Plot to specific plot in multiple-plot window? - r

If I create a multi-plot window with par(mfrow=...), is it possible to send data to a specific plot (i.e. "the one in the lower left corner") or is the plotting always necessarily sequential? Is there a package for R that does something like this?
For those that are interested, this problem arises out of the fact that R is a single-threaded application and is not ideal for real-time visualization. I have multiple real-time data streams coming into R from an outside source that produces the data asynchronously (and therefore the data streams don't always come in the same order). This results in R flipping around the order of the data visualization plots every time it updates.

You could use split.screen():
par(bg = "white") # erase.screen() will appear not to work
# if the background color is transparent
# (as it is by default on most devices).
split.screen(c(2,1)) # split display into two screens
split.screen(c(1,3), screen = 2) # now split the bottom half into 3
screen(1) # prepare screen 1 for output
plot(10:1)
screen(4) # prepare screen 4 for output
plot(10:1)

Have a look at help(layout). This allows you to specify the what, where and in which sizes.
Once plotted, I don't think you re-plot just partially. But you you can use dev.set() et al to switch between different 'plot devices' (ie windows); see help(dev.list).

Note that the suggested answer here is to use split.screen(). It may work, but according to the split.screen help file: "The recommended way to use these functions is to completely draw a plot and all additions (i.e. points and lines) to the base plot, prior to selecting and plotting on another screen. The behavior associated with returning to a screen to add to an existing plot is unpredictable and may result in problems that are not readily visible."
In an answer to my question, there is a more useful solution, using the par(mfg) option:
Change plot panel in multipanel plot in R

Another option is that of implementing a little GUI e.g. with RGtk2 or RTclTk.
I generally do this for graphs that I want to change in realtime and it works great.
For instance, with RGtk2 and cairoDevice you could just do something like (I assume you have a Glade interface)
# Helper function to get a widget from the Glade interface
getWidget <- function(name)
{
return (interface$getWidget(name))
}
interface <- gladeXMLNew("interface.glade", root="mainWindow")
# Our cairo devices (to draw graphics).
# plot1, plot2, and plot3 are GtkDrawingArea widgets
asCairoDevice(getWidget("plot1"))
# dev.cur() will give the device number of the last device we created
# You'll use this to switch device when you draw in different plots
# Storing the device number is important because you may have other
# devices open from other unrelated plots
# (so never assume they'll just start from 1 and be sequential!!!)
plot1.dev <- as.integer(dev.cur())
asCairoDevice(getWidget("plot2"))
plot2.dev <- as.integer(dev.cur())
asCairoDevice(getWidget("plot3"))
plot3.dev <- as.integer(dev.cur())
# To draw in a specific plot you just do
dev.set(plot2.dev)
plot(....)
This has many other advantages, like that of being able to positions the graphs easily where you want (using Glade Interface Designer) and having the possibility of user interaction through specific buttons (e.g. you may have a "pause acquisition" button).

Related

How to add node size as legend in Cytoscape 3?

