I have a parameterized contour that I'm plotting in R. What I'm trying to do is add arrows along the curve to show the viewer which direction the curve is going in.
Here's the code I'm using to generate the curve:
library(ggplot2)
library(grid)
set.seed(9)
T<-sort(runif(2^12,min=2^-5, max=16))
U<-function(t) exp(4*log(t) - 4*t)*(cos(log(t) + 3*t))
#Re(t^(4+1i)*t)*exp(-(4-3i)*t))
V<-function(t) exp(4*log(t) - 4*t)*(sin(log(t) + 3*t))
#Im(t^(4+1i)*t)*exp(-(4-3i)*t))
X<-sapply(T,U)
Y<-sapply(T,V)
df<-data.frame(X=X,Y=Y)
p<-ggplot(data=df,aes(x = df$X, y = df$Y))
p+theme_bw()+
geom_path(size=1,color='blue',linetype=1) #+
#geom_segment(aes(xend=c(tail(X, n=-1), NA), yend=c(tail(Y, n=-1), NA)),
#arrow=arrow(length=unit(0.2,"cm")),color='blue')
dev.off()
The last part I commented out:
#+
#geom_segment(aes(xend=c(tail(X, n=-1), NA), yend=c(tail(Y, n=-1), NA)),
#arrow=arrow(length=unit(0.2,"cm")),color='blue')
does something similar to what I want, but the arrows are very close together and the curve ends up looking "fuzzy" rather than directed.
Here's the fuzzy and non-fuzzy version of the curve:
Thank you!
It might look better if the arrows were more equally spaced along the curved path, e.g.
library(ggplot2)
library(grid)
set.seed(9)
T <- sort(runif(2^12,min=2^-5, max=16))
U <- function(t) exp(4*log(t) - 4*t)*(cos(log(t) + 3*t))
V <- function(t) exp(4*log(t) - 4*t)*(sin(log(t) + 3*t))
drough <- data.frame(x=sapply(T,U), y=sapply(T,V))
p <- ggplot(data = drough, aes(x = x, y = y)) +
geom_path()
## because the parametric curve was generated with uneven spacing
## we can try to resample more evenly along the path
parametric_smoothie <- function(x, y, N=1e2, phase=1, offset=0) {
lengths <- c(0, sqrt(diff(x)^2 + diff(y)^2))
l <- cumsum(lengths)
lmax <- max(l)
newpos <- seq(phase*lmax/N, lmax-phase*lmax/N, length.out = N) + offset*lmax/N
xx <- approx(l, x, newpos)$y
yy <- approx(l, y, newpos)$y
data.frame(x = xx, y = yy)
}
## this is a finer set of points
dfine <- parametric_smoothie(X, Y, 20)
gridExtra::grid.arrange(p + geom_point(data = drough, col="grey"),
p + geom_point(data = dfine, col="grey"), ncol=2)
## now we use this function to create N start points for the arrows
## and another N end points slightly apart to give a sense of direction
relay_arrow <- function(x, y, N=10, phase = 0.8, offset = 1e-2, ...){
start <- parametric_smoothie(x, y, N, phase)
end <- parametric_smoothie(x, y, N, phase, offset)
data.frame(xstart = start$x, xend = end$x,
ystart = start$y, yend = end$y)
}
breaks <- relay_arrow(drough$x, drough$y, N=20)
p + geom_point(data = breaks, aes(xstart, ystart), col="grey98", size=2) +
geom_segment(data = breaks, aes(xstart, ystart, xend = xend, yend = yend),
arrow = arrow(length = unit(0.5, "line")),
col="red", lwd=1)
One way to do it is to draw them on after. You can probably get the direction better by using the angle aesthetic (if it's easy enough to work out):
p<-ggplot(data=df,aes(x = X, y = Y))
p +
geom_path(size=1,color='blue',linetype=1)+
geom_segment(data = df[seq(1, nrow(df), 20), ], aes(x = X, y = Y, xend=c(tail(X, n=-1), NA), yend=c(tail(Y, n=-1), NA)),
arrow=arrow(length=unit(0.2,"cm"), type = "closed"), color="blue", linetype = 0, inherit.aes = FALSE)
Note the closed arrow type. I had to do that so they weren't interpreted as lines and hence disappear when linetype = 0.
