Related
Without using ggplot2 or other plotting libraries, I would need to draw circles around a polygon/star chart vertices, i.e. each circle with a radius equal to the respective polygon radius. You can see an example here:
d1 <- 1:4
names(d1) <- LETTERS[1:4]
stars(matrix(d1,nrow=1),axes=TRUE, scale=FALSE,radius=TRUE, frame.plot=TRUE,labels = dimnames(d1)[[1]])
grid()[enter image description here][1]
I understand I should combine the stars() with the symbols(), polygon() functions or par(...) graphics, but honestly, I am new to these kind of plotting techniques and very lost on how to combine functions and arguments
I don't know of any functions in base R that do circles for you, but you can concoct them manually.
center <- c(x=2.1, y=2.1) # probably a better way
half <- seq(0, pi, length.out = 51)
for (D in d1) {
Xs <- D * cos(half); Ys <- D * sin(half)
lines(center["x"] + Xs, center["y"] + Ys, col = "gray", xpd = NA)
lines(center["x"] + Xs, center["y"] - Ys, col = "gray", xpd = NA)
}
Notes:
I don't know off-hand how the center-point should be calculated, I chose that point using locator(1); not being familiar with stars, there may be a better way to determine this programmatically and more accurately;
The first lines(.) draws the upper semi-circle; the second draws the lower.
The xpd=NA is to preclude clipping due to the drawing margin. It may not be necessary in your "real" data. See ?par for more details on this.
Though it may be difficult to detect here, the gray circles are drawn on top of the stars plot, which might be an aesthetic compromise. The only way around that is to plot the circles first. To do this, draw the first semicircle first with plot(..., type="l") and then add the remainder as expected, and only then run stars(..., add=TRUE).
I am plotting the density of a two-dimensional, weighted spatial point pattern. I'd like to make the plot without a colour scale legend, and save it with no (or minimal) boarders on all sides, like this: My problem is that I can't remove the colour scale legend. Reproducible code below:
## Install libraries:
library(spatstat) #spatial package
library(RColorBrewer) #create custom colour ramps
## Create reproducible data:
data <- data.frame(matrix(ncol = 3, nrow = 50))
x <- c("x", "y", "weight")
colnames(data) <- x
data$x <- runif(50, 0, 20)
data$y <- runif(50, 0, 20)
data$weight <- sample(1:200, 50)
## Set plotting window and colours:
plot.win <- owin(c(0,20), c(0,20)) # plot window as 20x20m
spat.bat.frame <- NULL # create a frame to store values in
cols1<-colorRampPalette(brewer.pal(9,"Blues"))(100) #define colour ramp for density plots
## Create and save plots:
jpeg(filename = "Bad plot.jpeg", res = 300, units = "cm", width = 20, height = 20)
par(mar=c(0,0,0,0),oma=c(0,0,0,0),lwd=1)
ppp_01 <- ppp(x = data$x, y = data$y, window = plot.win)
ppp_02 <- ppp(x = data$x, y = data$y, window = plot.win)
plot(density(ppp_01, weights = data$weights), main=NULL, col=cols1, sigma = 1)
plot(ppp_02, add=TRUE) #add spp points to density plot
dev.off()
I've tried legend=FALSE, auto.key=FALSE, colorkey=FALSE, which don't seem to be compatible with plot() (i.e. they don't give an error but don't change anything). I've also tried some work-arounds like saving a cropped image with dev.off.crop() or by adjusting margins with par(), but haven't been able to completely remove the legend. Does anyone have any suggestions on how to remove a colour scale legend of a density spp (real-valued pixel image) using plot()?
I specifically need to plot the density of the spatial point pattern, to specify a custom colour ramp, and to overlay the spp points onto the density image. I could try plotting with spplot() instead, but I'm not sure this will allow for these three things, and I feel like I'm missing a simple fix with plot(). I can't crop the figures manually after saving from R because there are 800 of them, and I need them all to be exactly the same size and in the exact same position.
Thank you!
Since plot is a generic function, the options available for controlling the plot will depend on the class of object that is being plotted. You want to plot the result of density(ppp_01, weights = data$weights). Let's call this Z:
Z <- density(ppp_01, weights = data$weights, sigma=1)
Note: the smoothing bandwidth sigma should be given inside the call to density
To find out about Z, you can just print it, or type class(Z).
