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I want create a scatter plot. I have point size and colour in additional variables (vectors).
x = Float64.(1:10)
y = Float64.(1:10)
c = Float64.(1:10)
s = Float64.(1:10)
scatter(x,y)
I've been googling very long now and could not find the right answer. What I am looking for is this:
This should get you part of the way there:
x = 1.0:10.0
y = 1.0:10.0
c = 1.0:10.0
s = 1.0:10.0
theme(:ggplot2)
scatter(x, y; zcolor=c, markersize=s, color=:oslo)
I didn't have time to try to figure out the legends.
There are a nearly unlimited number of colorschemes, have a look here if this one isn't exactly right: https://docs.juliaplots.org/latest/generated/colorschemes/
My objective is to portray the locations with varying numbers of traffic conflicts in a road intersection. My data consists of all the conflicts that we observed in a given time period at an intersection coded into a .CSV file with the following fields "time of conflict", "TTC" (means Time to Collision), "Lat", "Lon" and "Conflict Type". I figured the best way to do so would be using the 'ggmap+stat_density2d' function in R. I am using the following code:
df = read.csv(filename, header = TRUE)
int.map = get_map(location = c(mean.long, mean.lat), zoom = 20, maptype = "satellite")
int.map = ggmap(int.map, extent ="device", legend = "right")'''
int.map +stat_density2d(data = new_xdf, aes(x, y, fill = ..levels.., alpha = ..levels..),
geom = "polygon")
int.map + scale_fill_gradientn(guide = "colourbar", colours = rev(brewer.pal(7,"Spectral")),
name = "Conflict Density")
The output is a very nice map Safety Heat Map that correctly portrays the conflict hotspots. My problem is that in the legends it gives the values of "levels" automatically calculated by the 'stat_density2d()' function. I tried searching for a way to display, say, the counts of all conflict points inside each level on the legend bar but to no avail.
I did find the below link that handles a similar question, but the problem with that is that it creates a new data frame (new_xdf) with much more points than in the original data. Thus, the counts determined in that program seems to be of no use to me as I want the exact number of conflict points in my original data to be displayed in the legends bar.
How to find points within contours in R?
Thanks in advance.
Edit: Link to a sample data file
https://docs.google.com/spreadsheets/d/11vc3lOhzQ-tgEiAXe-MNw2v3fsAqnadweVrvBdNyNuo/edit?usp=sharing
I have a grid and I want to produce a map out of this grid with some map elements (scale, north arrow, etc). I have no problem drawing the grid and the coloring I need, but the additional map elements won't show on the map. I tried putting first=TRUE to the sp.layout argument according to the sp manual, but still no success.
I reproduced the issue with the integrated meuse dataset, so you may just copy&paste that code. I use those package versions: lattice_0.20-33 and sp_1.2-0
library(sp)
library(lattice) # required for trellis.par.set():
trellis.par.set(sp.theme()) # sets color ramp to bpy.colors()
alphaChannelSupported = function() {
!is.na(match(names(dev.cur()), c("pdf")))
}
data(meuse)
coordinates(meuse)=~x+y
data(meuse.riv)
library(gstat, pos = match(paste("package", "sp", sep=":"), search()) + 1)
data(meuse.grid)
coordinates(meuse.grid) = ~x+y
gridded(meuse.grid) = TRUE
v.uk = variogram(log(zinc)~sqrt(dist), meuse)
uk.model = fit.variogram(v.uk, vgm(1, "Exp", 300, 1))
meuse[["ff"]] = factor(meuse[["ffreq"]])
meuse.grid[["ff"]] = factor(meuse.grid[["ffreq"]])
zn.uk = krige(log(zinc)~sqrt(dist), meuse, meuse.grid, model = uk.model)
zn.uk[["se"]] = sqrt(zn.uk[["var1.var"]])
meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
rv = list("sp.polygons", meuse.sr, fill = "lightblue")
sampling = list("sp.points", meuse.riv, color = "black")
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4)
text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4)
text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(181300,329800),
scale = 400, which = 4)
library(RColorBrewer)
library(lattice)
trellis.par.set(sp.theme())
precip.pal <- colorRampPalette(brewer.pal(7, name="Blues"))
spplot(zn.uk, "var1.pred",
sp.layout = list(rv, sampling, scale, text1, text2),
main = "log(zinc); universal kriging standard errors",
col.regions=precip.pal,
contour=TRUE,
col='black',
pretty=TRUE,
scales=list(draw = TRUE),
labels=TRUE)
And that's how it looks...all naked:
So my questions:
Where is the scale bar, north arrow, etc hiding? Did I miss something? Every example I could find on the internet looks similar to that. On my own dataset I can see the scale bar and north arrow being drawn at first, but as soon as the grid is rendered, it superimposes the additional map elements (except for the scale text, that is shown on the map - not the bar and north arrow for some reason I don't seem to comprehend).
The error message appearing on the map just shows when I try to add the sampling locations sampling = list("sp.points", meuse.riv, color = "black"). Without this entry, the map shows without error, but also without additional map elements. How can I show the sampling points on the map (e.g. in circles whose size depends on the absolute value of this sampling point)?
