I have a problem when trying to make MCP from many points in R.
library(shapefiles)
# no problem when only three points...
dd <- data.frame(Id=c(1,1,1,1),X=c(3,5,8,3),Y=c(9,8,3,9))
ddTable <- data.frame(Id=c(1),Name=c("Item1"))
ddShapefile <- convert.to.shapefile(dd, ddTable, "Id", 5)
write.shapefile(ddShapefile, "/directory.../pgn_test", arcgis=T)
my.pgn <- readOGR("/directory...","pgn_test")
plot(my.pgn)
points(dd$X, dd$Y, cex = 0.7, pch = 1)
Code above works perfectly when only three points are given, however in my case there are many points...
# when some points are inside the polygon
dd <- data.frame(Id=c(rep(1, times = 6)),X=c(1,2,3,5,5,1),Y=c(1,5,3,5,1,1))
ddTable <- data.frame(Id=c(1),Name=c("Item1"))
ddShapefile <- convert.to.shapefile(dd, ddTable, "Id", 5)
write.shapefile(ddShapefile, "/directory.../pgn_test", arcgis=T)
my.pgn <- readOGR("/directory...","pgn_test")
plot(my.pgn)
points(dd$X, dd$Y, cex = 0.7, pch = 1)
Can anybody know how to solve this situation?
You could just use the base R function chull(), which "computes the subset of points which lie on the convex hull of the set of points specified":
dd <- data.frame(X = c(1,2,3,5,5,1), Y = c(1,5,3,5,1,1))
ii <- with(dd, chull(X,Y))
ii <- c(ii, ii[1])
plot(Y~X, data=dd)
lines(Y~X, data=dd[ii,])
Related
So I have this code that produces the exact surface
f = function(x, y){
z = ((x^2)+(3*y^2))*exp(-(x^2)-(y^2))
}
plot3d(f, col = colorRampPalette(c("blue", "white")),
xlab = "X", ylab = "Y", zlab = "Z",
xlim = c(-3, 3), ylim = c(-3, 3),
aspect = c(1, 1, 0.5))
Giving the following plot:
Now I have some code that does a random walk metropolis algorithm to reproduce the above image. I think it works as if I do another plot of these calculated values I get the next image with 500 points. Here is the code
open3d()
plot3d(x0, y0, f(x0, y0), type = "p")
Which gives the following plot:
I know it's hard looking at this still image but being able to rotate the sampling is working.
Now here is my question: How can I use plot3d() so that I can have a surface that connects all these points and gives a more jagged representation of the exact plot? Or how can I have each point in the z axis as a bar from the xy plane? I just want something more 3 dimensional than points and I can't find how to do this.
Thanks for your help
You can do this by triangulating the surface. You don't give us your actual data, but I can create some similar data using
f = function(x, y){
z = ((x^2)+(3*y^2))*exp(-(x^2)-(y^2))
}
x <- runif(500, -3, 3)
y <- runif(500, -3, 3)
z <- f(x, y)
Then the plotting is done using the method in ?persp3d.deldir:
library(deldir)
library(rgl)
col <- colorRampPalette(c("blue", "white"))(20)[1 + round(19*(z - min(z))/diff(range(z)))]
dxyz <- deldir::deldir(x, y, z = z, suppressMsge = TRUE)
persp3d(dxyz, col = col, front = "lines", back = "lines")
This might need some cosmetic fixes, e.g.
aspect3d(2, 2, 1)
After some rotation, this gives me the following plot:
I'm not sure to understand what you want. If my understanding is correct, here is a solution. Define a parametric representation of your surface:
fx <- function(u,v) u
fy <- function(u,v) v
fz <- function(u,v){
((u^2)+(3*v^2))*exp(-(u^2)-(v^2))
}
Let's say you have these points:
x0 <- seq(-3, 3, length.out = 20)
y0 <- seq(-3, 3, length.out = 20)
Then you can use the function parametric3d of the misc3d package, with the option fill=FALSE to get a wireframe:
library(misc3d)
parametric3d(fx, fy, fz, u=x0, v=y0,
color="blue", fill = FALSE)
Is it what you want?
To get some vertical bars, use the function segments3d of rgl:
i <- 8
bar <- rbind(c(x0[i],y0[i],0),c(x0[i],y0[i],f(x0[i],y0[i])))
segments3d(bar, color="red")
Here is a plot with only 50 points using my original code.