The result is that Z is an object of class"im" (pixel image).
So you need to look up the help file for plot.im, the plot method for class "im". Typing ?plot.im shows that there is an argument ribbon that controls whether or not the colour ribbon is displayed. Simply set ribbon=FALSE in the call to plot:
plot(Z, ribbon=FALSE, main="", col=cols1)
Or in your original code
plot(density(ppp_01, weights=data$weights, sigma=1), main="", col=cols1)
However I strongly recommend separating this into two lines, one which creates the image object, and one which plots the image. This makes it much easier to spot mistakes like the misplacement of the sigma argument.
I have a time series that I'd like to plot using the polygon function as I want to create a shade between different time series. However, when calling polygon (), the function adds a line between the first and last point (in essence it connects the first and last point to finish the plot). I would like to know how to tell R not to join up the two. Slightly related questions have been posted (Line connecting the points in the plot function in R) but the solutions didn't help. Any help would be appreciated.
I have already tried several things, such as reordering the data like in the part below.
% ts_lb_vec is my time-series in vector format;
% x is a vector of time (2000 to 2015);
% I first call plot which plots x (time) with y (the time-series). This works fine;
plot(x, ts_lb_vec,type='n',ylim=c(-300,300), ylab="", xlab="")
But if I want to use the polygon function to use the shading capabilities, it draws the line and I have tried reordering the data (as below) to try to eliminate the problem but this is unsuccessful
polygon(x[order(x),ts_lb_vec[order(x)], xlim=range(x), ylim=range(ts_lb_vec))
I would just like R when calling the polygon function to not connect my first and last point (see image). The figure attached bellow was produced using the following code:
plot(x, ts_lb_vec,type='n', ylab="", xlab="")
polygon(x, ts_lb_vec)
Just to clarify, what I would like is for the space between two time series to be filled, hence why I need the function polygon. See image below
I put together a solution using ggplot2.
The key step is drawing a separate polygon where the order of one of the curves is inverted to avoid the crossing over back to the start.
# simple example data
examp.df <- data.frame(time = seq_len(15), a = c(1,2,3,4,5,5,5,4,3,2,4,5,6,7,8), b = c(2,4,5,6,7,8,7,6,6,5,6,4,3,2,1))
# the polygon is generated by inverting the curve b
polygon <- data.frame(time <- c(examp.df$time, rev(examp.df$time)), y.pos = c(examp.df$a, rev(examp.df$b)))
ggplot(examp.df) +
geom_polygon(data = polygon, aes(x = time, y = y.pos), fill = "blue", alpha = 0.25) +
geom_line(aes(x= time, y = a), size = 1, color = "red") +
geom_line(aes(x = time, y = b), size = 1, color = "green") +
theme_classic()
Which results in:
If you want to know more about ggplot2 this is a good introduction.
I've run a 2d simulation in some modelling software from which i've got an export of x,y point locations with a set of 6 attributes. I wish to recreate a figure that combines the data, like this:
The ellipses and the background are shaded according to attribute 1 (and the borders of these are of course representing the model geometry, but I don't think I can replicate that), the isolines are contours of attribute 2, and the arrow glyphs are from attributes 3 (x magnitude) and 4 (y magnitude).
The x,y points are centres of the triangulated mesh I think, and look like this:
I want to know how I can recreate a plot like this with R. To start with I have irregularly-spaced data due to it being exported from an irregular mesh. That's immediately where I get stuck with R, having only ever used it for producing box-and-whisper plots and the like.
Here's the data:
https://dl.dropbox.com/u/22417033/Ellipses_noheader.txt
Edit: fields: x, y, heat flux (x), heat flux (y), thermal conductivity, Temperature, gradT (x), gradT (y).
names(Ellipses) <- c('x','y','dfluxx','dfluxy','kxx','Temps','gradTx','gradTy')
It's quite easy to make the lower plot (making the assumption that there is a dataframe named 'edat' read in with:
edat <- read.table(file=file.choose())
with(edat, plot(V1,V2), cex=0.2)
Things get a bit more beautiful with:
with(edat, plot(V1,V2, cex=0.2, col=V5))
So I do not think your original is being faithfully represented by the data. The contour lines are NOT straight across the "conductors". I call them "conductors" because this looks somewhat like iso-potential lines in electrostatics. I'm adding some text here to serve as a search handle for others who might be searching for plotting problems in real world physics: vector-field (the arrows) , heat equations, gradient, potential lines.
