I am looking for a way to draw a 3d line plot. Preferably I would like to use the datavisualization framework, but it does not seem to provide this out of the box.
I experimented a little bit and ended up using 3D surface plots (Surface3D) displaying the lines as surfaces (i.e. ribbons) like this:
While this works and looks okay in above picture the thickness of the line depends on the perspective. Rotating the plot always allows to find the angle where the line disappears since it has not thickness:
Is there a type of plot that would be better suited for this? I tested the bars which don't perform well for lots of samples and don't look nice in my application. I also tested scatterplots which are not suitable either.
If there isn't: Where would I start to implement this myself on top of the existing classes in the datavisualization framework? I am thinking about adding another surface "ribbon" in z direction, however that seems a little hackish.
I used the technique described as hackish above. While I am not too happy about the approach the overall look is quite okay:
So basically each data line consists of three QSurfaceDataRows that together form two 90° ribbons as can be seen here:
Related
I know it is possible to create such double pie charts in excel like this:
http://chandoo.org/wp/2009/12/02/group-small-slices-in-pie-charts/
but can SPSS or R do this also?
In relation to R:
The answer to the title question is "yes" ... see ?pie
As for the second question, the one in the body - it would be possible but would involve some coding. You'd have to draw two pie charts side by side (which could be managed with two calls to pie) and use segments or arrows (and text if necessary) to do the additional components of the plot.
Here's a rough example:
That required the fig argument of par to get them side-by-side.
(That example required a little fiddling to get right, but it would be possible to write a function to automate the details.)
The main issue I can see would be 'why on earth would you do it?' -- pie charts are a poor way of conveying information of this form. There are alternatives that result in much better ability to distinguish values, and less bias (such as what you get when comparing nearly horizontal vs nearly vertical slices).
I'm trying to draw some appealing curved arrows on top of a map. The best looking arrows that I can find are network.arrow() from the network package. However, when I make them curved things get weird. Compare the following:
library(maps)
library(network)
map("usa")
network.arrow(-110,37,-85,40,length=5,width=1,angle=20)
map("usa")
network.arrow(-110,37,-85,40,length=5,width=1,angle=20,curve=2)
I find it difficult to believe that network.arrow() would integrate a curve option that inherently makes the arrowheads look bizarre, and yet I cant get it perform properly. How can I fix this?
Note: I am aware of great circle, and no, I'm not trying to get flight paths drawn properly. The data I am trying to visualize is on a much smaller scale than the example presented here.
I'm getting familiar with Graphviz and wonder if it's doable to generate a diagram/graph like the one below (not sure what you call it). If not, does anyone know what's a good open source framework that does it? (pref, C++, Java or Python).
According to Many Eyes, this is a bubble chart. They say:
It is especially useful for data sets with dozens to hundreds of values, or with values that differ by several orders of magnitude.
...
To see the exact value of a circle on the chart, move your mouse over it. If you are charting more than one dimension, use the menu to choose which dimension to show. If your data set has multiple numeric columns, you can choose which column to base the circle sizes on by using the menu at the bottom of the chart.
Thus, any presentation with a lot of bubbles in it (especially with many small bubbles) would have to be dynamic to respond to the mouse.
My usual practice with bubble charts is to show three or four variables (x, y and another variable through the size of the bubble, and perhaps another variable with the color or shading of the bubble). With animation, you can show development over time too - see GapMinder. FlowingData provides a good example with a tutorial on how to make static bubble charts in R.
In the example shown in the question, though, the bubbles appear to be located somewhat to have similar companies close together. Even then, the exact design criteria are unclear to me. For example, I'd have expected Volkswagen to be closer to General Motors than Pfizer is (if some measure of company similarity is used to place the bubbles), but that isn't so in this diagram.
You could use Graphviz to produce a static version of a bubble chart, but there would be quite a lot of work involved to do so. You would have to preprocess the data to calculate a similarity matrix, obtain edge weights from that matrix, assign colours and sizes to each bubble and then have the preprocessing script write the Graphviz file with all edges hidden and run the Graphviz file through neato to draw it.
I am in my way of finishing the graphs for a paper and decided (after a discussion on stats.stackoverflow), in order to transmit as much information as possible, to create the following graph that present both in the foreground the means and in the background the raw data:
However, one problem remains and that is overplotting. For example, the marked point looks like it reflects one data point, but in fact 5 data points exists with the same value at that place.
Therefore, I would like to know if there is a way to deal with overplotting in base graph using points as the function.
It would be ideal if e.g., the respective points get darker, or thicker or,...
Manually doing it is not an option (too many graphs and points like this). Furthermore, ggplot2 is also not what I want to learn to deal with this single problem (one reason is that I tend to like dual-axes what is not supprted in ggplot2).
Update: I wrote a function which automatically creates the above graphs and avoids overplotting by adding vertical or horizontal jitter (or both): check it out!
This function is now available as raw.means.plot and raw.means.plot2 in the plotrix package (on CRAN).
Standard approach is to add some noise to the data before plotting. R has a function jitter() which does exactly that. You could use it to add the necessary noise to the coordinates in your plot. eg:
X <- rep(1:10,10)
Z <- as.factor(sample(letters[1:10],100,replace=T))
plot(jitter(as.numeric(Z),factor=0.2),X,xaxt="n")
axis(1,at=1:10,labels=levels(Z))
Besides jittering, another good approach is alpha blending which you can obtain (on the graphics devices supporing it) as the fourth color parameter. I provided an example for 'overplotting' of two histograms in this SO question.
One additional idea for the general problem of showing the number of points is using a rug plot (rug function), this places small tick marks along the margin that can show how many points contribute (still use jittering or alpha blending for ties). This allows the actual points to show their true rather than jittered values, but the rug can then indicate which parts of the plot have more values.
For the example plot direct jittering or alpha blending is probably best, but in some other cases the rug plot can be useful.
You may also use sunflowerplot, while it would be hard to implement it here. I would use alpha-blending, as Dirk suggested.
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.