When I then apply what was said by Stéphane Laurent I then get this plot which feels too accurate when given the actual points I have
Perhaps you need to explain to me what is actually happening in the function parametric3d
I am trying to plot rings of trees and calculate their areas. However, I have noticed that in reality not all rings have symmetric radii like a circle. I have data measurements of 4 radii, and I would like to plot rings (or any similar shape) following each point of every radio like this example (this figure was done manually with vectors in PowerPoint):
the problem is that in R I found only the possibility to plot these rings with the circles option from the symbols() function, and I got this graph:
using this R script:
data <- data.frame(
a = c(1,4,5,8, 10),
b = c(1, 3,7,9, 10),
c = c(2, 6, 8, 9 ,10),
d = c(1, 3, 4, 7, 9) )
data$y <- (data$a - data$b)/2 # y position
data$x <- (data$d - data$c)/2 # x position
data$z <- rowMeans(data[,1:4]) # radio length
symbols(x = data$x, y = data$y, circles=data$z,
xlim = c(-10, 10)*1.5, ylim = c(-10, 10)*1.5, inches = F, fg = "orange", lwd = 2)
I have checked some packages with functions to draw ellipses (elliplot, ellipse, ellipseplot, car, etc), but I don't like their functions. I am not interested in use these packages, on the contrary I would like to write an own code.
My idea is to plot a shape which best meets the real figure of a ring with my data values of the four radii, it can be an ellipse, oval, etc.
With a circle I am using only data of one radio (in my example, the mean of all radii).
With a ellipse would be better, because I can use at least two values, the major-axis (A+B), and the minor-axis (C+D). But would be great to draw a shape that use the values of four radii (A, B, C, D) or even more radii.
Here a guy drew a very nice superellipse using a R script, and another one drew some ellipses likes rings also in R.
However, I don't know how to use their methods to my specific problem.
If somebody have idea how to start drawing at least an ellipse in R would be nice. But would be great to know how to draw a shape (oval, ellipse, etc.) using the values of four radii and finally calculate their area.
I would appreciate very much your help or any direction to do that.
UPDATE:
Thanks #cuttlefish44 for your excellent answer, that was very useful to explain tree growth to my students. However, most tropical trees have very irregular shapes and now I am wondering to know if can I draw this other shape with an additional radio "E" and the radii axes at different positions like this scheme:
any direction would be very useful for me.
If A & B are on y-axis and C & D are on x-axis, it isn't difficult to calculate the parameters of ellipses. I used optim() to get params (Note: this approach has tiny error, such as 2.439826e-12).
data manipulation
# change all data into xy coordinates and make ring-factor
library(reshape2); library(dplyr)
data <- data.frame(
a = c(1, 4, 5, 8, 10),
b = c(1, 3, 7, 9, 10) * -1,
c = c(2, 6, 8, 9, 10) * -1,
d = c(1, 3, 4, 7, 9) )
data <- t(data)
colnames(data) <- LETTERS[1:ncol(data)] # ring-factor
df <- melt(data, value.name = "x") # change into long-form
df$y <- df$x # make xy coordinates
df[df$Var1=="a"|df$Var1=="b", "x"] <- 0
df[df$Var1=="c"|df$Var1=="d", "y"] <- 0
calculation of center coordinates, ox & oy
center <- df %>% group_by(Var2) %>% summarize(sum(x)/2, sum(y)/2) %>% as.data.frame()
calculation of parameters of ellipse; semi-major and -minor axis, ra & rb
opt.f <- function(par, subset, center) { # target function
ox <- center[[1]] # par[1] and par[2] are ra and rb
oy <- center[[2]]
x <- subset$x
y <- subset$y
sum(abs((x - ox)^2/par[1]^2 + (y - oy)^2/par[2]^2 - 1)) # from ellipse equation
}
lev <- levels(df$Var2)
## search parameters
res <- sapply(1:length(lev), function(a)
optim(c(1,1), opt.f, subset = subset(df, Var2 == lev[a]),
center = center[a, 2:3], control = list(reltol = 1.0e-12)))
res # result. you can get detail by res[,1etc]. values are not 0 but much nearly 0
function to plot (Probably some packages have similar one)
radian <- function(degree) degree/180*pi
plot.ellipse <- function(ox, oy, ra, rb, phi=0, start=0, end=360, length=100, func=lines, ...) {
theta <- c(seq(radian(start), radian(end), length=length), radian(end))
if (phi == 0) {
func(ra*cos(theta)+ox, rb*sin(theta)+oy, ...)