You can then overlay the vector field with:
with(edat, arrows(V1,V2, V1-20*V6*V7, V2-20*V6*V8, length=0.04, col="orange") )
You could"zoom in" with xlim and ylim:
with(edat, plot(V1,V2, cex=0.3, col=V5, xlim=c(0, 10000), ylim=c(-8000, -2000) ))
with(edat, arrows(V1,V2, V1-20*V6*V7, V2-20*V6*V8, length=0.04, col="orange") )
Guessing that the contour requested if for the Temps variable. Take your pick of contourplots.
require(akima)
intflow<- with(edat, interp(x=x, y=y, z=Temps, xo=seq(min(x), max(x), length = 410),
yo=seq(min(y), max(y), length = 410), duplicate="mean", linear=FALSE) )
require(lattice)
contourplot(intflow$z)
filled.contour(intflow)
with( intflow, contour(x=x, y=y, z=z) )
The last one will mix with the other plotting examples since those were using base plotting functions. You may need to switch to points instead of plot.
There are several parts to your plot so you will probably need several tools to make the different parts.
The background and ellipses can be created with polygon (once you figure where they should be).
The contourLines function can calculate the contour lines for you which you can add with the lines function (or contour has and add argument and could probably be used to add the lines directly).
The akima package has a function interp which can estimate values on a grid given the values ungridded.
The my.symbols function along with ms.arrows, both from the TeachingDemos package, can be used to draw the vector field.
#DWin is right to say that your graph don't represent faithfully your data, so I would advice to follow his answer. However here is how to reproduce (the closest I could) your graph:
Ellipses <- read.table(file.choose())
names(Ellipses) <- c('x','y','dfluxx','dfluxy','kxx','Temps','gradTx','gradTy')
require(splancs)
require(akima)
First preparing the data:
#First the background layer (the 'kxx' layer):
# Here the regular grid on which we're gonna do the interpolation
E.grid <- with(Ellipses,
expand.grid(seq(min(x),max(x),length=200),
seq(min(y),max(y),length=200)))
names(E.grid) <- c("x","y") # Without this step, function inout throws an error
E.grid$Value <- rep(0,nrow(E.grid))
#Split the dataset according to unique values of kxx
E.k <- split(Ellipses,Ellipses$kxx)
# Find the convex hull delimiting each of those values domain
E.k.ch <- lapply(E.k,function(X){X[chull(X$x,X$y),]})
for(i in unique(Ellipses$kxx)){ # Pick the value for each coordinate in our regular grid
E.grid$Value[inout(E.grid[,1:2],E.k.ch[names(E.k.ch)==i][[1]],bound=TRUE)]<-i
}
# Then the regular grid for the second layer (Temp)
T.grid <- with(Ellipses,
interp(x,y,Temps, xo=seq(min(x),max(x),length=200),
yo=seq(min(y),max(y),length=200),
duplicate="mean", linear=FALSE))
# The regular grids for the arrow layer (gradT)
dx <- with(Ellipses,
interp(x,y,gradTx,xo=seq(min(x),max(x),length=15),
yo=seq(min(y),max(y),length=10),
duplicate="mean", linear=FALSE))
dy <- with(Ellipses,
interp(x,y,gradTy,xo=seq(min(x),max(x),length=15),
yo=seq(min(y),max(y),length=10),
duplicate="mean", linear=FALSE))
T.grid2 <- with(Ellipses,
interp(x,y,Temps, xo=seq(min(x),max(x),length=15),
yo=seq(min(y),max(y),length=10),
duplicate="mean", linear=FALSE))
gradTgrid<-expand.grid(dx$x,dx$y)
And then the plotting:
palette(grey(seq(0.5,0.9,length=5)))
par(mar=rep(0,4))
plot(E.grid$x, E.grid$y, col=E.grid$Value,
axes=F, xaxs="i", yaxs="i", pch=19)
contour(T.grid, add=TRUE, col=colorRampPalette(c("blue","red"))(15), drawlabels=FALSE)
arrows(gradTgrid[,1], gradTgrid[,2], # Here I multiply the values so you can see them
gradTgrid[,1]-dx$z*40*T.grid2$z, gradTgrid[,2]-dy$z*40*T.grid2$z,
col="yellow", length=0.05)
To understand in details how this code works, I advise you to read the following help pages: ?inout, ?chull, ?interp, ?expand.grid and ?contour.