From an R function (cnetplot) I've obtained the following image which does not look very nice.
Therefore, I extracted the data from the R object and wrote a script to create an equivalent network data file that is readable by Cytoscape. The following equivalent plot from Cytoscape looks much better but the problem is that I am not able to add legends based on node size in Cytoscape as the R function did. I tried with Legend Creator app in cytoscspe but couldn't do it.
The original data and R code to reproduce the plots can be found in the following link.
ftp://ftp.lrz.de/transfer/Data_For_Plot/StackOverflow/
I looked into this Mapping nodes sizes and adding legends in cytoscape network, but in that case questioner already was able to load the node sizes as legends in cytoscape and moreover, he/she used a python package.
Any suggestions will highly be appreciated
Here's a little R script that will generate a min/max node size legend. You'll need to set the first variable to the name of the Visual Style in your network. This one works with the sample session file, "Yeast Perturbation.cys" if you want to test it there first.
If you are familiar with RCy3, then it should be self-explanatory. You can customize the positioning of the nodes, labels and label font size, etc. You can even adapt it to generate intermediate values (like in your example above) if you want.
NOTE: This adds nodes to your network. If you run a layout after adding these, then they will be moved! If you rely on node counts or connectivity measures, then these will affect those counts! Etc.
If you find this uesful, I might try to add it as helper function in the RCy3 package. Let me know if you have feedback or questions.
# https://bioconductor.org/packages/release/bioc/html/RCy3.html
library(RCy3)
# Set your current style name
style.name <- "galFiltered Style"
# Extract min and max node size
res<-cyrestGET(paste0("styles/",style.name,"/mappings/NODE_SIZE"))
size.col <- res$mappingColumn
min.size <- res$points[[1]]$equal
min.value <- res$points[[1]]$value
max.size <- res$points[[length(res$points)]]$equal
max.value <- res$points[[length(res$points)]]$value
# Prepare as data.frame
legend.df <-data.frame(c(min.size, max.size), c(min.value, max.value))
colnames(legend.df) <- c("legend.label",size.col)
rownames(legend.df) <- c("legend.size.min", "legend.size.max")
# Add legend nodes and data
addCyNodes(c("legend.size.min", "legend.size.max"))
loadTableData(legend.df)
# Style and position
setNodeColorBypass(c("legend.size.min", "legend.size.max"),"#000000")
setNodePropertyBypass(c("legend.size.min", "legend.size.max"),
c("E,W,l,5,0", "E,W,l,5,0"), # node_anchor, label_anchor, justification, x-offset, y-offset
"NODE_LABEL_POSITION")
setNodeLabelBypass(c("legend.size.min", "legend.size.max"), legend.df$legend.label)
setNodePropertyBypass("legend.size.max",
as.numeric(max.size)/2 + as.numeric(min.size)/2 + 10, # vertical spacing
"NODE_Y_LOCATION")
setNodeFontSizeBypass(c("legend.size.min", "legend.size.max"), c(20,20))

How to rasterize a single layer of a ggplot?

