I'm working off a CSV of over a million rows of data taken from a rectangular sample. The CSV contains 3 columns, x coordinate (in steps of .06 or .07), y coordinate (in steps of .16 or .17) and the reading at that point. I'm looking for someway to visualize the sample in R to create a pseudo-image of the sample based in the readings using a color gradient.
Searching online, this solution seemed promising, Creating a matrix by color in R, but I'm running into issues creating a matrix from my data
x = c(unique(CTLI$xcoord)) #getting all of the different x values
y = c(unique(CTLI$ycoord)) #getting all the y values
matVar = matrix(CTLI$CTLI, nrow = x, ncol = y)
And am getting the error
Warning message:
In matrix(CTLI$CTLI, nrow = x, ncol = y) :
data length exceeds size of matrix
I'm not committed to this solution though, so any other ideas would be much appreciated. Thank you!
I played with the answer you linked too, learning some from it and get there:
CTLI <- read.table(text="xcoord,ycoord,measure
12,15,30
16,20,25
19,35,38",header=TRUE,sep=",")
mCTLI <- melt(CTLI,id.vars=c("xcoord","ycoord"))
iCTLI <- ggplot(mCTLI,aes(x=xcoord,y=ycoord,fill=value)) + geom_raster()
iCTLI
You don't have to build a matrix before, melt create a data.frame suitable for ggplot.
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 am trying to create a 3D mesh of a specific building from points that I extracted from a lidar point cloud. I then created a matrix from the x, y and z values to feed into the as.mesh3d function from the rlg package and since its from a lidar survey, I have 27,000+ points for this one building. I run into an error when I try to create the mesh. I've copied in a sample of 20 points from the point cloud:
X <- c(1566328,1566328,1566328,1566328,1566328,1566327,1566327,1566327,
1566327,1566327,1566327,1566327,1566327,1566327,1566327,1566327,
1566326,1566326,1566326,1566326)
Y <- c(5180937,5180937,5180936,5180935,5180936,5180937,5180937,5180936,
5180936,5180935,5180935,5180935,5180936,5180936,5180937,5180938,
5180938,5180937,5180936,5180936)
Z <- c(19.92300028,19.98300046,19.93700046,19.88099962,19.93500046,19.99500046,
20.00400046,20.00600046,19.97199962,19.92499962,19.95400046,
19.99099991,20.01199991,19.97600020,19.95800008,19.93200008,
19.95300008,19.94800008,19.94300020,19.98399991)
#created a matrix
xyz <- matrix(c(X, Y, Z), byrow = TRUE, ncol = 3)
The problem arises when I try to create the mesh using as.mesh3d():
mesh <- as.mesh3d(xyz, y = NULL, Z = NULL, type = "triangle", col = "red")
This is what I get: Error in as.mesh3d.default(xyz, y = NULL, Z = NULL, type = "triangle", : Wrong number of vertices
The same error happens for the original dataset of 27000+ points despite all being of the same length.
I'm really not advanced in R and was hoping I could get some advice or solutions on how to get past this.
Thankyou
The as.mesh3d function assumes the points are already organized as triangles. Since you're giving it 20 points, that's not possible: it needs a multiple of 3 points.
There's a problem with your calculation of xyz: you say byrow = TRUE, but you're specifying values by column. Using
xyz <- cbind(X, Y, Z)
would work.
If I plot all of your points using text3d(xyz, text=1:20), it looks as though there are a lot of repeats.
There are several ways to triangulate those points, but they depend on assumptions about the surface. For example, if you know there is only one Z value for each (X, Y) pair, you could use as.mesh3d.deldir (see the help page) to triangulate. Here's the code and output for your sample:
dxyz <- deldir::deldir(X - mean(X), Y - mean(Y), z = Z)
# Warning message:
# In deldir::deldir(X - mean(X), Y - mean(Y), z = Z) :
# There were different z "weights" corresponding to
# duplicated points.
persp3d(dxyz, col = "red")
I had to subtract the means from X and Y because rounding errors caused it to look very bad without that: rgl does a lot of things in single precision, which only gives 7 or 8 decimal place accuracy.
