Basically I have lists with 2 matrices (u and v) containing the windspeed in longitudal and latitudal direction (and vectors x and y containing the coordinates). I would like to make a map with arrows pointing in the resulting direction, of which the size is proportional to the wind speed. This question was asked before: http://www.mail-archive.com/r-help#r-project.org/msg18875.html.
Uforunately, the link given in the answer is broken. I tried using the quiver function, but I don't get it working.
Here is how my data looks like:
x=seq(10,15,by=0.25)
y=seq(40,50,by=0.25)
u=matrix(runif(length(x)*length(y),-2,3),nrow=length(y),ncol=length(y))
v=matrix(runif(length(x)*length(y),-2,3),nrow=length(y),ncol=length(y))
wind=list(u,v)
For the quiver function:
library(pracma)
quiver(x=x, y=y, u=wind[[1]], v=wind[[2]])
Which gives twice:
Error: invalid graphics state
I assume that u and v are wrong and need to be coordinates as well, but I honestly don't understand the explanation given in the package discription (u, v : x,y-coordinates of start points).
I saw that more info is available for quiver in matlab or python, but I never worked with that, so any advice about doing this in R would be greatly appreciated.
x=seq(10,15,by=0.25)
y=seq(40,50,by=0.25)
u=matrix(runif(length(x)*length(y),-2,3),nrow=length(x),ncol=length(y))
v=matrix(runif(length(x)*length(y),-2,3),nrow=length(x),ncol=length(y))
#note that I corrected these
#melt for plotting
library(reshape2)
u <- melt(u,value.name = "u")
v <- melt(v,value.name = "v")
wind <- merge(u, v)
wind$x <- x[wind[,1]]
wind$y <- y[wind[,2]]
#plot
library(ggplot2)
library(grid)
scaler <- 1
p <- ggplot(wind, aes(x=x, y=y, xend=x+u*scaler, yend=y+v*scaler)) + geom_segment(arrow=arrow())
print(p)
Related
I need to create something like a spider chart in R without using any libraries. That’s my code for now. It creates a figure with points number equal to the length of vector ‘a’. However, I’d like each point to be at the distance from the coordinates center equal to a respective number in a vector, for example one point at a distance 1, another at 2, so on. Is it possible to do so?
a <- 1:6
angle <- seq(0, 2*pi, (2*pi)/length(a))
x <- cos(angle)
y <- sin(angle)
plot(x, y,
type = "l")
See ?stars:
a <- 1:6
stars(matrix(a, nrow=1), scale=FALSE)
For future reference, using R's built-in help search would have found this with ??spider
I'm trying to make a 3D scatterplot with boudaries or zones based on combinations of 3 variables that return certain values. The variables each range between 0:1, and combine to make an index that ranges from -1:1 as follows:
f(x,y,z) = (x*y)-z
I'd like to create a visual representation that will highlight all combinations of variables that return a certain index value. As an example, I can easily show those variables where index > 0 using scatterplot3d (rgl would also work):
# Create imaginary dataset of 50 observations for each variable
x<-runif(50,0,1)
y<-runif(50,0,1)
z<-runif(50,0,1)
# Create subset where f(x,y,z) > 0
x1<-y1<-z1<-1
for (i in 1:length(x)){ if ((x[i]*y[i])-z[i] > 0) {
x1<-rbind(x1, x[i])
y1<-rbind(y1, y[i])
z1<-rbind(z1, z[i])}
}
s3d<-scatterplot3d(x,y,z) # Plot entire dataset
s3d$points3d(x1,y1,z1,pch=19, col="red") # Highlight subset where f(x,y,z) > 0
This gives me the following graph:
It seems fairly intuitive that there should be an easy way to plot either the surface (extending from top/right/back to bottom/left/front) separating the subset from the rest of the data, or else a volume/3D area within which these plots lie. E.g. my first instinct was to use something like surface3d, persp3d or planes3d. However, all attempts so far have only yielded error messages. Most solutions seem to use some form of z<-lm(y~x) but I obviously need something like q<-func((x*y)-z) for all values of x, y and z that yield q > 0.
I know I could calculate extreme points and use them as vertices for a 3D polygon, but that seems too "manual". It feels like I'm overlooking something fairly simple and obvious. I've looked at many similar questions on Stack but can't seem to find one that fits my particular problem. If I've missed any and this question has been answered already, please do point me in the right direction!
Here is a suggestion for an interactive 3D plot that is based on an example from the "R Graphics Cookbook" by Winston Chang.
set.seed(4321)
library(rgl)
interleave <- function(v1,v2) as.vector(rbind(v1,v2))
x <- runif(50)
y <- runif(50)
z <- runif(50)
plot3d(x, y, z, type="s", size=0.6, col=(2+(x*y<z)))
x0 <- y0 <- seq(0, 1, 0.1)
surface3d(x0, y0, outer(x0, y0), alpha=0.4) #plot the surface f(x,y)=x*y
x1 <- x[x * y > z] #select subset that is below the separating surface
y1 <- y[x * y > z]
z1 <- z[x * y > z]
segments3d(interleave(x1, x1), #highlight the distance of the points below the surface
interleave(y1, y1),
interleave(x1 * y1, z1), col="red", alpha=0.4)
If you don't like the red lines and only want the surface and the colored points, this will be sufficient:
plot3d(x,y,z,type="s",size=0.6,col=(2+(x*y<z)))
x0 <- y0 <- seq(0,1,0.1)
surface3d(x0,y0,outer(x0,y0),alpha=0.4)
Does this representation provide the information that you wanted to highlight?