Matplotlib allows to rasterize individual elements of a plot and save it as a mixed pixel/vector graphic (.pdf) (see e.g. this answer). How can the same achieved in R with ggplot2?
The following is a toy problem in which I would like to rasterize only the geom_point layer.
set.seed(1)
x <- rlnorm(10000,4)
y <- 1+rpois(length(x),lambda=x/10+1/x)
z <- sample(letters[1:2],length(x), replace=TRUE)
p <- ggplot(data.frame(x,y,z),aes(x=x,y=y)) +
facet_wrap("z") +
geom_point(size=0.1,alpha=0.1) +
scale_x_log10()+scale_y_log10() +
geom_smooth(method="gam",formula = y ~ s(x, bs = "cs"))
print(p)
ggsave("out.pdf", p)
When saved as .pdf as is, Adobe reader DC needs ~1s to render the figure. Below you can see a .png version:
Of course, it is often possible to avoid the problem by not plotting raw data
Thanks to the ggrastr package by Viktor Petukhov & Evan Biederstedt, it is now possible to rasterize individual layers. However, currently (2018-08-13), only geom_point and geom_tile are supported. and work by Teun van den Brand it is now possible to rasterize any individual ggplot layer by wrapping it in ggrastr::rasterise():
# install.packages('devtools')
# remotes::install_github('VPetukhov/ggrastr')
df %>% ggplot(aes(x=x, y=y)) +
# this layer will be rasterized:
ggrastr::rasterise(geom_point(size=0.1, alpha=0.1)) +
# this one will still be "vector":
geom_smooth()
Previously, only a few geoms were supported:
To use it, you had to replace geom_point by ggrastr::geom_point_rast.
For example:
# install.packages('devtools')
# devtools::install_github('VPetukhov/ggrastr')
library(ggplot2)
set.seed(1)
x <- rlnorm(10000, 4)
y <- 1+rpois(length(x), lambda = x/10+1/x)
z <- sample(letters[1:2], length(x), replace = TRUE)
ggplot(data.frame(x, y, z), aes(x=x, y=y)) +
facet_wrap("z") +
ggrastr::geom_point_rast(size=0.1, alpha=0.1) +
scale_x_log10() + scale_y_log10() +
geom_smooth(method="gam", formula = y ~ s(x, bs = "cs"))
ggsave("out.pdf")
This yields a pdf that contains only the geom_point layer as raster and everything else as vector graphic. Overall the figure looks as the one in the question, but zooming in reveals the difference:
Compare this to an all-raster graphic:
I think you've set yourself up to not have this question answered. You write:
I expect an answer to provide an extension to ggplot2 that allows to export plots with rasterized layers with minimal changes to to existing plotting commands, i.e. as wrapper for geom_... commands or as an additional parameter to these or a ggsave command that expects a list of unevaluated parts of a plot command (every second to be rasterized), not a hacky workaround as provided in the linked question.
This is a major development effort that could easily require several weeks or more of effort by a highly skilled developer. It's unlikely anybody will do this just because of a Stack Overflow question. In lieu of a functioning implementation, I'll describe here how one could implement what you're asking for and why it's rather challenging.
The players
Let's start with the key players we'll be dealing with. At the highest level sits the ggplot2 library. It takes data frames and turns them into figures. ggplot2 itself doesn't know anything about low-level drawing, though. It only deals with lines, polygons, text, etc., which it hands off to the grid library in the form of graphics objects (grobs).
The grid library itself is a fairly high-level library. It also doesn't know much about low-level drawing. It primarily deals with lines, polygons, text, etc., which it hands off to an R graphics device. The device does the actual drawing.
There are many different R graphics devices. Enter ?Devices in an R command line to see an incomplete list. There are vector-graphics devices, such as pdf, postscript, or svg, raster devices such as png, jpeg, or tiff, and interactive devices such as X11 or quartz. Obviously, rasterization as a concept only makes sense for vector-graphics devices, since raster devices raster everything anyways. Importantly, neither ggplot2 nor grid know or care which graphics device you're currently drawing on. They deal with graphical objects that can be drawn on any device.
Ideal high-level interface
The high-level interface should consist of an option rasterize in the layer() function of ggplot2. In this way, one could simply write, e.g., geom_point(rasterize = TRUE) to rasterize the points layer. This would work transparently for all geoms and stats, since they all call layer().
Possible implementations
I see four possible routes of implementation, ordered from most impossible to least.
1. Ideally, the layer() function would simply hand off the rasterize option to the grid library, which would hand it off to the graphics device to tell it which parts of the plot to rasterize. This approach would require major changes in the graphics device API. I don't see this happening. Not in my lifetime, at least.
2. Alternatively, one could write a new grob type that can take any arbitrary grob and rasterize it on demand when the grob is drawn on a graphics device. This approach would not require changes in the graphics device API, but it would require detailed knowledge of the low-level implementation of the grid library. It would also possibly make interactive viewing of such figures very slow.
3. A slightly simpler alternative to 2. would be to rasterize the arbitrary grob only once, on grob construction, and then reuse whenever that grob is drawn. This would be quite a bit faster on interactive graphics devices but the drawing would get distorted if the aspect ratio is changed interactively. Nevertheless, since the primary use of this functionality would be to generate pdf output (I assume), this option might be sufficient.
4. Finally, rasterization could also happen in the layer() function, and that function could simply place a regular raster grob into the grob tree. That solution is similar to the technique described here. Technically, it's not much different from 3. Either way, one needs to write code to rasterize a grob tree and then replace it by a raster grob.
Technical hurdles
To rasterize parts of the grob tree, we'd have to send them to an R raster graphics device to render. However, there isn't one that renders to memory. So, one would have to render to a temporary file (e.g., using png()), and then read the file back in. That's possible but ugly. It also depends on functionality (such as png()) that isn't guaranteed to be available on every R installation.
Second, to render parts of the grob tree separately from the overall rendering, we'll have to open a new graphics device in addition to the one currently open. That's possible but can lead to unexpected bugs. I'm dealing with such bugs all the time, see e.g. here or here for issues related to code using this technique. Whoever implements the rasterization functionality would have to deal with such issues.
Finally, we'll have to get the rasterization code accepted into the ggplot2 library, since we need to replace the layer() function and I don't think there's a way to do that from a separate package. Given how hackish the rasterization solutions are going to be (see previous two paragraphs), that may be a tall order.

