I am trying to plot 18 individual plots on a 3x6 multiplot in R. To be more efficient I have created these plots as a loop, however I would like the plots in each column to have their own color (i.e. the all the plots in column 1 would be red, all the plots in column 2 would be blue etc.). Is there a way I can do this while still retaining loop format?
par(mfcol = c(3,6))
for(i in 1:6)
{
plot(sigma_trace[,i], type ='l', main = paste("Sigma Traceplot Chain", i))
plot(theta_1_trace[,i], type = 'l', main = paste("Theta[1] Traceplot Chain", i))
plot(theta_2_trace[,i], type = 'l', main = paste("Theta[2] Traceplot Chain", i))
}
So basically, I think I want each loop statement to follow the same pattern of colours. Is this possible?
Thanks.
You can make a colour palette using RColorBrewer and then call each colour in your loop. For example.
library(RColorBrewer)
# set the colour palette
cols <- brewer.pal(4,'Set2')
# variables to plot
x = (1:250)/10
y = cos(x)
# plot in the loop
op <- par(mfrow = c(2, 2))
for (i in 1:4){
plot(x, y, col=cols[i], type='l', lwd=3)
}
par(op)
Here's an overview of the package.
In Base R you can use colorRampPalette() to create gradient, or you can even just make an object with the colours that you wan to reference:
plotcolors <- colorRampPalette(c("gold","blue"))(6)
par(mfrow = c(2, 3))
for(i in 1:6){
plot(1:10,1:10,type='l',col=plotcolors[i])
}
If you want to specify all 6 of your colours its as easy as modifying the above code
plotcolors <- c("red","blue","green","black","yellow","purple")
I want to create a function, that result will be a plot of moniomals ( degree less than "n").
I wrote the simple code.
Monomial=function(m){
x=1:100
y=1:100
for(i in m) x2=x^m
plot(y,x2,type="l",col="red",xlab="Arguments",ylab="Values",
main=expression("Monomials"))
But for example: Monomial(3) I getting plot x^3. I need yet x^1 and x^2. How to name each line?
Here is what you need:
Monomial <- function(m){
x <- 1:100
cols <- palette(rainbow(m))
plot(x,x,type="l",col = cols[1],xlab="Arguments",ylab="Values",
main=expression("Monomials"))
for (d in 2:m){
lines(x, x^d, type="l", col=cols[d])
}
legend(90, 60, legend=c(as.character(paste0("x",1:m))),
col=cols, lty=1, cex=0.6)
}
You need to generate colors. This is what the cols variable achieves. lines adds a new curve to existing axes. Finally, ledend adds a legend to the plot.
When using matplot to plot a matrix using:
matplot(t, X[,1:4], col=1:4, lty = 1, xlab="Time", ylab="Stock Value")
my graph comes out as:
How do I reduce the line thickness? I previously used a different method and my graph was fine:
I have tried manupilating lwd but to no avail.
Even tried plot(t, X[1:4097,1]), yet the line being printed is very thick. Something wrong with my R?
EDIT: Here is the code I used to produce the matrix X:
####Inputs mean return, volatility, time period and time step
mu=0.25; sigma=2; T=1; n=2^(12); X0=5;
#############Generating trajectories for stocks
##NOTE: Seed is fixed. Changing seed will produce
##different trajectories
dt=T/n
t=seq(0,T,by=dt)
set.seed(201)
X <- matrix(nrow = n+1, ncol = 4)
for(i in 1:4){
X[,i] <- c(X0,mu*dt+sigma*sqrt(dt)*rnorm(n,mean=0,sd=1))
X[,i] <- cumsum(X[,i])
}
colnames(X) <- paste0("Stock", seq_len(ncol(X)))
Just needed to add type = "l" to matplot(....). Plots fine now.
matplot(t, X[,1:4], col=1:4, type = "l", xlab="Time", ylab="Stock Value")
I need to use black and white color for my boxplots in R. I would like to colorfill the boxplot with lines and dots. For an example:
I imagine ggplot2 could do that but I can't find any way to do it.
