How to plot a set of densities in 3D using R? - r

I need to plot, in 3D, a set of densities associated to a time series. More precisely, I would like to be able in R to build an image close to this example
This image is taken from [1]. The transparency plays an important role as let us see the trajectory of the "measures" in the x-y plane.
Any help will be greatly appreciated.
[1]: Juban and Kariniotakis, "Uncertainty Estimation of Wind Power Forecasts", presentation at EWEC 2008 - 01 April - Brussels, Belgium. (I can't post the link, google will help interested readers).

In 1996 I wrote a paper (published in JCGS) with a figure very similar to that but without the transparency. See http://robjhyndman.com/papers/estimating-and-visualizing-conditional-densities/ for the details. The plotting function is implemented in the R package hdrcde available on CRAN. The package contains some examples in the help files. You should be able to adapt my code to add the transparency.

This is how far I got thanks to Rob's hint. I used persp() to create an empty plot and added polygons and lines to it:
However, it is not as pretty as the original one... :(

Related

How to turn a spatial plot in R into an ArcGIS layer

So I hope I can clearly communicate my issue. Since I'm fairly new to R and ArcGIS I may miss some obvious things.
Basically, I'm using R to process spatial data to make a canopy height model and detect tree tops. That parts fine. I then make a watershed segment plot using forestTools package, and visually it looks great, but how do I export that as a file I can add into ArcGIS?
I'll copy some of the code that goes into what I'm discussing.
Basically, I just followed this guide's supplemental material to get the tree detection https://www.degruyter.com/document/doi/10.1515/geo-2020-0290/html?lang=en.
With that done, I then used the forestTools package to creat an interesting segmentation polygon grid on the map. https://www.rdocumentation.org/packages/ForestTools/versions/0.2.5/topics/mcws
This is quickly the plotting code to get visualized what I want.
[1]: https://i.stack.imgur.com/7Y0EF.png
This is what the map looks like with those plotted.
[2]: https://i.stack.imgur.com/Kdfl6.png
The layer that I want to bring solo to ArcGIS is that last plot the mcws one. I'll show a pic of that as well here.
[3]: https://i.stack.imgur.com/3PKfk.png
Is there a way that I can export that as a .shp or .tif?
Any help would be wonderful and much appreciated!
Nvmd I figured it out.
What you have to do is use the Raster package to export a shapefile.
raster::shapefile(site2_ttops,"Products/site2plot_ttops.shp")

How to draw the following 3D Matlab plot/graph?

I am working on a data analytics project as part of my result.
I have cleaned and sorted my data as I wanted.
I want to show the data as shown in the figure below.
I have multiple files.
Each file represents a single day and the date in each file is hour and activity (as shown in the picture).
I am working in Matlab, I know how to do the 3D graphs. I also know about the ribbon function in Matlab.
But I can't exactly figure out how to draw the following graph. Any assistance will be highly appreciated. Thank you.
If you still haven't solved the issues, you can use ribbon plot in matlab. Please find the details in the following link.
https://www.mathworks.com/help/matlab/ref/ribbon.html
For example, if X is the vector containing all the data, then you can simply write.
ribbon(X(:,1));

Plot 3D graphs in R-studio

Sorry for the question, but I have a variable that I would like to plot like this:
I am a newby on R, so I am having some difficulties. I appreciate any kind of help.
Thanks!
Since you're looking to plot what appears to be a 3d surface, I'd suggest starting with the persp function, from the graphics package. This blog post (http://www.r-bloggers.com/3d-plots-in-r/) gives a good treatment of several options for 3D plotting:
the generic function persp() in the base graphics package draws perspective plots of a surface over the x–y plane. Typing demo(persp) at the console will give you an idea of what this function can do.
And running demo(persp) gives you a number of examples, including this one:
There are also some more suggestions for going further:
The plot3D package from Karline Soetaert builds on on persp()to provide functions for both 2D and 3D plotting. [...] Load the package and type the following commands at the console: example(persp3D), example(surf3D) and example(scatter3D) to see examples of 3D surface and scatter plots.
As a side note, #rawr's comment is spot on - I found all this in less than a minute, using two google searches - one of which was the title of your post. I'm putting this answer up anyway, since StackOverflow posts frequently become the top google result for many topics. But the best advice I can give you going forward is that R is one of the most aggressively well-documented languages out there, both in terms of formal and informal documentation, and you can find a lot just by googling what you want to do.

