1), "s1")
srs2 <- Polygons(list(sr2), "s2")
spP <- SpatialPolygons(list(srs1, srs2), 1:2)
Plot(spP, addTo = "landscape$habitatQuality", gp = gpar(lwd = 2))
# from SpatialLines help file
l1 <- cbind(c(10, 2, 30), c(30, 2, 2))
l1a <- cbind(l1[, 1] + .05, l1[, 2] + .05)
l2 <- cbind(c(1, 20, 3), c(10, 1.5, 1))
sl1 <- Line(l1)
sl1a <- Line(l1a)
sl2 <- Line(l2)
s1 <- Lines(list(sl1, sl1a), ID = "a")
s2 <- Lines(list(sl2), ID = "b")
sl <- SpatialLines(list(s1, s2))
Plot(sl, gp = gpar(col = c("red", "blue"), lwd = 2), addTo = "landscape$DEM")
Any combination of `RasterStack`, `RasterLayer`, `SpatialPoints*`, and `SpatialPolygons*` objects can be plotted from a single invocation of `Plot`:
```{r mixing_layer_types, eval=TRUE, echo=TRUE, fig.height=5}
Plot(landscape, caribou, maps$DEM, spP, axes = TRUE,
gp = gpar(cex = 0.5), visualSqueeze = 0.65)
## Other object types
### `ggplot2` objects
`Plot` can also handle gg objects as created with `qplot` or `ggplot`.
These can be combined with spatial objects as well.
```{r ggplot, eval=TRUE, echo=TRUE, cache=FALSE, fig.height=2}
ggObj <- qplot(stats::rnorm(1e3), binwidth = 0.1)
Plot(caribou, axes = "L", new = TRUE)
### `base` plotting
`Plot` can is also a wrapper around `plot.default`.
The advantage of using `Plot` is that you can place individual plots by named area, or have the `Plot` function do that automatically.
The `Plot` function can only handle one of these at a time, unlike the previous cases.
Most arguments that can be passed into `plot` should work here.
Note, as with other objects passed to `Plot`, there must be a way for R to refind the data in order to replot it if a rearranged windows occurs.
To do this, the user can provide a name for the plot, via `addTo` argument.
If there is not yet an object with that name, then it will create it.
If no `addTo` is passed in, then it will give the default, `basePlot1`, name.
If using `addTo`, and there are both x and y arguments, it will *add* points to the previous plot.
```{r base-objects, eval=FALSE, echo=TRUE, cache=FALSE, fig.height=2}
baseObj <- rnorm(1e3)
baseObj2 <- baseObj * 1.2 + rnorm(1e3)
Plot(baseObj, axes = "L", ylab = "Something meaningful")
Plot(baseObj, baseObj2, addTo = "scatterplot", axes = TRUE)
newPoints <- rnorm(10)
newPoints2 <- newPoints * 1.2 + rnorm(10)
Plot(newPoints, newPoints2, addTo = "scatterplot", col = "red")
### Other classes
`Plot` methods can be extended to accommodate other classes.
See package `SpaDES` for examples.
## Colours
We likely won't want the default colours for every map.
There are two ways to change the colour of a map: by adding a `colortable` to a `Raster*` object (*e.g.*, using the `setColors` function in `quickPlot`), or using the `cols` argument of the `Plot` function.
Adding a `colortable` to a `Raster*` will be more persistent (*i.e.*, it will stay with the same colour table even if the device is wiped clean or a new device is created.
Every `RasterLayer` can have a `colortable`, which gives the mapping of raster values to colours.
If not already set in the file (many `.tif` files and other formats already have their `colortable` set), we can use `setColors(Raster*)` with a named list of colours, if a `RasterStack`, or just a vector of colours if only a single `RasterLayer`.
These can be easily built with the `RColorBrewer` package, with the function `brewer.pal()`, `colorRampPalette()`, `heat.colors()`, or just a character vector of colours that will be interpolated.
Note that overplotting (accomplished with `addTo=`) will not overplot the legend; in general, overplotting should be used for cases where the maps are compatible with the underlying map layer.
See overplotting below.
`zero.color` is an optional string indicating the colour for zero values, when zero is the minimum value, otherwise, it is treated as any other colour.
The default is `NULL`.
