I am trying to plot a 3D space time cube in R and I want to have a basemap.
I am using rgl library. I know how to plot my data using x, y and z, where z is the time variable. I have also managed to download a map that I want to use as reference from openstreetmap, using the library in R. However, I cannot find a way to plot my data on the map in a 3D environment. I found the following code in several sites and as an answer to a similar question:
map3d <- function(map, ...){
if(length(map$tiles)!=1){stop("multiple tiles not implemented") }
nx = map$tiles[[1]]$xres
ny = map$tiles[[1]]$yres
xmin = map$tiles[[1]]$bbox$p1[1]
xmax = map$tiles[[1]]$bbox$p2[1]
ymin = map$tiles[[1]]$bbox$p1[2]
ymax = map$tiles[[1]]$bbox$p2[2]
xc = seq(xmin,xmax,len=ny)
yc = seq(ymin,ymax,len=nx)
colours = matrix(map$tiles[[1]]$colorData,ny,nx)
m = matrix(0,ny,nx)
surface3d(xc,yc,m,col=colours, ...)
}
However, I cannot really understand how it works.
Here's my code so far:
library(rgl)
library(ggplot2)
library(OpenStreetMap)
map <- openmap(c(53.5,73.6),c(15.7,134.7),type= 'esri-topo')
plot3d(x,y,z, col= colour) # to plot my data
autoplot(map) # to plot the map. though this is 2D
Again, I know how to plot my data on a 2D map. Confused with the 3D.
Any hints and tips on how to do this?
One option is to use the newish 'show2d' function in 'rgl'.
library(rgl)
library(OpenStreetMap)
library(raster)
map <- openmap(c(53.5,73.6),c(15.7,134.7),type= 'esri-topo')
## fake up some xyz
xyz <- expand.grid(x = map$bbox$p1,
y = map$bbox$p2,
z = 1:4)
plot3d(xyz, col = "black") # to plot my data
EDIT: this is wrong, it's only fitted to the bounding box
getting the orientation right is confusing, needs to be check with x, y, z arguments to show2d.
show2d(raster::plotRGB(raster(map)))
This function captures the normal plot expression, writes it to PNG and then texture maps it onto a quad in the scene.
I can't quite see how to control the position of the quad for the image texture with the x, y, z args - work in progress.
Related
In R, using package rgl, I'd like to add the shadows of the points in plot3d(), just like in the image below.
I've added the bottom grid using grid3d(), but still have no clue on how to add the shadows. If I plot the same points changing the 3rd axis value to its minimum value (-100 in the image), the plot area automatically increases, leaving a gap between the points and the grid. Is there a better way to do that?
I think it was obvious from the question, but here is a sample code:
library(rgl)
df <- data.frame(x=rnorm(100),
y=rnorm(100),
z=rnorm(100))
plot3d(df)
grid3d('z')
The idea of setting z to the minimal value fails because rgl makes the bounding region slightly bigger. But you can grab the z value from the grid, and use that. You can also tell rgl not to expand the bounding box to include the new points. This code does both things:
library(rgl)
df <- data.frame(x=rnorm(100),
y=rnorm(100),
z=rnorm(100))
plot3d(df)
id <- grid3d('z') # Get id values for grid
gridz <- rgl.attrib(id[1], "vertices")[1,3] # Use the first z value
save <- par3d(ignoreExtent = TRUE) # Ignore points for bbox
with(df, points3d(x, y, gridz, col = "gray"))# Plot the "shadows"
par3d(save) # Restore bbox status
Here's what I get:
there is now the convenience show2d function available to produce the desired 2D projections
library(rgl)
df <- data.frame(x=rnorm(100),
y=rnorm(100),
z=rnorm(100))
plot3d(df)
grid3d('z')
show2d({
par(mar=c(0,0,0,0))
plot(x = df$x, y = df$y,
col = "black")
})
I'm trying to create 3D plots of simulated tree roots in R. Here is an example of a root system growing over time:
This is essentially a 3D network of cylinders, where the cylinder diameter (and, optionally, color) represents the size of the root. The available data includes:
x, y, z of the root centroid
direction of "parent" root (e.g. +x, -x, +y, -y, +z, -z), although this information could be captured in several different ways, including by calculating the x, y, z of the parent directly prior to plotting.
size of root
Example 3D data is here, but here is my first attempt at it in just 2D using ggplot2::geom_spoke:
dat <- data.frame(x = c(0,1,-1,0,1,-1),
y = c(-1,-1,-1,-2,-2,-2),
biomass = c(3,1.5,1.5,1,1,1),
parent.dir = c("+y","-x","+x","+y","+y","+y"))
dat$parent.dir <- as.numeric(as.character(factor(dat$parent.dir,
levels = c("-x", "+x", "-y", "+y"),
labels = c(pi, 0, pi*3/2, pi/2))))
ggplot(dat, aes(x = x, y = y)) +
geom_point(x = 0, y = 0, size = 20) +
geom_spoke(radius = 1,
aes(angle = parent.dir,
size = biomass)) +
coord_equal()
I prefer a solution based in the ggplot2 framework, but I realize that there are not a ton of 3D options for ggplot2. One interesting approach could be to creatively utilize the concept of network graphs via the ggraph and tidygraph packages. While those packages only operate in 2D as far as I know, their developer has also had some interesting related ideas in 3D that could also be applied.