Try this with slight modification of your code (you don't want to compromise the quality of the curve by having smaller number of points and at the same time you want to have smaller number of segments to draw the arrows for better quality of the arrows):
library(ggplot2)
library(grid)
set.seed(9)
T<-sort(runif(2^12,min=2^-5, max=16))
U<-function(t) exp(4*log(t) - 4*t)*(cos(log(t) + 3*t))
#Re(t^(4+1i)*t)*exp(-(4-3i)*t))
V<-function(t) exp(4*log(t) - 4*t)*(sin(log(t) + 3*t))
#Im(t^(4+1i)*t)*exp(-(4-3i)*t))
X<-sapply(T,U)
Y<-sapply(T,V)
df<-data.frame(X=X,Y=Y)
df1 <- df[seq(1,length(X), 8),]
p<-ggplot(data=df,aes(x = df$X, y = df$Y))
p+theme_bw()+
geom_path(size=1,color='blue',linetype=1) +
geom_segment(data=df1,aes(x=X, y=Y, xend=c(tail(X, n=-1), NA), yend=c(tail(Y, n=-1), NA)),
arrow=arrow(length=unit(0.3,"cm"),type='closed'),color='blue')
#dev.off()
Related
I have a graph of vertices and edges which I'd like to plot using a fruchtermanreingold layout.
Here's the graph edges matrix:
edge.mat <- matrix(as.numeric(strsplit("3651,0,0,1,0,0,0,0,2,0,11,2,0,0,0,300,0,1,0,0,66,0,78,9,0,0,0,0,0,0,11690,0,1,0,0,0,0,0,0,0,0,493,1,1,0,4288,5,0,0,36,0,9,7,3,0,6,1,0,1,7,490,0,0,0,6,0,0,628,6,12,0,0,0,0,0,641,0,0,4,0,0,0,0,0,0,66,0,0,0,0,3165,0,281,0,0,0,0,0,0,0,0,45,1,0,0,35248,0,1698,2,0,1,0,2,99,0,0,6,29,286,0,31987,0,1,10,0,8,0,16,0,21,1,0,0,1718,0,51234,0,0,17,3,12,0,0,7,0,0,0,1,0,2,16736,0,0,0,3,0,0,4,630,0,0,0,9,0,0,29495,53,6,0,0,0,0,5,0,0,0,0,3,0,19,186,0,0,0,482,8,12,0,1,0,7,1,0,6,0,26338",
split = ",")[[1]]),
nrow = 14,
dimnames = list(LETTERS[1:14], LETTERS[1:14]))
I then create an igraph object from that using:
gr <- igraph::graph_from_adjacency_matrix(edge.mat, mode="undirected", weighted=T, diag=F)
And then use ggnetwork to convert gr to a data.frame, with specified vertex colors:
set.seed(1)
gr.df <- ggnetwork::ggnetwork(gr,
layout="fruchtermanreingold",
weights="weight",
niter=50000,
arrow.gap=0)
And then I plot it using ggplot2 and ggnetwork:
vertex.colors <- strsplit("#00BE6B,#DC2D00,#F57962,#EE8044,#A6A400,#62B200,#FF6C91,#F77769,#EA8332,#DA8E00,#C59900,#00ACFC,#C49A00,#DC8D00",
split=",")[[1]]
library(ggplot2)
library(ggnetwork)
ggplot(gr.df, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(color = "gray", aes(size = weight)) +
geom_nodes(color = "black")+
geom_nodelabel(aes(label = vertex.names),
color = vertex.colors, fontface = "bold")+
theme_minimal() +
theme(axis.text=element_blank(),
axis.title=element_blank(),
legend.position="none")
In my case each vertex actually represents many points, where each vertex has a different number of points. Adding that information to gr.df:
gr.df$n <- NA
gr.df$n[which(is.na(gr.df$weight))] <- as.integer(runif(length(which(is.na(gr.df$weight))), 100, 500))
What I'd like to do is add to the plot gr.df$n radially jittered points around each vertex (i.e., with its corresponding n), with the same vertex.colors coding. Any idea how to do that?
I think sampling and then plotting with geom_point is a reasonable strategy. (otherwise you could create your own geom).
Here is some rough code, starting from the relevant bit of your question
gr.df$n <- 1
gr.df$n[which(is.na(gr.df$weight))] <- as.integer(runif(length(which(is.na(gr.df$weight))), 100, 500))
# function to sample
# https://stackoverflow.com/questions/5837572/generate-a-random-point-within-a-circle-uniformly
circSamp <- function(x, y, R=0.1){
n <- length(x)
A <- a <- runif(n,0,1)
b <- runif(n,0,1)
ind <- b < a
a[ind] <- b[ind]
b[ind] <- A[ind]
xn = x+b*R*cos(2*pi*a/b)
yn = y+b*R*sin(2*pi*a/b)
cbind(x=xn, y=yn)
}
# sample
d <- with(gr.df, data.frame(vertex.names=rep(vertex.names, n),
circSamp(rep(x,n), rep(y,n))))
# p is your plot
p + geom_point(data=d, aes(x, y, color = vertex.names),
alpha=0.1, inherit.aes = FALSE) +
scale_color_manual(values = vertex.colors)
Giving
I can not figure out how to use .gif figures as points in x-y plotting, and rotate each point according to data in the dataset.
