I have a very complex plot, containing about 56,000 data points. It doesn't look right if I downsample it, so I really need to keep all of them. I would additionally like to add LaTeX captions to the figure. (The expression syntax, IMO, does not produce satisfactory rendering.)
After doing some digging around, TikZ seemed like the way to do it. But I found that it ran out of memory trying to plot the figure. I followed all of the advice I could find for TikZ memory management: this amounted to (1) using externalize and (2) increasing the main_memory for LaTeX to the maximum value (~12M). (I am using MacTeX 2014.) Neither of these solutions seemed to work.
At this point, having looked over SO and some other message boards, I am aware of only two options:
Switch to an alternate TeX interpreter, such as LuaTeX, which will allow me to use more memory, or
Use the native R plot, and then manually superimpose the desired labels onto the figures.
I consider (1) to be an acceptable solution, but the fact that I would need an alternate product makes me wonder if I am missing something. I wonder if there is a way to render complex native R plots, which happen to have TeX-style labels in them.
Related
I use Gnuplot for most of my plots and save the plots as a png. But the resolution of the plots are not so good to put in research papers. So, I need help regarding the following two things:
How to prepare publication-quality plots (eps) in Gnuplot?
How to use calligraphic fonts in the plot, like those written using \mathcal{} in latex?
I searched on the internet regarding these two things, but could not get any ideas.
Thanks in advance.
Since you are stating LaTeX code in your question, I suppose that a solution including LaTeX is suitable for you. I am using gnuplot for producing publication-quality plots (and even TOC-figures!) too, and for me the most convenient method is to use the cairolatex standalone terminal, use LaTeX syntax (e.g. \mathcal{}) in the labels, plot titles and so on, and to compile the figures with pdfLaTeX. Often enough, journals accept figures not only in .eps, but also in .pdf format. If a journal was to refuse .pdf, I would simply convert the figure in the end (i.e. right before submission) to .eps, .png or whatever.
One interesting feature of RStudio is it allows to save multiple plots generated from a script. This however opens up the problem of how to edit multiple plots. My issue at the moment is adding lines to histograms using the abline() function. This function was designed however to work with the last plot generated by the environment. One way of course would be ad the lines as soon as the plot is generated, however I have to calculate the coordinates at the end of the algorithm, by then I have transformed the data and generated multiple plots from it. So I was wondering if there isn't a way to tell R to search for a given plot and add the line to it. I read abline() documentation but found nothing regarding it. One can always save the data necessary to generate the plot and generate it at the end of the script, but I was wondering if there isn't a less consuming memory method.
One way to get around this issue is:
1.Save your graphics as variables, for ex: hist_1=hist(x, plot=FALSE)
2.Write any code u like, for ex: very complicated code give y as a number for output
3.plot(hist_1)
4.abline(hist_1, v=y)
gives a general idea of how to edit multiple plots without having to save multiple copies of datasets and without overloading Rstudio interface. Works well with the R ubuntu terminal too.
Suppose that I have a PostScript file containing a plot which was generated using gnuplot. However, I do not have the source data, nor do I have the gnuplot commands that were used to generate the plot.
Do you know of any way to somehow extract data from a graphic representation (i.e., a PostScript file)? Such code would have to literally "read off of the graph" (in particular, I have a smoothed line/scatter XY plot) from the pixel representation, and I know that the results would be approximate at best (but this would still be very highly desirable).
Do you have any experience with this? Thank you for your time!
PostScript is nothing but a programming language to describe pages. A PostScript file is a plain text file containing a program that descbrise a page which gets interpreted by a printer or viewer.
As such it is amendable to programmatic manipulation, albeit in a low-level way.
I would approach this task in the following way.
Learn PostScript. (The reference manual will come in handy.)
Study the output from gnuplot. How does the gnuplot outputs the graph? Is this systematic? etcetera.
Parse and extract the needed information.
g3data, available here, looks like a possibility. It runs on Linux.
Recently a few neat uses of ggplot2 have come up, and either partial or full solutions have been posted:
ggheat
Curly braces
position_dynamic
ggheat is notable because it rather breaks the ggplot metaphor by just plotting rather than returning an object.
The curly brace solutions are notable because none really fits in the ggplot2 high-level concept (e.g. you should be specifying a range of points you want to breaks, and then somewhere else be able to specify the geom of how you want that range displayed--brace, box, purple cow, etc.).
The ggplot2 book (which I will order soon and have read the 2 online chapters) seems to be about using the grammar and functions rather than writing new ones or extensively extending existing ones.
I would like to learn to add a specific feature or develop a new geom, and do it properly. ggplot2 may not be intended as a general graphics package in the same way that grid or base graphics are, but there are a great many graphs which are only a step or two extension from an existing ggplot2 geom. When these situations come up, I can typically put together enough objects to do something once, but what if I need the same plot a few dozen times? What if other people like it and want to use it--now they have to kludge through the same process each time they want that graph. It seems to me that the proper solution is to add in a stat_heatplot and geom_heatplot, or to add a geom_Tuftebox for Tufte box plots, etc. Yet I've never seen an example of actually extending ggplot2; just examples of how to use it.
What resources exist to dig deeper into ggplot2 and start extending it? I'm particularly interested in a high-level way to specify a range on an axis as described above, but general knowledge about what makes ggplot2 tick is welcome as well.
