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.
Related
I am attempting to plot several great circles between points on a world map. I am using sf to generate the great circles, largely following the method outlined here. I am able to generate the plot and lines all ok, except several of the lines wrap around behind the globe so to speak, and I end up with a horizontal line connecting the two points where the great circle paths wrap around - see "Correcting gcIntermediate" here for a visual explanation and a solution for gcIntermediate. Looking at the sf dataframe that has been created, my guess is that I need to split the items where absolute longitude difference is greater than 180 degress into two sf lines. I can kind of see a method that involves many lines of code brute forcing the sf dataframe to resolve this issue, but would very like to see if there are any other better ideas to solve this elegantly.
st_wrap_dateline to the rescue!
path.sf <- st_wrap_dateline(path.sf,
options=c("WRAPDATELINE=YES", "DATELINEOFFSET=180"))
Then plotting this with ggplot2 seems to plot all ok.
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:
I'm trying to plot the cluster obtained from fuzzy c-means clustering.
The plot should look like this.
code for the plot
plot(data$Longitude, data$Latitude, main="Fuzzy C-Means",col=data$Revised, pch=16, cex=.6,
xlab="Longitude",ylab="Latitude")
library(maps)
map("state", add=T)
However, when I tried to use clusplot the plot is displaying in opposite direction(both top and bottom and left and right) as below.
I wanna know if there's a way to reverse the plot to show in the order as the above picture.
Also, for the very dense area, it's hard to find the ellipse label. I wanna know if there's a way to show the label inside the ellipse instead of outside.
code for 2nd pic
library(cluster)
clusplot(cbind(Geocode$Longitude, Geocode$Latitude), cluster, color=TRUE,shade=TRUE,
labels=4, lines=0,col.p=cluster,
xlab="Longitude",ylab="Latitude",cex=1)
clusplot is a function that performs a lot of magic for you. In particular it projects the data set - which happens in a way you don't like, unfortunately. (Also note the scales - it centered and scaled the data, too)
clusplot.default: Creates a bivariate plot visualizing a partition (clustering) of the data. All observation are represented by points in the plot, using principal components or multidimensional scaling.
As far as I can tell, clusplot doesn't have map support, but you will want such a map I guess...
While maybe you can use the s.x.2d parameter to specify the exact projection (and this way disable automatic scaling), it probably is still difficult to add the map. Maybe look at the source of clusplot instead, and take only the parts you want?
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.
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.