} else {
x <- ra*cos(theta)
y <- rb*sin(theta)
phi <- radian(phi)
cosine <- cos(phi)
sine <- sin(phi)
func(cosine*x-sine*y+ox, sine*x+cosine*y+oy, ...)
}
}
draw
plot(0, type="n", xlim=c(-10, 10), ylim =c(-10, 10), asp=1, xlab="x", ylab="y", axes = F)
axis(1, pos=0);axis(2, pos=0, las=2)
points(df$x, df$y)
for(a in 1:length(lev)) plot.ellipse(ox = center[a, 2], oy = center[a, 3],
ra = res[,a]$par[1], rb = res[,a]$par[2], length=300)
area <- sapply(res[1,], function(a) pi * a[1] * a[2])
I have a following data:
library(rgl)
x <- c(rep(1,6),
rep(3,6),
rep(6,6),
rep(9,6),
rep(12,6))
y <- c(1.35,1.39,1.48,1.29,1.35,1.32,
NA,1.5,1.44,1.6,1.5,1.41,
NA,NA,1.72,1.56,1.6,1.55,
NA,NA,NA,1.95,1.9,1.75,
NA,NA,NA,NA,2.05,1.95)
z <- rep(1:6,5)
open3d()
plot3d(x,y,z, type = 'n')
lines3d(x,y,z)
Which is plotting lines in 3d as I expect.
But I cannot get it to plot a surface3d.
As I already read some threads I might need to interpolate my data. RGL docs has not cover this subject well. I tried akima but it doesn't accept NA's.
I would like to link lines to create a surface in linear way. I aware of the NA, so I expect that surface will be decreasing in the area for bigger x (more NA's).
Do I need to perform interpolation? If yes, how to do that on my sample data?
If no, how to achieve the surface3d on my sample data?
Thanks
the solution comes to me from this thread:
Making a wireframe plot from an x,y,z data.frame
below code will work for the sample data provided above (just switch x->y,y->z,z->x)
zmat <- matrix(data = z, nrow = 6, ncol = 5, byrow = FALSE)
surface3d(x = 1:6, y = c(1,3,6,9,12), z = zmat, alpha = 0.4, colour = 'blue')
have some data that I would like to add "stippling" to show where it is "important", as they do in the IPCC plots
At the moment I am really struggling with trying to do this in R.
If I make up some test data and plot it:
data <- array(runif(12*6), dim=c(12,6) )
over <- ifelse(data > 0.5, 1, 0 )
image(1:12, 1:6, data)
What I would like to finally do is over-plot some points based on the array "over" on top of the current image.
Any suggestions!??
This should help - I had do do a similar thing before and wrote a function that I posted here.
#required function from www.menugget.blogspot.com
matrix.poly <- function(x, y, z=mat, n=NULL){
if(missing(z)) stop("Must define matrix 'z'")
if(missing(n)) stop("Must define at least 1 grid location 'n'")
if(missing(x)) x <- seq(0,1,,dim(z)[1])
if(missing(y)) y <- seq(0,1,,dim(z)[2])
poly <- vector(mode="list", length(n))
for(i in seq(length(n))){
ROW <- ((n[i]-1) %% dim(z)[1]) +1
COL <- ((n[i]-1) %/% dim(z)[1]) +1
dist.left <- (x[ROW]-x[ROW-1])/2
dist.right <- (x[ROW+1]-x[ROW])/2
if(ROW==1) dist.left <- dist.right
if(ROW==dim(z)[1]) dist.right <- dist.left
dist.down <- (y[COL]-y[COL-1])/2
dist.up <- (y[COL+1]-y[COL])/2
if(COL==1) dist.down <- dist.up
if(COL==dim(z)[2]) dist.up <- dist.down
xs <- c(x[ROW]-dist.left, x[ROW]-dist.left, x[ROW]+dist.right, x[ROW]+dist.right)
ys <- c(y[COL]-dist.down, y[COL]+dist.up, y[COL]+dist.up, y[COL]-dist.down)
poly[[i]] <- data.frame(x=xs, y=ys)
}
return(poly)
}
#make vector of grids for hatching
incl <- which(over==1)
#make polygons for each grid for hatching
polys <- matrix.poly(1:12, 1:6, z=over, n=incl)
#plot
png("hatched_image.png")
image(1:12, 1:6, data)
for(i in seq(polys)){
polygon(polys[[i]], density=10, angle=45, border=NA)
polygon(polys[[i]], density=10, angle=-45, border=NA)
}
box()
dev.off()
Or, and alternative with "stipples":
png("hatched_image2.png")