Absent a coherent guide (which rarely exists for sufficiently advanced tinkering and therefore may not exist here), how would one go about learning about the internals? Inspecting source is obviously one way, but what functions to start with, etc.
ggplot2 is gradually becoming more and more extensible. The development version, https://github.com/hadley/ggplot2/tree/develop, uses roxygen2 (instead of two separate homegrown systems), and has begun the switch from proto to simpler S3 classes (currently complete for coords and scales). These two changes should hopefully make the source code easier to understand, and hence easier for others to extend (backup by the fact that pull request for ggplot2 are increasing).
Another big improvement that will be included in the next version is Kohske Takahashi's improvements to the guide system (https://github.com/kohske/ggplot2/tree/feature/new-guides-with-gtable). As well as improving the default guides (e.g. with elegant continuous colour bars), his changes also make it easier to override the defaults with your own custom legends and axes. This would make it possible to draw the curly braces in the axes, where they probably belong.
The next big round of changes (which I probably won't be able to tackle until summer 2012) will include a rewrite of geoms, stats and position adjustments, along the lines of the sketch in the layers package (https://github.com/hadley/layers). This should make geoms, stats and position adjustments much easier to write, and will hopefully foster more community contributions, such as a geom_tufteboxplot.
I am not certain that I agree with your analysis. I'll explain why, and will then point you to some resources for writing your own geoms.
ggheat
As far as I can tell, ggheat returns an object of class ggplot. Thus it is a convenient wrapper around ggplot, customised for a specific use case. Although qplot is far more generic, it does in principle the same thing: It is a wrapper around ggplot that makes some informed guesses about the data and chooses sensible defaults. Hadley calls this plot functions and it is described briefly on page 181 of the ggplot2 book.
curly braces
The curly brace solution does exactly what the ggplot philosophy says, i.e. separate data from presentation. In this case, the data is generated by a little custom function and is stored in a data.frame. It is then displayed using a geom that makes sense, i.e. geom_line.
quo vadis?
You have noted (in the r chat room) that you would prefer to have a more generic approach to plotting the curly braces. Something along the following lines (and I paraphrase and extend at the same time):
Supply data in the form of a bounding box coordinates (i.e. x0, x1, y0 and y1)
Specify a "statistic", such as brace, box or whatever
Specify a geom, such as geom_custom_shape
This sounds like a nice generalisation and extension of the ideas behind the curly brace solution, and would clearly require writing a new geom. There is an official ggplot wiki, where you can find instructions for creating a new geom.
Why do you want to extend it? What is the motivation? As I see it ggplot2 is meant to be a high-level graphics package designed to produce nice figures from a particular data set. And do things right and make other things easy: like scales, legends etc. ggplot2 is not meant to be a general-purpose graphics tool-kit. Like lattice it has a particular paradigm in mind and you use it for that purpose.
grid is the underlying graphical toolkit you want to use to do general purpose, customised plotting. And IIRC, it is relatively easy to add grid grobs to lattice or ggplot2 plots/objects, for this sort of arbitrary notation/annotation etc.
What doesn't make too much sense is extending ggplot2 or lattice along the lines you are thinking. I don't see why the ggplot2 can't do heatplots as it is? Or am I missing something here?
What would be very useful would be if the data processing guts of ggplot2 or lattice were available for others to write actual plotting code on top of. Hadley has mentioned this somewhere before.
ggplot2, in particular, and lattice are quite difficult codes to get into to read/understand. ggplot2 uses the proto package for a version of OOP, which means you need to understand what that is doing as well as ggplot2 semantics. lattice is similar as there is a lot of computing on the language done there that, if you are not familiar with that sort of R programming, can by quite intimidating, daunting and impenetrable!
For grid, I suggest you look at Paul Murrell's R Graphics book, a second edition of which is with the publisher: http://www.stat.auckland.ac.nz/~paul/RG2e/
Edit: The point I was intending to get across was that the interfaces provided by packages like ggplot2 and lattice are necessarily high-level. Extending them is fine as long as they stick to the paradigm/philosophy in use. Heatplots can already be made by using existing geoms; part of the philosophy of the ggplot system is to separate the data from the display/presentation, and to use geoms in interesting ways to produce the desired display.
Wrapping base ggplot + geom calls into a more user friendly function is OK as long as i) it works like ggplot already does and returns an object, and ii) it doesn't have an interface that is too different from the way ggplot works. Developers are free to write whatever code they want, it just isn't helpful to the wider community to provide wrappers that move too far away from the original's workings. That leads to confusion on the part of the user and doesn't foster learning of ggplot2 itself.
The dynamic positioning idea is interesting; you could include these ideas in all plotting packages. You could bolt this into a geom, or alternatively as an external function that modified the input coordinates to produce a new data object that could be used by the relevant geom. That same function could be used for other plotting packages - it wouldn't need to be ggplot-specific.
I am trying to render 739455 data point on a graph using R, but on the x-axis I can not view all those numbers, is there a way I can do that?
I am new to R.
Thank you
As others suggested, try hist, hexbin, plot(density(node)), as these are standard methods for dealing with more points than pixels. (I like to set hist with the parameter breaks = "FD" - it tends to have better breakpoints than the default setting.)
Where you may find some joy is in using the iplots package, an interactive plotting package. The corresponding commands include ihist, iplot, and more. As you have a Mac, the more recent Acinonyx package may be even more fun. You can zoom in and out quite easily. I recommend starting with the iplots package as it has more documentation and a nice site.
If you have a data frame with several variables, not just node, then being able to link the different plots such that brushing points in one plot highlights them in another will make the whole process more stimulating and efficient.
That's not to say that you should ignore hexbin and the other ideas - those are still very useful. Be sure to check out the options for hexbin, e.g. ?hexbin.