This bothered me for many, many hours by now and I can't find any solution to this. In Bivand et al's textbook (2013) "Applied Spatial Data Analysis with R" I could read the following entry:
The order of items in the sp.layout argument matters; in principle objects
are drawn in the order they appear. By default, when the object of spplot has
points or lines, sp.layout items are drawn before the points to allow grids
and polygons drawn as a background. For grids and polygons, sp.layout
items are drawn afterwards (so the item will not be overdrawn by the grid
and/or polygon). For grids, adding a list element first = TRUE ensures that
the item is drawn before the grid is drawn (e.g. when filled polygons are added). Transparency may help when combining layers; it is available for the
PDF device and several other devices.
Function sp.theme returns a lattice theme that can be useful for plots
made by spplot; use trellis.par.set(sp.theme()) after a device is opened
or changed to make this effective.
However, also with this additional information I wasn't able to solve this problem. Glad for any hint!
The elements you miss are being drawn in panel four, which does not exist, so are not being drawn. Try removing the which = 4.
meuse.riv in your example is a matrix, which causes the error message, but should be a SpatialPoints object, so create sampling by:
sampling = list("sp.points", SpatialPoints(meuse.riv), color = "black")
When working from examples, my advice is to choose examples as close as possible to what you need, and only change one thing at a time.
I want to achieve the following outcomes:
Rescale the size of the bubbles such that the largest bubble has a
diameter of 1 (on whichever has the more compressed scale of the x
and y axes).
Rescale the size of the bubbles such that the smallest bubble has a diameter of 1 mm
Have a legend with the first and last points the minimum non-zero
frequency and the maximum frequency.
The best I have been able to do is as follows, but I need a more general solution where the value of maxSize is computed rather than hard-coded. If I was doing it in the traditional R plots I would use par("pin") to work out the size of plot area and work backwards, but I cannot figure out how to access this information with ggplot2. Any suggestions?
library(ggplot2)
agData = data.frame(
class=rep(1:7,3),
drv = rep(1:3,rep(7,3)),
freq = as.numeric(xtabs(~class+drv,data = mpg))
)
agData = agData[agData$freq != 0,]
rng = range(agData$freq)
mn = rng[1]
mx = rng[2]
minimumArea = mx - mn
maxSize = 20
minSize = max(1,maxSize * sqrt(mn/mx))
qplot(class,drv,data = agData, size = freq) + theme_bw() +
scale_area(range = c(minSize,maxSize),
breaks = seq(mn,mx,minimumArea/4), limits = rng)
Here is what it looks like so far:
When no ggplot, lattice or other highlevel package seems to do the job without hours of fine tuning I always revert to the base graphics. The following code gets you what you want, and after it I have another example based on how I would have plotted it.
Note however that I have set the maximum radius to 1 cm, but just divide size.range/2 to get diameter instead. I just thought radius gave me nicer plots, and you'll probably want to adjust things anyways.
size.range <- c(.1, 1) # Min and max radius of circles, in cm
# Calculate the relative radius of each circle
radii <- sqrt(agData$freq)
radii <- diff(size.range)*(radii - min(radii))/diff(range(radii)) + size.range[1]
# Plot in two panels
mar0 <- par("mar")
layout(t(1:2), widths=c(4,1))
# Panel 1: The circles
par(mar=c(mar0[1:3],.5))
symbols(agData$class, agData$drv, radii, inches=size.range[2]/cm(1), bg="black")
# Panel 2: The legend
par(mar=c(mar0[1],.5,mar0[3:4]))
symbols(c(0,0), 1:2, size.range, xlim=c(-4, 4), ylim=c(-2,4),
inches=1/cm(1), bg="black", axes=FALSE, xlab="", ylab="")
text(0, 3, "Freq")
text(c(2,0), 1:2, range(agData$freq), col=c("black", "white"))
# Reset par settings
par(mar=mar0)
Now follows my suggestion. The largest circle has a radius of 1 cm and area of the circles are proportional to agData$freq, without forcing a size of the smallest circle. Personally I think this is easier to read (both code and figure) and looks nicer.
with(agData, symbols(class, drv, sqrt(freq),
inches=size.range[2]/cm(1), bg="black"))
with(agData, text(class, drv, freq, col="white"))
See this example
This was created in matlab by making two scatter plots independently, creating images of each, then using the imagesc to draw them into the same figure and then finally setting the alpha of the top image to 0.5.
I would like to do this in R or matlab without using images, since creating an image does not preserve the axis scale information, nor can I overlay a grid (e.g. using 'grid on' in matlab). Ideally I wold like to do this properly in matlab, but would also be happy with a solution in R. It seems like it should be possible but I can't for the life of me figure it out.
So generally, I would like to be able to set the alpha of an entire plotted object (i.e. of a matlab plot handle in matlab parlance...)
Thanks,
Ben.
EDIT: The data in the above example is actually 2D. The plotted points are from a computer simulation. Each point represents 'amplitude' (y-axis) (an emergent property specific to the simulation I'm running), plotted against 'performance' (x-axis).
EDIT 2: There are 1796400 points in each data set.
Using ggplot2 you can add together two geom_point's and make them transparent using the alpha parameter. ggplot2 als adds up transparency, and I think this is what you want. This should work, although I haven't run this.
dat = data.frame(x = runif(1000), y = runif(1000), cat = rep(c("A","B"), each = 500))
ggplot(aes(x = x, y = y, color = cat), data = dat) + geom_point(alpha = 0.3)
ggplot2 is awesome!
This is an example of calculating and drawing a convex hull:
library(automap)
library(ggplot2)
library(plyr)
loadMeuse()
theme_set(theme_bw())
meuse = as.data.frame(meuse)
chull_per_soil = ddply(meuse, .(soil),
function(sub) sub[chull(sub$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.