Thank you in advance for your help!
I thought this was a great question and pondered if it was possible to do this in base R and to obtain the checkered look. So I put together some code that relies on boxplot.stats and polygon (which can draw angled lines). Here's the solution, which is really not ready for primetime, but is a solution that could be tinkered with to make more general.
boxpattern <-
function(y, xcenter, boxwidth, angle=NULL, angle.density=10, ...) {
# draw an individual box
bstats <- boxplot.stats(y)
bxmin <- bstats$stats[1]
bxq2 <- bstats$stats[2]
bxmedian <- bstats$stats[3]
bxq4 <- bstats$stats[4]
bxmax <- bstats$stats[5]
bleft <- xcenter-(boxwidth/2)
bright <- xcenter+(boxwidth/2)
# boxplot
polygon(c(bleft,bright,bright,bleft,bleft),
c(bxq2,bxq2,bxq4,bxq4,bxq2), angle=angle[1], density=angle.density)
polygon(c(bleft,bright,bright,bleft,bleft),
c(bxq2,bxq2,bxq4,bxq4,bxq2), angle=angle[2], density=angle.density)
# lines
segments(bleft,bxmedian,bright,bxmedian,lwd=3) # median
segments(bleft,bxmin,bright,bxmin,lwd=1) # min
segments(xcenter,bxmin,xcenter,bxq2,lwd=1)
segments(bleft,bxmax,bright,bxmax,lwd=1) # max
segments(xcenter,bxq4,xcenter,bxmax,lwd=1)
# outliers
if(length(bstats$out)>0){
for(i in 1:length(bstats$out))
points(xcenter,bstats$out[i])
}
}
drawboxplots <- function(y, x, boxwidth=1, angle=NULL, ...){
# figure out all the boxes and start the plot
groups <- split(y,as.factor(x))
len <- length(groups)
bxylim <- c((min(y)-0.04*abs(min(y))),(max(y)+0.04*max(y)))
xcenters <- seq(1,max(2,(len*(1.4))),length.out=len)
if(is.null(angle)){
angle <- seq(-90,75,length.out=len)
angle <- lapply(angle,function(x) c(x,x))
}
else if(!length(angle)==len)
stop("angle must be a vector or list of two-element vectors")
else if(!is.list(angle))
angle <- lapply(angle,function(x) c(x,x))
# draw plot area
plot(0, xlim=c(.97*(min(xcenters)-1), 1.04*(max(xcenters)+1)),
ylim=bxylim,
xlab="", xaxt="n",
ylab=names(y),
col="white", las=1)
axis(1, at=xcenters, labels=names(groups))
# draw boxplots
plots <- mapply(boxpattern, y=groups, xcenter=xcenters,
boxwidth=boxwidth, angle=angle, ...)
}
Some examples in action:
mydat <- data.frame(y=c(rnorm(200,1,4),rnorm(200,2,2)),
x=sort(rep(1:2,200)))
drawboxplots(mydat$y, mydat$x)
mydat <- data.frame(y=c(rnorm(200,1,4),rnorm(200,2,2),
rnorm(200,3,3),rnorm(400,-2,8)),
x=sort(rep(1:5,200)))
drawboxplots(mydat$y, mydat$x)
drawboxplots(mydat$y, mydat$x, boxwidth=.5, angle.density=30)
drawboxplots(mydat$y, mydat$x, # specify list of two-element angle parameters
angle=list(c(0,0),c(90,90),c(45,45),c(45,-45),c(0,90)))
EDIT: I wanted to add that one could also obtain dots as a fill by basically drawing a pattern of dots, then covering them a "donut"-shaped polygon, like so:
x <- rep(1:10,10)
y <- sort(x)
plot(y~x, xlim=c(0,11), ylim=c(0,11), pch=20)
outerbox.x <- c(2.5,0.5,10.5,10.5,0.5,0.5,2.5,7.5,7.5,2.5)
outerbox.y <- c(2.5,0.5,0.5,10.5,10.5,0.5,2.5,2.5,7.5,7.5)
polygon(outerbox.x,outerbox.y, col="white", border="white") # donut
polygon(c(2.5,2.5,7.5,7.5,2.5),c(2.5,2.5,2.5,7.5,7.5)) # inner box
But mixing that with angled lines in a single plotting function would be a bit difficult, and is generally a bit more challenging, but it starts to get you there.