image(1:12, 1:6, data)
for(i in seq(polys)){
xran <- range(polys[[i]]$x)
yran <- range(polys[[i]]$y)
xs <- seq(xran[1], xran[2],,5)
ys <- seq(yran[1], yran[2],,5)
grd <- expand.grid(xs,ys)
points(grd, pch=19, cex=0.5)
}
box()
dev.off()
Update:
In (very late) response to Paul Hiemstra's comment, here are two more examples with a matrix of higher resolution. The hatching maintains a nice regular pattern, but it is not nice to look at when broken up. The stippled example is much nicer:
n <- 100
x <- 1:n
y <- 1:n
M <- list(x=x, y=y, z=outer(x, y, FUN = function(x,y){x^2 * y * rlnorm(n^2,0,0.2)}))
image(M)
range(M$z)
incl <- which(M$z>5e5)
polys <- matrix.poly(M$x, M$y, z=M$z, n=incl)
png("hatched_image.png", height=5, width=5, units="in", res=400)
op <- par(mar=c(3,3,1,1))
image(M)
for(i in seq(polys)){
polygon(polys[[i]], density=10, angle=45, border=NA, lwd=0.5)
polygon(polys[[i]], density=10, angle=-45, border=NA, lwd=0.5)
}
box()
par(op)
dev.off()
png("stippled_image.png", height=5, width=5, units="in", res=400)
op <- par(mar=c(3,3,1,1))
image(M)
grd <- expand.grid(x=x, y=y)
points(grd$x[incl], grd$y[incl], pch=".", cex=1.5)
box()
par(op)
dev.off()
Do it using the coordinate positioning mechanism of ?image [1].
data(volcano)
m <- volcano
dimx <- nrow(m)
dimy <- ncol(m)
d1 <- list(x = seq(0, 1, length = dimx), y = seq(0, 1, length = dimy), z = m)
With your 'image' constructed that way you keep the structure with the object, and its
coordinates intact. You can collect multiple matrices into a 3D array or as multiple
elements, but you need to augment image() in order to handle that, so I keep them
separate here.
Make a copy of the data to specify an interesting area.
d2 <- d1
d2$z <- d2$z > 155
Use the coordinates to specify which cells are interesting. This is expensive if you have a very big raster, but it's super easy to do.
pts <- expand.grid(x = d2$x, y = d2$y)
pts$over <- as.vector(d2$z)
Set up the plot.
op <- par(mfcol = c(2, 1))
image(d1)
image(d1)
points(pts$x[pts$over], pts$y[pts$over], cex = 0.7)
par(op)
Don't forget to modify the plotting of points to get different effects, in particular a very dense grid with lots of points will take ages to draw all those little circles. pch = "." is a good choice.
Now, do you have some real data to plot on that nice projection? See examples here for some of the options: http://spatial-analyst.net/wiki/index.php?title=Global_datasets
[1] R has classes for more sophisticated handling of raster data, see package sp and raster
for two different approaches.
This is a solution in the spirit of #mdsummer's comment using ggplot2. I first draw the grid, and then draw +'es at the locations where a certain value has been exceeded. Note that ggplot2 works with data.frame's, not with multi-dimensional arrays or matrices. You can use melt from the reshape package to convert from an array / marix to a data.frame flat structure.
Here is a concrete example using the example data from the geom_tile documentation:
pp <- function (n,r=4) {
x <- seq(-r*pi, r*pi, len=n)
df <- expand.grid(x=x, y=x)
df$r <- sqrt(df$x^2 + df$y^2)
df$z <- cos(df$r^2)*exp(-df$r/6)
df
}
require(ggplot2)
dat = pp(200)
over = dat[,c("x","y")]
over$value = with(dat, ifelse(z > 0.5, 1, 0))
ggplot(aes(x = x, y = y), data = dat) +
geom_raster(aes(fill = z)) +
scale_fill_gradient2() +
geom_point(data = subset(over, value == 1), shape = "+", size = 1)
This is probably coming too late, but I'd like to post my answer as a reference too.