Use `"white"` or another colour if this default is not desired.
`na.color` is an optional string indicating the colour for `NA` values.
The default is transparent.
Use `"white"` or another colour if this default is not desired.
See text about `na.color` below after raster values have changed.
```{r set_colours, eval=TRUE, echo=TRUE, fig.height=2}
# can change colour palette
Plot(landscape) # original
mapColours <- list(
DEM = topo.colors(50),
forestCover = colorRampPalette(c("blue", "orange", "purple", "red"))(50),
habitatQuality = brewer.pal(9, "Spectral")
setColors(landscape, n = 50) <- mapColours
Plot(landscape, new = TRUE) # oh, how pretty!
## `Plot` arguments
### `grid` graphical parameters: `gp`, `gpText`, `gpAxis`, `size`
These parameters will specify plot parameters that are available with `gpar()`.
`gp` will adjust plot parameters, `gpText` will adjust the title and legend text only, `gpAxis` will adjust the axes only.
`size` adjusts point size in a `SpatialPoints` object.
Multiple entries can be used, but they must be passed as list elements and they will be matched to the `...` items to plot.
```{r gp_gpAxis_gpText, eval=TRUE, echo=TRUE, fig.height=2}
Plot(caribou, gpAxis = gpar(cex = 0.4), size = 1)
Plot(maps$DEM, gpText = gpar(cex = 0.4))
Plot(maps$DEM, caribou, gpText = list(gpar(cex = 2), gpar(cex = 0.1)), new = TRUE)
### `visualSqueeze`
Under most circumstances, the plotting regions will be automatically scaled to maximize the area taken by the map layers, minimizing white space, but allowing axes, legends and titles to be visible when they are plotted.
In some devices and some situations, this automatic scaling is imperfect, so axes or legends may be squished.
The `visualSqueeze` argument is an easy way to shrink or grow the plots on the device.
The default value is 0.75 representing ~75% of the area.
If the plots need to be slightly smaller, this could be set to 0.6; if they can be larger, `visualSqueeze` could be set to 0.8.
`visualSqueeze` is 1 for fully filled device.
```{r visualSqueeze, eval=TRUE, echo=TRUE, fig.height=2}
# x axis gets cut off in pdf and html
Plot(maps$DEM, visualSqueeze = 0.6, new = TRUE)
A key reason why the legends or axes are cut off sometimes is because there is a minimum threshold for font size for readability.
So, either `visualSqueeze` can be set or making a larger device will usually also solve these problems.
### `legend` and `legendRange`
`legend` is a logical indicating whether a legend should automatically be added (the default).
`legendRange` will allow overriding of the default automatic legend creation.
This is useful in situations where, say, an version of a raster at the initial stage of a simulation does not cover the range of the eventual values of that raster.
Since the default is to automatically scale the legend to the values contained in the raster at the first time it is drawn, the legend will not cover the whole necessary range.
Adding `legendRange = 0:20`, where `0:20` covers the expected range of all stages of that raster, will work.
```{r legendRange, eval=TRUE, echo=TRUE, fig.height=2}
Plot(maps$DEM, legendRange = c(0, 500), new = TRUE)
### `zoomExtent`
A plot can be made with a custom zoom.
This can either be done by cropping the object, or by doing a `Plot` call, passing an extent object via `zoomExtent`.
A common way of using this would be to initially `Plot` an object, then use `clickExtent`, assigning the new extent to an object.
Then call `Plot(obj, zoomExtent=newZoom)`.
This new zoom will persist with rearrangements.
```{r zoomExtent, eval=TRUE, echo=TRUE, fig.height=2}
Plot(maps$DEM, zoomExtent = extent(c(-1, 10, -1, 20)), new = TRUE)
### `length`
For `SpatialLines*` objects, length will give arrow head length, in inches, meaning that the lines will be arrows.
```{r arrows, eval=TRUE, echo=TRUE, fig.height=2}
Plot(sl, addTo = "maps$DEM", length = 0.2)
# Modularity
One of the main purposes of the `Plot` function is modularity.
The goal is to enable any `SpaDES` module to be able to add a plot to the plotting device, without being aware of what is already in the plotting device.
To do this, each open plotting device has a corresponding `.quickPlot` object in the `.quickPlotEnv`, that gets created when a first Plot function is called.
