I was working with 3 vectors x , y , z , each of length N (Assume N to be some large natural number, say 20000). In order to visualize this, I was able to plot this easily using the following R code :
library("plot3D")
lines3D(x, y, z, type = "l")
Now, I was thinking if we can make a little 3D animation (i.e. a 3D GIF) from the vectors x , y , z. Is it possible in R ?
NOTE : I've previously done 2D GIFs in R, with the help of packages like ggplot2 , gganimate , magick etc. However, I'm curious whether the same thing can be done for 3D data. Thanks in advance.
It is very simple to create .gif with the animation package (note it requires ‘ImageMagick’ or ‘GraphicsMagick’ to run). After the installation, you could do something like this (I assume that you want to display your plot with different point of view):
library(plot3D)
library(animation)
x <- runif(1000)
y <- x*2+runif(1000)
z <- sample(1:10,length(x),replace = T)*x/y
ani.options(interval=0.5,nmax=35)
saveGIF(for(t in seq(0,360,10)){
lines3D(x, y, z, type = "l",theta=t)
}, movie.name = "animation.gif")
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 plot 3-dimensional vectors (x, y, z coordinates) onto a 3D coordinate system in R like in the picture below. Ideally, I would then like to construct 3d kernel density plots, also like in the image below.
Ideal result of vector plot and 3d kernel density plot
I have a matrix containing ~100 rows and one column for each coordinate (x, y , z). Initially, I tried arrow3D() from the plot3D package but I find the perspective to be sub-par, it's rather difficult to discern directions of the arrows from one perspective in the final plot. Next I tried the rgl package which gives me interactivity - great. Minimal working example:
library(rgl)
library(matlib)
data2 <- data.frame(replicate(6,rnorm(100))) #sample data set for minimum working example
colnames(data2) <- c("x_target", "y_target", "z_target", "x_start", "y_start", "z_start")
x1 <- data2$x_target - data2$x_start
y1 <- data2$y_target - data2$y_start
z1 <- data2$z_target - data2$z_start
vec <- (diag(6,3)) # coordinates for x, y and z axis
rownames(vec) <- c("X", "Y", "Z") # labels for x, y and z axis
z <- as.matrix((data.frame(x=x1, y=y1, z=z1)))
open3d()
vectors3d(vec, color=c(rep("black",3)), lwd=2, radius=1/25)
vectors3d(X=z, headlength=1/25)
(due to the random numbers generator the strange looking rods appear at different coordinates, not exactly like in the image i link to below)
The result of the code above is a version of the image link below. One set of coordinates produces a very strange looking more like rod object which is far longer then the coordinates would produce. If I plot the vectors individually, no such object is created. Anyone have any ideas why this happens? Also, if anyone has a tool (doesn't have to be R), that can create a 3D vector plot like in the first image, I'd be grateful. I find it to be very complicated in R, but I'm definitely a beginner.
Strange object to the right (long red rod that doesn't look like an arrow at all)
Thank you!
This is due to a bug in the matlib package, fixed in verson 0.9.2 of that package. I think you need to install it from Github instead of CRAN to get the bug fix:
devtools::install_github("friendly/matlib")
BTW, if you are using random numbers in a reproducible example, you can make it perfectly reproducible by something like
set.seed(123)
at the start (or some number other than 123). I saw reproducible problems with your example for set.seed(4).
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'm looking into plotting functions and I've run into persp and curve but I'm not able to follow them to plot a 2D function.
They are for surface plots, yes?
If I had a function like x^2 + y^2 [x,y] in [-3,3] how do I go about it?
Any links will be much appreciated and critique on existing packages (if multiple) ? gold.
Thanks.
To use persp, you need to supply values of x, values of y, and values of z for each combination of x and y. The easiest way to do this is to define x and y and then use outer to create a matrix that crosses x and y. You need to specify the way the two variables should be combined as the third argument to outer, in this case the function +:
x <- seq(-3,3,length.out=100)
y <- seq(-3,3,length.out=100)
z <- outer(x^2,y^2,`+`)
persp(x,y,z, col='blue')
You may also be interested in rotating the results. Here are some examples using the theta parameter:
par(mar=c(1,1,1,1))
layout(matrix(1:4, nrow=2))
s=lapply(c(0,30,60,90), function(t) persp(x,y,z, col='blue', theta=t))
EDIT: I understand from your comment you would like a 2D representation of this surface. The easiest way to get that in base R is with image of your z matrix:
image(z)
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 ...)