Raster map vs alternative

I recently found this web page Crime in Downtown Houston that I'm interested in reproducing. This is my first learning experience with mapping in R and thus lack the vocabulary and understanding necessary to make appropriate decisions.
At the end of the page David Kahle states:
One last point might be helpful. In making these kinds of plots, one
might tempted to use the map raster file itself as a background. This
method can be used to make map plots much more quickly than the
methods described above. However, the method has one very significant
disadvantage which, if not handled properly, can destroy the entire
purpose of using the map.
In very plain English what is the difference between the raster file
approach and his approach?
Does the RgoogleMaps package have the ability to produce these types
of high quality maps as seen on the page I referenced above that
calls a google map into R?
I ask not because I lack information but the opposite. There's too much and I want to make a good decision(s) about the approach to pursue so I'm not wasting my time on outdated or inefficient techniques.
Feel free to pass along any readings you think would benefit me.
Thank you in advance for your direction.
Basically, you had two options at the time this plot was made:
draw the map as a layer using geom_tile, where each pixel of the image is mapped onto the x,y axes (slow but accurate)
add a background image to the plot, as a purely "cosmetic" annotation. This method is faster, because you can use grid.raster which draws images more efficiently, but the image is not constrained by the axes of the plotting region. In other words, you have to manually adjust the x and y axes limits to make sure that the image corresponds to the actual positions on the plot.
Now, I would suggest you look at the new annotation_raster in ggplot2 v. 0.9.0. It should have the advantage of speed and leaner output files, and still conform to the data space of the plot. I believe that this function, as well as geom_raster and annotation_map did not exist when David made those plots.

Combining 3D/2D plots

I'm trying to make a visualization that looks like this http://www.gradient-da.com/img/temperature%20surface%20plot%20470x406.JPG http://www.gradient-da.com/img/temperature%20surface%20plot%20470x406.JPG.
The idea is to have a 3D surface plot overlapping a 2d representation of a surface.
I can build arbitrary surfaces/polygon shapes (as in http://addictedtor.free.fr/graphiques/graphcode.php?graph=135 ) and I can make the respective 2D plot. What I don't seem to be able to figure out is the way to put them together in a nice way (like the one shown in the jpg above).
I've tried googling for the answer, but I wasn't able to find anything similar done in R.
Any help would be greatly appreciated!
EDIT: The 2D portion is not a projection of the 2D one. I chose this specific picture to illustrate this. For example
Here the 2D portion is the image of the circuit and on the 3D portion is the temperature).
In 2D you can have the map of a city and in 3D the traffic
etc...
Best,
Bruno
I will give a theoretical Idea,
In the same 3D plot, select a plane perpendicular to the 3D surface (just below the 3D-surface) and project all the values to it. Instead of 2D & 3D plot, you will use only a 3D plot, which also plots your surface.
HTH
It looks like the 2D plot is a layout of a microelectronic circuit, albeit with some detail skipped, and the 3D plot is perhaps a thermal plot of the same circuit.
I don't know enough about R's capabilities, but I imagine it would be easier to generate the two plots separately with R from the same dataset which represents the layout information (but with and without the thermal data) and then combine them with a graphics manipulation program.
No help in R, but you can do something similar in ROOT as seen in this image:
taken from the THistPainter class documentation.
The code is open source and could be examined if wanted for reimplementation.
Maybe you should try to make an opengl texture out of your 2d picture and map it on a 3d polygon to be included in your scenegraph?
Don't really understand if you wish to do it with R specifically, so maybe diving in opengl is a too low level for you. In case you'd be ready for that, you may reuse a simple java library that simplify plotting 3d surface: http://code.google.com/p/jzy3d
Hope that helps,
Martin
What you're looking for is called a texture map -- and if it's not provided in the R graphics package, you may be able to do it "by hand". The suggestion below may not be fast or convenient (or even helpful, as I'm not really familiar with R), but it may actually work...
Since you know you can draw a 3D surface plot with specified colors, you can try drawing a flat 3D surface using the colors of your image.
If R also lacks methods for extracting its data from image formats, there is an image format called PPM (standing for Portable PixMap), one variant of which is basically space-separated decimal numbers. After converting your image to this format (using Photoshop, say, or some dedicated image conversion program), it should be relatively easy to input into R.

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