This object keeps the layer names, their extents, and whether they were in a RasterStack (and a few other things).
So, when a new `Plot` is called, and `new` is used, then it will simply add the new layer.
There may not be space on the plot device for this, in which case, everything will be replotted in a new arrangement, but taking the original R objects.
This is different than the grid package engine for replotting.
That engine was not designed for large numbers of plots to be added to a region; it slows down immensely as the number of plots increases.
## The `new` argument
The `new` argument wipes the individual plots that are being added to (via `addTo` explicitly or implicitly because it is the same object name as a current plot), and plots the new object or base plot in the stead of the old one. This can be used for changing any `Plot` argument, legend etc. The title, however, remains.
This argument can be passed in as a list if there are several objects being replotted. This list version is somewhat experimental so may not work as desired.
```{r new-as-list}
Plot(landscape, new = list(TRUE, FALSE, TRUE), axes = "L")
## Calling Plot again and again
Plot is meant to be modular, so, making a Plot call does not wipe clear any previous plots. Rather, it adds the new objects or base plots. This may require a re-arrangement of the device. When `Plot` is called and a new plot is being added to an existing plot, the behaviour depends on how much blank space is currently available.
### If there is a blank spot for a new plot
The `Plot` function simply adds the new plot in the available empty space.
```{r simple_add, eval=TRUE, echo=TRUE, fig.height=3}
# can add a new plot to the plotting window
Plot(caribou, new = FALSE, axes = FALSE)
### If there is *not* a blank spot for a new plot
The `Plot` function creates a new arrangement, keeping the pre-existing order of plots, and adding the new plots afterwards.
The plots will all be a little bit smaller (assuming the device has not changed size), and they will be in different locations on the device.
```{r add_with_rearrangement, eval=TRUE, echo=TRUE, fig.height=2}
# can add a new plot to the plotting window
Plot(caribou, new = FALSE, axes = FALSE)
### A pre-existing name to a device
The `Plot` function will overplot the new layer in the location as the layer with the same name.
If colours in the layer are not transparent, then this will effectively block the previous plot.
The legend will remain as per the original legend, unless `new = TRUE` is used.
```{r add_with_same_name, eval=TRUE, echo=TRUE, fig.height=2}
landscape$forestCover[] = ((landscape$forestCover[] + 10) %% 30)
# can add a new plot to the plotting window
Plot(landscape, new = FALSE)
# note: zeros are treated as no colour by default.
# if this is not the correct behavior, can use `zero.color`
Plot(landscape, new = FALSE, zero.color = "blue")
# Plotting Speed
A second main purpose of the `Plot` function is to plot as fast as possible so that visual updates, which may be frequent, take as little time as possible.
To do this, several automatic calculations are made upon a call to `Plot`.
First, the number of plots is compared to the physical size of the device window.
If the layers are `RasterLayer`s, then they are subsampled before plotting, automatically scaled to the number of pixels that would be discernible by the human eye.
If the layer is a `SpatialPoints*` object, then a maximum of 10,000 points will be plotted.
These defaults can be adjusted by using the `speedup` argument.
Broadly, `speedup` is a positive real number (default is 1).
Values greater than 1 should plot faster; numbers between 0 and 1 should plot slower.
There will be exceptions to this (see next), so trial and error for a particular situation is the only way to determine for certain what speedups will be gained.
## `speedup`
The `speedup` argument is a *relative* speed increase at the cost of resolution if it is greater than 1.
If it is between 0 and 1, it will be a relative speed decrease at the gain of resolution.
This may be used successfully when the layer texture is particularly coarse, *i.e.*, there are clusters of identical pixels, so subsampling will have little effect.
In the examples below, the speedup gains are modest because the Rasters are relatively small (10,000 pixels).
This speed gain will be much greater for larger rasters.
For `SpatialPoints`, the default is to only plot 10,000 points; if there are more than this in the object, then a random sample will be drawn.
Speedup is used as the denominator to determine how many to plot, *i.e.*, `10000/speedup`.
In the example here, the speedup actually slows down plotting because the rasters are already very small.
This would not be the case when the original Raster had `1e8` pixels.