Optimize Plotly Parallel plot

I am doing a parallel plot with plotly. It works, but get very time consuming when I apply it to huge data, because I am doing as many "trace" as markers.
How can I do it faster?
There is a reproducible example showing how I do it yet :
#Create data
markers=paste("markers",letters,sep="_")
data=data.frame(
mark=rep(markers,3),
map=c(rep(1,26) , rep(2,26) , rep(3,26)),
pos=sample(seq(1:100) , 26*3)
)
# Make the plot with Plotly
library(plotly)
plot_ly(data , x=map , y=pos, mode="markers+lines" , group=mark)
And that is the result
My current plotly parallel plot
Thank you so much for your help !
For each trace, plotly.js must calculate default plot and layout data, so the initial plot will not be very fast. For large data sets, you should use the scattergl plot type instead of scatter, but ideally, you should re-arrange your data so that you don't need to plot as many traces (if this is possible).
Now there's a native parallel coordinates trace parcoords in plotly.js which was built for scalable operations, e.g. uses the GPU for crossfiltering and rendering. Some of the styling is therefore more constrained than with regular lines. Besides the brush filtering, it also has axis drag and drop by grabbing the axis title.
Doc: https://plot.ly/javascript/reference/#parcoords
Examples:
https://plot.ly/javascript/parallel-coordinates-plot/
https://codepen.io/monfera/pen/dNBwOv
As usual, the functionality is accessible via the Python, R etc. API bindings too, e.g. https://plot.ly/python/parallel-coordinates-plot/
It's an older question, just adding this answer for future search hits on this page.

layout inside layout in R

I'm using R to create a heatmap from a matrix using heatmap.2 - and i want to group these images into one big image - What i usually use to achieve this is layout() - but this doesn't work, as heatmap.2 uses layout, and apparently layout does not work recursively.
Does anyone have any suggestions on how to group together 2 images without layout, or how to make layout support recursive calls?
mat = matrix(nrow=3,nrow=3,1:9)
layout(matrix(nrow=2,ncol=1))
heatmap.2(mat) ## overrides the layout and produces only one plot that takes whole screen
heatmap.2(mat) ## still only one image
thanks.
What follows is a hack that is almost certainly not a perfect solution, but it may get you started.
Create your own version of the heatmap.2 function called hm3. In the code for hm3, comment out all the lines between:
if (missing(lhei) || is.null(lhei))
and the layout call:
layout(lmat, widths = lwid, heights = lhei, respect = FALSE)
it's a big chunk of code, maybe 30 lines. Now the following code produces two heat maps with dendrograms and keys side by side:
x <- as.matrix(mtcars)
lhei <- c(1.5, 4,1.5,4)
lwid <- c(1.5, 4,1.5,4)
layout(rbind(c(4,3,8,7),c(2,1,6,5)),
widths = lwid, heights = lhei, respect = FALSE)
hm3(x)
hm3(x)
Clearly, this will require considerable tweaks to make it look nice (and a larger plotting area; I've squished everything to be a reasonable size to post here).
This is entirely untested. It is likely that using any of the options in the hm3 function that control the appearance of the plot will cause things to go slightly haywire. But this may be a good starting point for your own experimentation to address those issues.
What are you planning on doing with the results?
If you just want to compare 2 heatmaps side by side on the screen then rather than combine them into one single plot you can open 2 plotting devices and arrange them side by side to compare (much simpler than creating a single graph):
heatmap.2(mat1)
dev.new()
heatmap.2(mat2)
Now drag one to the side of the other using your mouse.
If you want to include the combined graphic in a publication then it may be easiest to create the 2 plots and just set them side by side in whatever program you are using to create the article. If you need them in one file you can still save the 2 heatmaps (or other plots) as 2 files then use tools such as imagemagick, gimp, or inkscape to combine the 2 files into 1 with the graphs side by side.

Repeat plot command with minor changes in R

I made a plot in R and I want to repeat all the commands (like plot(), legend() or line()) that were carried out for this plot, with some minor changes. For example I want to set the axes to logarithmic scale and change the title of the plot.
In gnuplot I would use the replot command.
plot ...
set title "The same plot with logarithmic axes"
set logscale
replot
Is something like this possible in R. The only thing that comes to my mind of doing this (besides changing the values manually and re-run the lines of codes) would be setting up a function, that asks for all parameters that might be changed by the user.
Thanks for your help,
Sven
R uses a pen and paper graphics model - once the plot has been drawn on the device that is it. If you want to change some aspect of the plot, you need to replay the graphics function calls that produce the plot with the changes made to the code.
Depending on what you are really doing there are two options:
If this is just for you, write code in a text editor / IDE that knows R and can send chunks of code at a time to R. That way the code to produce the figure is recorded in a separate script which you can paste into/send to R making the changes you need each time to the script.
If you are going to be doing this often, then write yourself a wrapper plotting function that encapsulates the plot code you want but allows you to pass in arguments to alter the aspects you want.
Lattice and ggplot2 are a little different as they are based on grid graphics and create objects that when printed produce a plot on the device. One can manipulate that object to alter what is drawn, and with grid one can push and pop things on to / off a viewport.

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