I have a plot that has data that runs into the area I'd like to use for a legend. Is there a way to have the plot automatically put in something like a header space above the highest data points to fit the legend into?
I can get it to work if I manually enter the ylim() arguments to expand the size and then give the exact coordinates of where I want the legend located, but I'd prefer to have a more flexible means of doing this as it's a front end for a data base query and the data levels could have very different levels.
Edit 2017:
use ggplot and theme(legend.position = ""):
library(ggplot2)
library(reshape2)
set.seed(121)
a=sample(1:100,5)
b=sample(1:100,5)
c=sample(1:100,5)
df = data.frame(number = 1:5,a,b,c)
df_long <- melt(df,id.vars = "number")
ggplot(data=df_long,aes(x = number,y=value, colour=variable)) +geom_line() +
theme(legend.position="bottom")
Original answer 2012:
Put the legend on the bottom:
set.seed(121)
a=sample(1:100,5)
b=sample(1:100,5)
c=sample(1:100,5)
dev.off()
layout(rbind(1,2), heights=c(7,1)) # put legend on bottom 1/8th of the chart
plot(a,type='l',ylim=c(min(c(a,b,c)),max(c(a,b,c))))
lines(b,lty=2)
lines(c,lty=3,col='blue')
# setup for no margins on the legend
par(mar=c(0, 0, 0, 0))
# c(bottom, left, top, right)
plot.new()
legend('center','groups',c("A","B","C"), lty = c(1,2,3),
col=c('black','black','blue'),ncol=3,bty ="n")
You have to add the size of the legend box to the ylim range
#Plot an empty graph and legend to get the size of the legend
x <-1:10
y <-11:20
plot(x,y,type="n", xaxt="n", yaxt="n")
my.legend.size <-legend("topright",c("Series1","Series2","Series3"),plot = FALSE)
#custom ylim. Add the height of legend to upper bound of the range
my.range <- range(y)
my.range[2] <- 1.04*(my.range[2]+my.legend.size$rect$h)
#draw the plot with custom ylim
plot(x,y,ylim=my.range, type="l")
my.legend.size <-legend("topright",c("Series1","Series2","Series3"))
Building on #P-Lapointe solution, but making it extremely easy, you could use the maximum values from your data using max() and then you re-use those maximum values to set the legend xy coordinates. To make sure you don't get beyond the borders, you set up ylim slightly over the maximum values.
a=c(rnorm(1000))
b=c(rnorm(1000))
par(mfrow=c(1,2))
plot(a,ylim=c(0,max(a)+1))
legend(x=max(a)+0.5,legend="a",pch=1)
plot(a,b,ylim=c(0,max(b)+1),pch=2)
legend(x=max(b)-1.5,y=max(b)+1,legend="b",pch=2)
?legend will tell you:
Arguments
x, y
the x and y co-ordinates to be used to position the legend. They can be specified by keyword or in any way which is accepted by xy.coords: See ‘Details’.
Details:
Arguments x, y, legend are interpreted in a non-standard way to allow the coordinates to be specified via one or two arguments. If legend is missing and y is not numeric, it is assumed that the second argument is intended to be legend and that the first argument specifies the coordinates.
The coordinates can be specified in any way which is accepted by xy.coords. If this gives the coordinates of one point, it is used as the top-left coordinate of the rectangle containing the legend. If it gives the coordinates of two points, these specify opposite corners of the rectangle (either pair of corners, in any order).
The location may also be specified by setting x to a single keyword from the list bottomright, bottom, bottomleft, left, topleft, top, topright, right and center. This places the legend on the inside of the plot frame at the given location. Partial argument matching is used. The optional inset argument specifies how far the legend is inset from the plot margins. If a single value is given, it is used for both margins; if two values are given, the first is used for x- distance, the second for y-distance.