Demo dataset:
library("ggplot2")
library("ggimage")
N=5
d <- data.frame(x = rnorm(N),
y = rnorm(N),
image = rep("https://www.r-project.org/logo/Rlogo.png", N),
size = seq(.05, 0.15, length.out = N),
angle = seq(0, 45, length.out = N) )
The follwing will make a plot where all Rlogo's are rotated 45 degrees:
ggplot(d, aes(x, y)) +
geom_image(aes(image=image, size=I(size)), angle = 45)
# Plot with tilted png
But how to set the rotation angle individually for each point? The snippet below simply does not work and makes a plot without tilting any points..
ggplot(d, aes(x, y)) +
geom_image(aes(image=image, size=I(size), angle = I(angle)))
#plotting but without tilting the points
Setting the plot angle outside the aes does not help.
ggplot(d, aes(x, y)) + geom_image(aes(image=image, size=I(size)), angle = I(d$angle))
# No plotting: Error in valid.viewport(x, y, width, height, just, gp, clip, xscale, yscale, invalid 'angle' in viewport
So, anyone having a good idea for this?
Thanks in advance:-)
You could try this, although the real solution would be to write a better geom,
library("ggplot2")
library("egg")
library("grid")
N=5
d <- data.frame(x = rnorm(N),
y = rnorm(N),
image = replicate(N, system.file("img", "Rlogo.png", package="png")),
size = seq(.05, 0.15, length.out = N),
angle = seq(0, 45, length.out = N),
stringsAsFactors = FALSE)
grobs <- purrr::map2(d$image, runif(N,0,180),
function(i,a) list(raster = png::readPNG(i), angle=a) )
d$grob <- I(grobs)
custom_grob <- function(data, x=0.5, y=0.5){
grob(data=data$raster,angle=data$angle,x=x,y=y, cl="custom")
}
preDrawDetails.custom <- function(x){
pushViewport(viewport(x=x$x,y=x$y, angle = x$angle))
}
postDrawDetails.custom <- function(x){
upViewport()
}
drawDetails.custom <- function(x, recording=FALSE, ...){
grid.raster(x$data, interpolate = FALSE, width=unit(1,"cm"), height=unit(1,"cm"))
}
ggplot(d, aes(x, y)) +
theme_bw() +
geom_custom(data = d, aes(data = grob),
grob_fun = custom_grob)
enter image description here
I have spent sometimes doing this but I could not reach the solution. I have this code to plot concentric circles over 25 by 25 grids using ggplot2 in R. I do not know how to be able to manipulate the center of the concentric circles to be not at the origin(0,0), but at the center of the grid (5,5). I also would like to keep the scale of the grid from 25 to 25. Thank you very much in advance
require(ggplot2)
require(grid)
x <- rep(seq(25), 25)
y <- rep(seq(25), each=25)
circ_rads <- seq(1,5,2)
qplot(x, y) +
lapply(circ_rads, FUN = function(x)
annotation_custom(circleGrob(gp = gpar(fill = "transparent", color = "black")),
-x, x, -x, x)) +
geom_text(aes(x = 0, y = circ_rads + 0.1, label = circ_rads)) +
coord_fixed(ratio = 1)
We can use ggforce::geom_circle like this:
library(ggplot2)
library(ggforce)
x <- rep(seq(25), 25)
y <- rep(seq(25), each=25)
circ_rads <- seq(1,5,2)
xy <- data.frame(x=x, y=y)
circles <- data.frame(
x0 = 5, # You say circles should be a 'centre of the grid' and 5, 5
y0 = 5, # not sure what you really mean, so going with 5, 5 here
r = circ_rads
)
ggplot() +
geom_point(data = xy,
aes(x,
y)) +
geom_circle(data = circles,
aes(x0 = x0,
y0 = y0,
r = r)) +
coord_fixed()
I want to add shaded areas to a chart to help people understand where bad, ok, and good points can fit.
Good = x*y>=.66
Ok = x*y>=.34
Bad = x*y<.34
Generating the right sequence of data to correctly apply the curved boundaries to the chart is proving tough.