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")
By using R version 3.4.2 and the library "geoR", I made kriging interpolations for different variables (bellow I give an example of my process). I also made a matrix with the coordinates for 305 trees with distinct marks (species, DBH, Height) that are within the same space for the interpolations, as seen in the image attached (https://imgur.com/SLQBnZH). I've been looking for ways to extract the nearest value from each variable for each tree and save the corresponding values in a data.frame or matrix, but haven't been successful, and I can't find specific answers to this.
One thing I've been looking at is trying to convert the Kriging result into a Raster (.tif) and proceed from there. But Kriging interpolations are made out of vector data, so is it even posible?
I'd be glad to receive any sort of help, thank you in advance!
P.S. I'm doing this so that I can latter use the data for spatial point patern analysis.
#Kriging####:
PG<-read.csv("PGF.csv", header=T, stringsAsFactors=FALSE)
library("geoR")
x<-(PG$x)
y<-(PG$y)
#Grid
loci<-expand.grid(x=seq(-5, 65, length=100), y=seq(-5, 85, length=100))
names(loci)<-c("x", "y")
mix<-cbind(rep(1,10000), loci$x, loci$y, loci$x*loci$y)
#Model
pH1.mod<-lm(pH1~y*x, data=PG, x=T)
pH1.kg<-cbind(pH1.mod$x[,3], pH1.mod$x[,2], pH1.mod$residuals)
#Transform to geographic data
pH1.geo<-as.geodata(pH1.kg)
#Variogram
pH1.vario<-variog(pH1.geo, max.dist=35)
pH1.vario.mod<-eyefit(pH1.vario)
#Cross validation
pH1.valcruz<-xvalid(pH1.geo, model=pH1.vario.mod)
#Kriging
pH1.krig<-krige.conv(pH1.geo, loc=loci, krige=krige.control(obj.model=pH1.vario.mod[[1]]))
#Predictive model
pH1a.yhat<-mix %*% pH1.mod$coefficients + pH1.krig$predict
#Exchange Kriging prediction values
pH1.krig$predict<-pH1.yhat
#Image
image(pH1.krig2)
contour(pH1.krig2, add=TRUE)
#Tree matrix####:
CoA<-read.csv("CoAr.csv", header=T)
#Data
xa<-(CoA$X)
ya<-(CoA$Y)
points(xa,ya, col=4)
TreeDF<-(cbind.data.frame(xa, ya, CoA$Species, CoA$DBH, CoA$Height, stringsAsFactors = TRUE))
m<-(cbind(xa, ya, 1:305))
as.matrix(m)
I tried to find the value of a point in space (trees [1:305]) through the minimum distance to a predicted value using the following code, (I suggest not running this since it takes too long):
for(i in 1:2){print(c(2:10000)[as.matrix(dist(rbind(m[i,], as.matrix(pH1.krig2$predict))))[i,2:10000]==min(as.matrix(dist(rbind(m[i,],as.matrix(pH1.krig2$predict))))[i,2:10000])])}
In the following link aldo_tapia's answer was the approach needed for this problem. Thank you to everyone! https://gis.stackexchange.com/questions/284698/how-to-extract-specific-values-with-point-coordinates-from-kriging-interpolation
The process is as follows:
Use extract() function from raster package:
library(raster)
r <- SpatialPointsDataFrame(loci, data.frame(predict = pH1.krig$predict))
gridded(r) <- T
r <- as(r,'RasterLayer')
pts <- SpatialPointsDataFrame(CoA[,c('X','Y')],CoA)
pH1.arb <-extract(r, pts)
to this I just added the values through cbind to the tree data frame since they are in order.
COA2<-cbind(CoA, pH1val=pH1.arb)
I will repeat the process for each variable.
I need to work with 3D data (spatial) very long tables with for coumns:
x, y, z, Value
There are too many data to be plotted with scatterplot3d or similar (rgl, lattice...)
I would like to reduce the number of data.
One idea could be to sample.