One nice option for spatial data is to use the rasterVis package. Once you have a "base" raster object, and the "mask" object, which you will use to draw the stippling, you can do something like:
require(raster)
require(rasterVis)
# Scratch raster objects
data(volcano)
r1 <- raster(volcano)
# Here we are selecting only values from 160 to 180.
# This will be our "mask" layer.
over <- ifelse(volcano >=160 & volcano <=180, 1, NA)
r2 <- raster(over)
# And this is the key step:
# Converting the "mask" raster to spatial points
r.mask <- rasterToPoints(r2, spatial=TRUE)
# Plot
levelplot(r1, margin=F) +
layer(sp.points(r.mask, pch=20, cex=0.3, alpha=0.8))
which resembles the map that the OP was looking for. Parameters of the points such as color, size and type can be fine tuned. ?sp.points provides all the arguments that can be used to do that.
I searched a lot of questions about heatmap throughout the site and packages, but I still have a problem.
I have clustered data (kmeans/EM/DBscan..), and I want to create a heatmap by grouping the same cluster. I want the similar color patterns to be grouped in the heatmap, so generally, it looks like a block-diagonal.
I tried to order the data by the cluster number and display it,
k = kmeans(data, 3)
d = data.frame(data)
d = data.frame(d, k$cluster)
d = d[order(d$k.cluster),]
heatmap(as.matrix(d))
but it is still not sorted and looks like this link: But, I want it to be sorted by its cluster number and looked like this:
Can I do this in R?
I searched lots of packages and tried many ways, but I still have a problem.
Thanks a lot.
You can do this using reshape2 and ggplot2 as follows:
library(reshape2)
library(ggplot2)
# Create dummy data
set.seed(123)
df <- data.frame(
a = sample(1:5, 1000, replace=TRUE),
b = sample(1:5, 1000, replace=TRUE),
c = sample(1:5, 1000, replace=TRUE)
)
# Perform clustering
k <- kmeans(df, 3)
# Append id and cluster
dfc <- cbind(df, id=seq(nrow(df)), cluster=k$cluster)
# Add idsort, the id number ordered by cluster
dfc$idsort <- dfc$id[order(dfc$cluster)]
dfc$idsort <- order(dfc$idsort)
# use reshape2::melt to create data.frame in long format
dfm <- melt(dfc, id.vars=c("id", "idsort"))
ggplot(dfm, aes(x=variable, y=idsort)) + geom_tile(aes(fill=value))
You should set Rowv and Colv to NA if you don't want the dendrograms and the subseuent ordering. BTW, You should also put of the scaling. Using the df of Andrie :
heatmap(as.matrix(df)[order(k$cluster),],Rowv=NA,Colv=NA,scale="none",labRow=NA)
In fact, this whole heatmap is based on image(). You can hack away using image to construct a plot exactly like you want. Heatmap is using layout() internally, so it will be diffucult to set the margins. With image you could do eg :
myHeatmap <- function(x,ord,xlab="",ylab="",main="My Heatmap",
col=heat.colors(5), ...){
op <- par(mar=c(3,0,2,0)+0.1)
on.exit(par(op))
nc <- NCOL(x)
nr <- NROW(x)
labCol <- names(x)
x <- t(x[ord,])
image(1L:nc, 1L:nr, x, xlim = 0.5 + c(0, nc), ylim = 0.5 +
c(0, nr), axes = FALSE, xlab=xlab, ylab=ylab, main=main,
col=col,...)
axis(1, 1L:nc, labels = labCol, las = 2, line = -0.5, tick = 0)
axis(2, 1L:nr, labels = NA, las = 2, line = -0.5, tick = 0)
}
library(RColorBrewer)
myHeatmap(df,order(k$cluster),col=brewer.pal(5,"BuGn"))
To produce a plot that has less margins on the side. You can also manipulate axes, colors, ... You should definitely take a look at the RColorBrewerpackage
(This custom function is based on the internal plotting used by heatmap btw, simplified for the illustration and to get rid of all the dendrogram stuff)