The first thought was to see if the existing functions within scatterplot3d could handle the problem but I think not:
my.lm <- lm(z ~ I(x) * I(y)+0)
s3d$plane3d(my.lm, lty.box = "solid", col="red")
pkg:scatterplot3d doesn't really have a surface3d function so you will need to choose a package that provides that capability; say 'rgl', 'lattice', or 'plot3d'. Any of them should provide the needed facilities.
I am trying to create a figure like the one depicted in the third column of the following image:
Link for the image in case of backup.
Basically I have x and y positions of 200 particles and I have the MSD data for these 200 positions. I'd like MSD to be the value that should determine a color map for the particles in coordinates (x,y). So MSD should be like the height, or the z position corresponding to each particle in (x,y).
I am surprised at my incompetence, because I have been trying to solve this problem for the last couple of days but none of the Google searches gave me any result. The closest thing that I have found is the concept of "self-organizing map" in Matlab and R, but I do not know how to use R and Matlab's toolbox for SOM was utterly useful for my needs.
I tried the following code in Matlab and get the attached plot as a result:
clear all; close all; clc;
x = (dlmread('xdata.dat'))'; % x is 1x200 array
y = (dlmread('ydata.dat'))'; % y is 1x200 array
msd = (dlmread('msd_field.txt'))'; % msd is 1x200 array
[X,Y] = meshgrid(x,y);
Z = meshgrid(msd);
z = [X; Y; Z];
surf(z)
But I think this plot is not useful at all. What I want is a 2D scatter plot of (x,y) depicting particle positions and on top of that color code this scatter plot with the values stored in msd like the plot I showed in the beginning. How can I create this through Matlab, or any other visualization tool? Thank you in advance.
It is not clear whay you want to have. Here a scatter plot using ggplot2.
## some reproducible data
set.seed(1)
dat <- data.frame(
x = round(runif(200,-30,30),2),
y = round(runif(200,-2,30),2),
msd = sample(c(0,2,3),200,rep=T))
## scatter plot where the size/color of points depends in msd
library(ggplot2)
ggplot(dat) +
geom_point(aes(x,y,size=msd,color=msd)) +
theme_bw()
I am using R to visualize some data. I am found RGL to be a great library for plotting points.
points3d(x,y,z)
where x = c(x1,x2, ...), y = c(y1,y2,...), z = c(z1,z2, ...) and x,y,z have the same length, is a great function for plotting large sets of data.
Now, I would like to plot ellipses, mixed in with the data. I have a characterization of ellipses by a center point C, a vector describing the major axis U, and a vector describing the minor axis V. I obtain points P on the boundary of the ellipse by
P = U*cos(t) + V*sin(t) (t ranges between 0 and 2*pi)
obtaining vectors, xt, yt, and zt. Then I can plot the ellipse with
polygon3d(xt,yt,zt)
It works fine, but I'm guessing everyone reading is cringing, and will tell me that this is a bad way to do this. Indeed it takes a couple seconds to render each ellipse this way.
I don't think the ellipse3d function from the RGL package works here; at the very least, I am not working a matrix of covariances, nor do I understand how to get the ellipse I want from this function. Also, it returns an ellipsoid, not an ellipse.
****** EDIT ************
For a concrete example that takes awhile:
library(rgl)
open3d()
td <- c(0:359)
t <- td*pi/180
plotEllipseFromVector <- function(c,u,v){
xt <- c[1] + u[1]*cos(t) + v[1]*sin(t)
yt <- c[2] + u[2]*cos(t) + v[2]*sin(t)
zt <- c[3] + u[3]*cos(t) + v[3]*sin(t)
polygon3d(xt,yt,zt)
}
Input center point, major, and minor axis you want. It takes just over 2 seconds for me.
On the other hand, if I change t to be 0,20,40,... 340, then it works quite fast.
I'm wondering if it is possible to caclulate the area within a contour in R.
For example, the area of the contour that results from:
sw<-loess(m~l+d)
mypredict<-predict(sw, fitdata) # Where fitdata is a data.frame of an x and y matrix
contour(x=seq(from=-2, to=2, length=30), y=seq(from=0, to=5, length=30), z=mypredict)
Sorry, I know this code might be convoluted. If it's too tough to read. Any example where you can show me how to calculate the area of a simply generated contour would be helpful.
Thanks for any help.
I'm going to assume you are working with an object returned by contourLines. (An unnamed list with x and y components at each level.) I was expecting to find this in an easy to access location but instead found a pdf file that provided an algorithm which I vaguely remember seeing http://finzi.psych.upenn.edu/R/library/PBSmapping/doc/PBSmapping-UG.pdf (See pdf page 19, labeled "-11-") (Added note: The Wikipedia article on "polygon" cites this discussion of the Surveyors' Formula: http://www.maa.org/pubs/Calc_articles/ma063.pdf , which justifies my use of abs().)
Building an example:
x <- 10*1:nrow(volcano)
y <- 10*1:ncol(volcano)
contour(x, y, volcano);
clines <- contourLines(x, y, volcano)
x <- clines[[9]][["x"]]
y <- clines[[9]][["y"]]
level <- clines[[9]][["level"]]
level
#[1] 130
The area at level == 130 (chosen because there are not two 130 levels and it doesn't meet any of the plot boundaries) is then:
A = 0.5* abs( sum( x[1:(length(x)-1)]*y[2:length(x)] - y[1:(length(x)-1)]*x[2:length(x)] ) )
A
#[1] 233542.1
Thanks to #DWin for reproducible example, and to the authors of sos (my favourite R package!) and splancs ...
library(sos)
findFn("area polygon compute")
library(splancs)
with(clines[[9]],areapl(cbind(x,y)))
Gets the same answer as #DWin, which is comforting. (Presumably it's the same algorithm, but implemented within a Fortran routine in the splancs package ...)