But I'd like to know how to reduce the data, getting new points that summarize the nearby points.
Is there any package to do it and work with this kind of data?
Something like creating a predefined 3D grid and averaging the points in each grid.
But I don't know whether it's better to choose the new points equidistants or just get their coordinates averaging the old ones locally. Or even weighting their final contribution with the distance to the new point.
Other issues:
The "optimal" grid could be tilted, but I don't know it beforehand.
I don't know if the grid should be extended a little bit beyond the data nor how much.
PD: I don't want to create surfaces nor wireframes nor adjust anything.
PD: I've checked spatial packages but as far as I see they are useful for data on a surface, such as the earth, but without height.
To reduce the size of the data set, have you thought about using a clustering methods such as kmeans or hierarchical clustering (hclust). These methods could reduce your data set down to a reasonable size. Be aware, if your data set is large enough these methods could still be too computational time consuming.
Seems like you might benefiit from fitting some sort of model to your data and then displaying the prediction on a resolution of your choice.
Here is an example of fitting with a GAM model:
library(sinkr) # https://github.com/marchtaylor/sinkr
library(mgcv)
library(rgl)
# make data ---------------------------------------------------------------
n <- 1000
x <- runif(n, min=-10, max=10)
y <- runif(n, min=-10, max=10)
z <- runif(n, min=-10, max=10)
value <- (-0.01*x^3 + -0.2*y^2 + -0.3*z^2) * rlnorm(n, 0, 0.1)
# fit model (GAM) ---------------------------------------------------------
fit <- gam(value ~ s(x) + s(y) + s(z))
plot.gam(fit, pages = 1)
This visualization is already helpful in understanding the 3d pattern of value, but you could also predict the values to a new grid. To visualize the prediction in 3d, the rgl package might be useful:
# predict to new grid -----------------------------------------------------
grd <- expand.grid(
x=seq(min(x), max(x),,10),
y=seq(min(y), max(y),,10),
z=seq(min(z), max(z),,10)
)
grd$value <- predict.gam(fit, newdata = grd)
# plot prediction with rgl ------------------------------------------------
# original data
plot3d(x, y, z, col=val2col(value, col=jetPal(100)))
rgl.snapshot("original.png")
# interpolated data
plot3d(grd$x, grd$y, grd$z, col=val2col(grd$value, col=jetPal(100)), alpha=0.5, size=5)
rgl.snapshot("points.png")
spheres3d(grd$x, grd$y, grd$z, col=val2col(grd$value, col=jetPal(100)), alpha=0.3, radius=1)
rgl.snapshot("spheres.png")
I've found the way to do it.
I'll post an example, just in case it's useful for others.
I write only two dimensions (and only working on the coordinates) to make it clear, but it can be generalized to higher dimensions and summarizing the functions at every coordinate).
set.seed(1)
xx <- runif(30,0,100); yy <- runif(30,0,100)
datos <- data.frame(xx,yy) #sample data
plot(xx,yy,pch=20) # 2D plot to visualize it.
n <- 4 # Same number of splits on every axis. Simple example.
rango <- function(ii){(max(ii)-min(ii))+0.000001}
renorm<- function(jj) {trunc(n*(jj-min(jj))/rango(jj))+1}
result <- aggregate(cbind(xx,yy)~renorm(xx) + renorm(yy),datos, mean)
points(result$xx,result$yy,pch=20, col="red")
abline(v=( min(xx) + (rango(xx)/n)*0:n) )
abline(h=( min(yy) + (rango(yy)/n)*0:n) )
Everything could be modified with na.rm=T
Maybe there are a simpler solutions with split, cut, dplyr, data.table, tapply...
I like this way more than fixing the new points coordinates at the center of every subregion because if you have only 1 point it keeps its original coordinates.
The +0.000000001 is to avoid the last point to move to a subregion further.
The full solution would have been:
aggregate(cbind(xx,yy,zz, Value)~renorm(xx)+renorm(yy)+renorm(zz),datos, mean)
And it could be further improved by weighting distances.