The rgl library in seems to be the go-to for 3D plots in R, but an rgl solution just seems so much more complex and lacks the other benefits of ggplot2, such as faceting by year as in the example, easily adjusting scales, etc.
Example data is here:
I don't understand the format of your data so I'm sure this isn't the display you want, but it shows how to draw a bunch of cylinders in rgl:
root <- read.csv("~/temp/root.csv")
segments <- data.frame(row.names = unique(root$parent.direction),
x = c(-1,0,1,0,0),
y = c(0,1,0,0,-1),
z = c(0,0,0,0.2,0))
library(rgl)
open3d()
for (i in seq_len(nrow(root))) {
rbind(root[i,2:4],
root[i,2:4] - segments[root$parent.direction[i],]) %>%
cylinder3d(radius = root$size[i]^0.3, closed = -2, sides = 20) %>%
shade3d(col = "green")
}
decorate3d()
This gives the following display (rotatable in the original):
You can pass each cylinder through addNormals if you want it to look smooth, or use sides = <some big number> in the cylinder3d to make them look rounder.
Lets consider that I have five 2D-Matrices which describe the magnetic field at different z-Layers. A nice, smoothed version of a 2D-Surface plot can be obtained as follows:
data2_I<-matrix(c(1.0,1.0,0.6,0.6,0.7,0.9,0.9,0.5,0.5,0.5,0.7,0.9,0.9,0.6,0.3,0.4,0.7,0.9,0.9,0.7,0.5,0.5,0.6,0.9,0.9,0.7,0.6,0.6,1.0,1.0), nrow=5)
Z = as.vector(data2_I)
length(Z)
XY=data.frame(x=as.numeric(gl(5,1,30)),y=as.numeric(gl(5,6,30)))
t=Tps(XY,Z)
surface(t)
Now it would be great if I could get a 3D-plot where at different z-Positions these surfaces are plotted. Is there a possibility to do that?
I found an alternative approach: With the package rgl I and the function surface 3D I can stack several 3D-Surface plots within one open3d-window. Lets look at a small example:
library("rgl")
data2_I<-matrix(c(1.0,1.0,0.6,0.6,0.7,0.9,0.9,0.5,0.5,0.5,0.7,0.9,0.9,0.6,0.3,0.4,0.7,0.9,0.9,0.7,0.5,0.5,0.6,0.9,0.9,0.7,0.6,0.6,1.0,1.0), nrow=5)
data0_I<-matrix(c(1.0,1.0,0.6,0.6,0.7,0.9,0.9,0.5,0.5,0.5,0.7,0.9,0.9,0.6,0.3,0.4,0.7,0.9,0.9,0.7,0.5,0.5,0.6,0.9,0.9,0.7,0.6,0.6,1.0,1.0), nrow=5)
data1_I<-2*data0_I
data2_I<-1/data1_I
elv=0
offs=5*elv+1
z0 <- scale*data0_I
z1 <- scale*data1_I
z2 <- scale*data2_I
x <- 1:nrow(z0)
y <- 1:ncol(z0)
palette <- colorRampPalette(c("blue","green","yellow", "red"))
col.table <- palette(256)
open3d(windowRect=c(50,50,800,800))
surface3d(x, y, elv*z0, color = col.table[cut(z0, 256)], back = "lines")
surface3d(x, y, elv*z1+1*offs, color = col.table[cut(z1, 256)], back = "lines")
surface3d(x, y, elv*z2+2*offs, color = col.table[cut(z2, 256)], back = "lines")
axes3d()
aspect3d(1,1,2)
The variables offsand elv are included for cosmetic purposes: offs controls the space between two surface plots and elevation how the z-axes of the surface3d-plots should scale. As I wanted to have a 2D surface plot without any elevation I set it to zero.
History: Extracted raster data from the static Google map png, loaded it on the R device through ggimage.
library (png)
library (ggmap)
rasterArray <- readPNG ("My.png")
x = c (40.702147,40.718217,40.711614)
y = c (-74.012318,-74.015794,-73.998284)
myData <- data.frame (x, y)
print (ggimage (rasterArray, fullpage = TRUE, coord_equal = FALSE)
+ geom_point (aes (x = x, y = y), data = myData, colour = I("green"),
size = I(5), fill = NA))
I did run dput on the rasterArray but the output is of 20 MBs, can't post here.
BTW, this is the URL of that static map:
Question: For plotting "GPS coordinates" on the R device containing the map in pixels, do I need to scale the data.frame?