```{r speedup, eval=TRUE, echo=TRUE, fig.height=2}
system.time(Plot(landscape, caribou, maps$DEM, new = TRUE))
system.time(Plot(landscape, caribou, maps$DEM, speedup = 10, new = TRUE))
# can add a new plot to the plotting window
# Overplotting: `addTo`
There are times when it is useful to add a plot to a different plot with a different name.
In these cases, the `new` argument will not work.
The argument `addTo` will allow plotting of a `RasterLayer` or `SpatialPoints*` object on top of a `RasterLayer`, *that does not share the same name*.
This can be useful to see where agents are on a `RasterLayer`, or if there is transparency on a second `RasterLayer`, it could be plotted on top of a first `RasterLayer`.
```{r add, eval=TRUE, echo=TRUE, fig.height=2}
Plot(caribou, addTo = "landscape$DEM", size = 2)
# Clearing Plot
A device and all the meta-data associated with that device can be cleared with `clearPlot` function.
Sometimes, the plot state can be "illegal" because it was interrupted during a previous plotting attempt. This function can be used to clear the device and all the associated metadata manually. Use "force = TRUE" to close the device, and reopen it, which is a sure-fire way to clear everything.
```{r clearPlot, eval=TRUE, echo=TRUE, fig.height=2}
# Plotting in RStudio
The built-in RStudio plot window is particularly slow.
It is recommended to always create a new plotting device whenever real simulations are being done and they will be substantially faster.
Until this is fixed in RStudio, we have created a function, `dev(x)` which will add devices up to the number in the parenthesis, or switch to that device if it is already open.
If an RStudio plot has not been called, `dev()` will create a new device outside RStudio.
`dev()` can also be used with a number, as in `dev(x=4)` and this function will activate that device, if already exists, or open up all devices required in sequence from current device to `x`.
Since RStudio uses 2 and 3 (for the png), a call to `dev(4)` will either create 3 new devices (device numbers 2, 3 and 4 because device number 1 is never used in R), or 1 new device if plotting to RStudio has already occurred.
To set the default graphics device to avoid using the RStudio one, use `dev.useRSGD(FALSE)`, which sets the `device` option for your platform in order to bypass the RStudio graphics device.
To reset this back to the RStudio device, use `dev.useRSGD(TRUE)`.
# Interacting with plots
## `clickValues`
This can be used to obtain the values on the plotting device at the locations of the mouse clicks.
This will work on multipanel plots.
Note that plotting of rasters with the grid package does not allow for partial pixels to be plotted at the edges of the raster.
As a result, the edges of pixels may not perfectly line up with the coordinates that they appear with.
*Do not rely on exact values when zoomed in*
```{r clickValues, eval=FALSE, echo=TRUE}
clickValues(3) # click at three locations on the Plot device
## `clickExtent`
This can be used like `zoom` for base package plot window.
Click two corners of a Plot.
```{r clickExtent, eval=FALSE, echo=TRUE}
clickExtent() # click at two locations on the Plot device
# Replotting or redrawing the entire plot window
`rePlot` can be used to redraw the entire plot window, either in the same frame as before (the default) or in a new frame.
If in a new frame, the new device will be created, then the entire Plot window will be redrawn in the new device.
This function can also be used to redraw everything after a window gets resized manually and objects become distorted.
Some plot arguments can be passed via `rePlot`, such as `visualSqueeze`, `axes`, but not all.
Further functionality like this will likely be in a future update.K
```{r rePlot, eval=FALSE, echo=TRUE, cache=FALSE}
rePlot(visualSqueeze = 1, axes = FALSE)
rePlot(visualSqueeze = 0.7, axes = FALSE, cols = "Reds")
# Assigning `Plot()` to object
The output of a `Plot` call can be assigned to an object for future plotting.
Simply pass the object name as if it were a Plot object.
Any data changes made to the objects contained within the plot, if they are in the `.GlobalEnv` or a `envir` will be reflected in the new plot, because the data are replotted from their original source.
This provides the same behavior as a call to `rePlot()`.
```{r Plot a .quickPlot object, eval=FALSE, echo=TRUE, cache=FALSE}
plots <- Plot(landscape)
# change values
landscape$forestCover[landscape$habitatQuality > 0.9] <- 0
# same as:
# but can be combined with other objects
Plot(caribou, plots)
Plot(caribou, plots, sl)