I think it is hard to do this with ggplot2 since it dont use shading polygon(gris limitatipn). But you can use shading line feature in base plot, paramtered by density and angle arguments in some plot functions ( ploygon, barplot,..).
The problem that boxplot don't use this feature. So I hack it , or rather I hack bxp internally used by boxplot. The hack consist in adding 2 arguments (angle and density) to bxp function and add them internally in the call of xypolygon function ( This occurs in 2 lines).
my.bxp <- function (all.bxp.argument,angle,density, ...) {
.....#### bxp code
xypolygon(xx, yy, lty = boxlty[i], lwd = boxlwd[i],
border = boxcol[i],angle[i],density[i])
.......## bxp code after
xypolygon(xx, yy, lty = "blank", col = boxfill[i],angle[i],density[i])
......
}
Here an example. It should be noted that it is entirely the responsibility of the user to ensure
that the legend corresponds to the plot. So I add some code to rearrange the legend an the boxplot code.
require(stats)
set.seed(753)
(bx.p <- boxplot(split(rt(100, 4), gl(5, 20))))
layout(matrix(c(1,2),nrow=1),
width=c(4,1))
angles=c(60,30,40,50,60)
densities=c(50,30,40,50,30)
par(mar=c(5,4,4,0)) #Get rid of the margin on the right side
my.bxp(bx.p,angle=angles,density=densities)
par(mar=c(5,0,4,2)) #No margin on the left side
plot(c(0,1),type="n", axes=F, xlab="", ylab="")
legend("top", paste("region", 1:5),
angle=angles,density=densities)
I'd like to superpose a histogram and an xyplot representing the cumulative distribution function using r's lattice package.
I've tried to accomplish this with custom panel functions, but can't seem to get it right--I'm getting hung up on one plot being univariate and one being bivariate I think.
Here's an example with the two plots I want stacked vertically:
set.seed(1)
x <- rnorm(100, 0, 1)
discrete.cdf <- function(x, decreasing=FALSE){
x <- x[order(x,decreasing=FALSE)]
result <- data.frame(rank=1:length(x),x=x)
result$cdf <- result$rank/nrow(result)
return(result)
}
my.df <- discrete.cdf(x)
chart.hist <- histogram(~x, data=my.df, xlab="")
chart.cdf <- xyplot(100*cdf~x, data=my.df, type="s",
ylab="Cumulative Percent of Total")
graphics.off()
trellis.device(width = 6, height = 8)
print(chart.hist, split = c(1,1,1,2), more = TRUE)
print(chart.cdf, split = c(1,2,1,2))
I'd like these superposed in the same frame, rather than stacked.
The following code doesn't work, nor do any of the simple variations of it that I have tried:
xyplot(cdf~x,data=cdf,
panel=function(...){
panel.xyplot(...)
panel.histogram(~x)
})
You were on the right track with your custom panel function. The trick is passing the correct arguments to the panel.- functions. For panel.histogram, this means not passing a formula and supplying an appropriate value to the breaks argument:
EDIT Proper percent values on y-axis and type of plots
xyplot(100*cdf~x,data=my.df,
panel=function(...){
panel.histogram(..., breaks = do.breaks(range(x), nint = 8),
type = "percent")
panel.xyplot(..., type = "s")
})
This answer is just a placeholder until a better answer comes.
The hist() function from the graphics package has an option called add. The following does what you want in the "classical" way:
plot( my.df$x, my.df$cdf * 100, type= "l" )
hist( my.df$x, add= T )