I saw this page: http://www-personal.umich.edu/~varel/rdatasets/Langren1644.html
Do I need to do scaling the way they have shown here?
If yes, then what else other than the man page of scale function do I need to understand to get this done?
Am I barking at the wrong tree?
I think your mistake was the following:
Trying to plot geographic data on an image, where that image doesn't have any awareness of the map coordinates
Possibly transposing your latitude and longitudes in the data frame
Here is how you should do it instead, in two steps:
Get the map with get_map() and save it to disk using save()
Plot the data with ggmap()
First, get the map.
library (ggmap)
# Read map from google maps and save data to file
mapImageData <- get_googlemap(
c(lon=-74.0087986666667, lat=40.7106593333333),
zoom=15
)
save(mapImageData, file="savedMap.rda")
Then, in a new session:
# Start a new session (well, clear the workspace, to be honest)
rm(list=ls())
# Load the saved file
load(file="savedMap.rda")
# Set up some data
myData <- data.frame(
lat = c (40.702147, 40.718217, 40.711614),
lon = c (-74.012318, -74.015794, -73.998284)
)
# Plot
ggmap(mapImageData) +
geom_point(aes(x=lon, y=lat), data=myData, colour="red", size=5)
See this example
This was created in matlab by making two scatter plots independently, creating images of each, then using the imagesc to draw them into the same figure and then finally setting the alpha of the top image to 0.5.
I would like to do this in R or matlab without using images, since creating an image does not preserve the axis scale information, nor can I overlay a grid (e.g. using 'grid on' in matlab). Ideally I wold like to do this properly in matlab, but would also be happy with a solution in R. It seems like it should be possible but I can't for the life of me figure it out.
So generally, I would like to be able to set the alpha of an entire plotted object (i.e. of a matlab plot handle in matlab parlance...)
Thanks,
Ben.
EDIT: The data in the above example is actually 2D. The plotted points are from a computer simulation. Each point represents 'amplitude' (y-axis) (an emergent property specific to the simulation I'm running), plotted against 'performance' (x-axis).
EDIT 2: There are 1796400 points in each data set.
Using ggplot2 you can add together two geom_point's and make them transparent using the alpha parameter. ggplot2 als adds up transparency, and I think this is what you want. This should work, although I haven't run this.
dat = data.frame(x = runif(1000), y = runif(1000), cat = rep(c("A","B"), each = 500))
ggplot(aes(x = x, y = y, color = cat), data = dat) + geom_point(alpha = 0.3)
ggplot2 is awesome!
This is an example of calculating and drawing a convex hull:
library(automap)
library(ggplot2)
library(plyr)
loadMeuse()
theme_set(theme_bw())
meuse = as.data.frame(meuse)
chull_per_soil = ddply(meuse, .(soil),
function(sub) sub[chull(sub$x, sub$y),c("x","y")])
ggplot(aes(x = x, y = y), data = meuse) +
geom_point(aes(size = log(zinc), color = ffreq)) +
geom_polygon(aes(color = soil), data = chull_per_soil, fill = NA) +
coord_equal()
which leads to the following illustration:
You could first export the two data sets as bitmap images, re-import them, add transparency:
library(grid)
N <- 1e7 # Warning: slow
d <- data.frame(x1=rnorm(N),
x2=rnorm(N, 0.8, 0.9),
y=rnorm(N, 0.8, 0.2),
z=rnorm(N, 0.2, 0.4))
v <- with(d, dataViewport(c(x1,x2),c(y, z)))
png("layer1.png", bg="transparent")
with(d, grid.points(x1,y, vp=v,default="native",pch=".",gp=gpar(col="blue")))
dev.off()
png("layer2.png", bg="transparent")
with(d, grid.points(x2,z, vp=v,default="native",pch=".",gp=gpar(col="red")))
dev.off()
library(png)
i1 <- readPNG("layer1.png", native=FALSE)
i2 <- readPNG("layer2.png", native=FALSE)
ghostize <- function(r, alpha=0.5)
matrix(adjustcolor(rgb(r[,,1],r[,,2],r[,,3],r[,,4]), alpha.f=alpha), nrow=dim(r)[1])
grid.newpage()
grid.rect(gp=gpar(fill="white"))
grid.raster(ghostize(i1))
grid.raster(ghostize(i2))
you can add these as layers in, say, ggplot2.
Use the transparency capability of color descriptions. You can define a color as a sequence of four 2-byte words: muddy <- "#888888FF" . The first three pairs set the RGB colors (00 to FF); the final pair sets the transparency level.
AFAIK, your best option with Matlab is to just make your own plot function. The scatter plot points unfortunately do not yet have a transparency attribute so you cannot affect it. However, if you create, say, most crudely, a bunch of loops which draw many tiny circles, you can then easily give them an alpha value and obtain a transparent set of data points.