What is the most elegant way to generate the curves?
Bonus Q: How would you do this to produce non-overlapping areas so that different colours could be used?
Updates
I've managed to do in a rather hacky way the drawing of the circle segments. I updated the MRE to use the revised segMaker function.
MRE
library(ggplot2)
pts<-seq(0,1,.02)
x<-sample(pts,50,replace=TRUE)
y<-sample(pts,50,replace=TRUE)
# What function will generate correct sequence of values as these are linear?
segMaker<-function(x,by){
# Original
# data.frame(x=c(seq(0,x,by),0)
# ,y=c(seq(x,0,-by),0)
# )
zero <- data.frame(x = 0, y = 0)
rs <- seq(0, pi, by)
xc <- x * cos(rs)
yc <- x * sin(rs)
gr <- data.frame(x = xc, y = yc)
gr <- rbind(gr[gr$x >= 0, ], zero)
return(gr)
}
firstSeg <-segMaker(.34,0.02)
secondSeg <-segMaker(.66,0.02)
thirdSeg <-segMaker(1,0.02)
ggplot(data.frame(x,y),aes(x,y, colour=x*y))+
geom_point() +
geom_polygon(data=firstSeg, fill="blue", alpha=.25)+
geom_polygon(data=secondSeg, fill="blue", alpha=.25)+
geom_polygon(data=thirdSeg, fill="blue", alpha=.25)
Current & desired shadings
You can create a data frame with the boundaries between each region and then use geom_ribbon to plot it. Here's an example using the conditions you supplied (which result in boundaries that are the reciprocal function, rather than circles, but the idea is the same, whichever function you use for the boundaries):
library(ggplot2)
# Fake data
pts<-seq(0,1,.02)
set.seed(19485)
x<-sample(pts,50,replace=TRUE)
y<-sample(pts,50,replace=TRUE)
df = data.frame(x,y)
# Region boundaries
x = seq(0.001,1.1,0.01)
bounds = data.frame(x, ymin=c(-100/x, 0.34/x, 0.66/x),
ymax=c(0.34/x, 0.66/x, 100/x),
g=rep(c("Bad","OK","Good"), each=length(x)))
bounds$g = factor(bounds$g, levels=c("Bad","OK","Good"))
ggplot() +
coord_cartesian(ylim=0:1, xlim=0:1) +
geom_ribbon(data=bounds, aes(x, ymin=ymin, ymax=ymax, fill=g), colour="grey50", lwd=0.2) +
geom_point(data=df, aes(x,y), colour="grey20") +
scale_fill_manual(values=hcl(c(15, 40, 240), 100, 80)) +
#scale_fill_manual(values=hcl(c(15, 40, 240), 100, 80, alpha=0.25)) + # If you want the fill colors to be transparent
labs(fill="") +
guides(fill=guide_legend(reverse=TRUE))
For circular boundaries, assuming we want boundaries at r=1/3 and r=2/3:
# Calculate y for circle, given r and x
cy = function(r, x) {sqrt(r^2 - x^2)}
n = 200
x = unlist(lapply(c(1/3,2/3,1), function(to) seq(0, to, len=n)))
bounds = data.frame(x, ymin = c(rep(0, n),
cy(1/3, seq(0, 1/3, len=n/2)), rep(0, n/2),
cy(2/3, seq(0, 2/3, len=2*n/3)), rep(0, n/3)),
ymax = c(cy(1/3, seq(0,1/3,len=n)),
cy(2/3, seq(0,2/3,len=n)),
rep(1,n)),
g=rep(c("Bad","OK","Good"), each=n))
bounds$g = factor(bounds$g, levels=c("Bad","OK","Good"))
If you can use a github package, ggforce adds geom_arc_bar():
# devtools::install_github('thomasp85/ggforce')
library(ggplot2)
library(ggforce)
pts<-seq(0,1,.02)
x<-sample(pts,50,replace=TRUE)
y<-sample(pts,50,replace=TRUE)
arcs <- data.frame(
x0 = 0,
y0 = 0,
start = 0,
end = pi / 2,
r0 = c(0, 1/3, 2/3),
r = c(1/3, 2/3, 1),
fill = c("bad", "ok", "good")
)
ggplot() +
geom_arc_bar(data = arcs,
aes(x0 = x0, y0 = y0, start = start, end = end, r0 = r0, r = r,
fill = fill), alpha = 0.6) +
geom_point(data = data.frame(x = x, y = y),
aes(x = x, y = y))
Based on #eipi10's great answer, to do the product component (basically ends up with the same thing) I did:
library(ggplot2)
library(data.table)
set.seed(19485)
pts <- seq(0, 1, .001)
x <- sample(pts, 50, replace = TRUE)
y <- sample(pts, 50, replace = TRUE)
df <- data.frame(x,y)
myRibbon<-CJ(pts,pts)
myRibbon[,prod:=V1 * V2]
myRibbon[,cat:=ifelse(prod<=1/3,"bad",
ifelse(prod<=2/3,"ok","good"))]
myRibbon<-myRibbon[
,.(ymin=min(V2),ymax=max(V2))
,.(cat,V1)]
ggplot() +
geom_ribbon(data=myRibbon
, aes(x=V1, ymin=ymin,ymax=ymax
, group=cat, fill=cat),
colour="grey90", lwd=0.2, alpha=.5)+
geom_point(data=df, aes(x,y), colour="grey20") +
theme_minimal()
This doesn't do anything fancy but works out for each value of x, what the smallest and largest values were that could give rise to a specific banding.
If I had just wanted arcs, the use of ggforce (#GregF) would be really great- it tucks away all the complexity.
The polygon function in R seems rather simple...however I can't get it to work.
It easily works with this code:
x <- seq(-3,3,0.01)
y1 <- dnorm(x,0,1)
y2 <- 0.5*dnorm(x,0,1)
plot(x,y1,type="l",bty="L",xlab="X",ylab="dnorm(X)")
points(x,y2,type="l",col="red")
polygon(c(x,rev(x)),c(y2,rev(y1)),col="skyblue")
When adopting this to something else, it doesn't work. Here some stuff to reproduce the issue:
lowerbound = c(0.05522914,0.06567045,0.07429926,0.08108482,0.08624472,0.09008050,0.09288837,0.09492226)
upperbound = c(0.1743657,0.1494058,0.1333106,0.1227383,0.1156714,0.1108787,0.1075915,0.1053178)
lim = c(100,200,400,800,1600,3200,6400,12800)
plot(upperbound, ylim=c(0, 0.2), type="b", axes=FALSE)
lines(lowerbound, type="b", col="red")
atvalues <- seq(1:8)
axis(side=1, at=atvalues, labels=lim)
axis(side=2, at=c(0,0.05,0.1,0.15,0.2), labels=c(0,0.05,0.1,0.15,0.2))
polygon(lowerbound,upperbound, col="skyblue")
It also doesn't work when only segmenting a subset when directly calling the coordinates:
xpoly <- c(100,200,200,100)
ypoly <- c(lowerbound[1], lowerbound[2], upperbound[2], upperbound[1])
polygon(xpoly,ypoly, col="skyblue")
What am I missing?
Plotting the whole polygon
You need to supply both x and y to polygon. Normally, you'd also do that for plot, but if you don't it will just use the Index as x, that is integers 1 to n. We can use that to make an x range. seq_along will create a 1:n vector, where n is the length of another object.
x <- c(seq_along(upperbound), rev(seq_along(lowerbound)))
y <- c(lowerbound, rev(upperbound))
plot(upperbound, ylim=c(0, 0.2), type="b", axes=FALSE)
lines(lowerbound, type="b", col="red")
atvalues <- seq(1:8)
axis(side=1, at=atvalues, labels=lim)
axis(side=2, at=c(0,0.05,0.1,0.15,0.2), labels=c(0,0.05,0.1,0.15,0.2))
polygon(x = x, y = y, col="skyblue")
Plotting a subset
For a subset, I would create the y first, and then use the old x to easily get `x values:
y2 <- c(lowerbound[1:2], upperbound[2:1])
x2 <- x[which(y2 == y)]
polygon(x2, y2, col="skyblue")
How I would do it
Creating something like this is much easier in ggplot2, where geom_ribbon does a lot of the heavy lifting. We just have to make an actual data.frame, an stop relying on indices.
Full polygon:
library(ggplot2)
ggplot(d, aes(x = x, ymin = low, ymax = up)) +
geom_ribbon(fill = 'skyblue', alpha = 0.5) +
geom_line(aes(y = low), col = 'red') +
geom_line(aes(y = up), col = 'black') +
scale_x_continuous(trans = 'log2') +
theme_bw()
Subset:
ggplot(d, aes(x = x, ymin = low, ymax = up)) +
geom_ribbon(data = d[1:2, ], fill = 'skyblue', alpha = 0.5) +
geom_line(aes(y = low), col = 'red') +
geom_line(aes(y = up), col = 'black') +
scale_x_continuous(trans = 'log2') +
theme_bw()