I have two graphs that I plotted in R and I want to export it as a high-resolution picture for publication.
For example:
a<-c(1,2,3,4,5,6,7)
b<-c(2,3,4,6,7,8,9)
par(mfrow=c(2,1))
plot (a,b)
plot(a,b)
I usually export this graph by:
dev.copy(jpeg,'test.jpeg',width=80,height=150,units="mm",res=200)
dev.off()
However I always find this process a bit troublesome. The graph that was plotted in R does not necessarily look like the one that I exported. Therefore, I am wondering if there is a way to specifiy the dimensions and resolution of graphs before I plot them so that I can visually inspect the graphs before I export them?
Thank you
You can try:
png('out.png')
a<-c(1,2,3,4,5,6,7)
b<-c(2,3,4,6,7,8,9)
par(mfrow=c(2,1))
plot (a,b)
plot(a,b)
dev.off()
As baptiste said, jpeg is the worst format you can choose. You should take a look at the help for the bmp and png functions (with ?bmp and ?png). Both bmp and png have height, width, and res arguments that you can use to specifiy the dimensions and resolution of the output. Also, I wouldn't recommend the use of dev.copy. As you could see, the result of the output is not always what you expect.
To add to Bonifacio2's answer, you if you call the function first to make the plot, you can also define your margins and window size etc before doing any actual plotting. That way you have full control over all fig specs.
pdf(file='test.jpeg',width=80,height=150,units="mm") #I prefer pdf, because they are editable files
a<-c(1,2,3,4,5,6,7)
b<-c(2,3,4,6,7,8,9)
par(mfrow=c(2,1))
plot (a,b)
plot(a,b)
dev.off()
You can use cowplot package to combine multiple panels in several different ways. For example, in your case, we export one plot with two panels arranged in two rows and one column. I assume that you prefer to use base-R 'plot' function instead of ggplot.
library(cowplot)
p1 <- ~{
plot(a,b)
}
p2 <- ~{
plot(b,a)
}
png("plot.png",
width = 3.149606, # 80 mm
height = 5.905512, # 150 mm
units = 'in',
res = 500)
plot_grid(p1, p2, labels = "AUTO", nrow = 2, ncol = 1)
dev.off()
Note that you can either remove the labels if not needed or print small letters by using "auto". Regarding size of the text, axis-labels etc, use the standard arguments for generic plot function of base-R. I hope this answer helps you. Best wishes.
Related
I'm trying to plot half circles using R. My final aim is to draw a circle, divided in the middle by color. The only way I have found yet is to draw two half-circles with different colors.
So I have created my own functions:
upper.half.circle <- function(x,y,r,nsteps=100,...){
rs <- seq(0,pi,len=nsteps)
xc <- x+r*cos(rs)
yc <- y+r*sin(rs)
polygon(xc,yc,...)
}
lower.half.circle <- function(x,y,r,nsteps=100,...){
rs <- seq(0,pi,len=nsteps)
xc <- x-r*cos(rs)
yc <- y-r*sin(rs)
polygon(xc,yc,...)
}
However, for some reason my half-circles end up more like half-ellipses. For example, try running:
plot(1, type="n",axes=F,xlab="", ylab="",xlim=c(0,200),ylim=c(0,200))
upper.half.circle(15,170,10,nsteps=1000,col='red')
Does anyone know why I'm having this trouble, or alternatively, knows of a better way to do what I want?
Thanks!
The problem is the default aspect ratio is not 1:1.
To fix this, set asp=1 in plot:
Inspired by this Q & A. You could have sniffed out this was the case by turning on the axes and x/y labels.
If using the grid package would be also an opportunity for you, there is a much simpler solution:
library(grid)
vp <- viewport(width=0.5, height=0.5, clip = "on")
grid.circle(0.5,0,r=0.5, gp = gpar(fill = 'red'), vp = vp)
This creates a viewport with clipping, i.e., an appropriate positioning of the filled circle creates a half circle.
If you want to add your half circles to an existing plot (and therefore cannot control the aspect ratio directly) then one option for this specific case is to use the floating.pie function from the plotrix package.
A more general tool for creating custom symbols and adding them to plots (with the symbols having a different aspect ratio from the overall plot) is to use the my.symbols function from the TeachingDemos package.
The R package wordcloud has a very useful function which is called wordlayout. It takes initial positions of words and their respective sizes an rearranges them in a way that they do not overlap. I would like to use the results of this functions to do a geom_text plot in ggplot.
I came up with the following example but soon realized that there seems to be a big difference betweetn cex (wordlayout) and size (geom_plot) since words in graphics package appear way larger.
here is my sample code. Plot 1 is the original wordcloud plot which has no overlaps:
library(wordcloud)
library(tm)
library(ggplot2)
samplesize=100
textdf <- data.frame(label=sample(stopwords("en"),samplesize,replace=TRUE),x=sample(c(1:1000),samplesize,replace=TRUE),y=sample(c(1:1000),samplesize,replace=TRUE),size=sample(c(1:5),samplesize,replace=TRUE))
#plot1
plot.new()
pdf(file="plot1.pdf")
textplot(textdf$x,textdf$y,textdf$label,textdf$size)
dev.off()
#plot2
ggplot(textdf,aes(x,y))+geom_text(aes(label = label, size = size))
ggsave("plot2.pdf")
#plot3
new_pos <- wordlayout(x=textdf$x,y=textdf$y,words=textdf$label,cex=textdf$size)
textdf$x <- new_pos[,1]
textdf$y <- new_pos[,2]
ggplot(textdf,aes(x,y))+geom_text(aes(label = label, size = size))
ggsave("plot3.pdf")
#plot4
textdf$x <- new_pos[,1]+0.5*new_pos[,3]#this is the way the wordcloud package rearranges the positions. I took this out of the textplot function
textdf$y <- new_pos[,2]+0.5*new_pos[,4]
ggplot(textdf,aes(x,y))+geom_text(aes(label = label, size = size))
ggsave("plot4.pdf")
is there a way to overcome this cex/size difference and reuse wordlayout for ggplots?
cex stands for character expansion and is the factor by which text is magnified relative the default, specified by cin - set on my installation to 0.15 in by 0.2 in: see ?par for more details.
#hadley explains that ggplot2 sizes are measured in mm. Therefore cex=1 would correspond to size=3.81 or size=5.08 depending on if it is being scaled by the width or height. Of course, font selection may cause differences.
In addition, to use absolute sizes, you need to have the size specification outside the aes otherwise it considers it a variable to map to and choose the scale itself, eg:
ggplot(textdf,aes(x,y))+geom_text(aes(label = label),size = textdf$size*3.81)
Sadly I think you're going to find the short answer is no! I think the package handles the text vector mapping differently from ggplot2, so you can tinker with size and font face/family, etc. but will struggle to replicate exactly what the package is doing.
I tried a few things:
1) Try to plot the grobs from textdata using annotation_custom
require(plyr)
require(grid)
# FIRST TRY PLOT INDIVIDUAL TEXT GROBS
qplot(0:1000,0:1000,geom="blank") +
alply(textdf,1,function(x){
annotation_custom(textGrob(label=x$label,0,0,c("center","center"),gp=gpar(cex=x$size)),x$x,x$x,x$y,x$y)
})
2) Run the wordlayout() function which should readjust the text, but difficult to see for what font (similarly doesn't work)
# THEN USE wordcloud() TO GET CO-ORDS
plot.new()
wordlayout(textdf$x,textdf$y,words=textdf$label,cex=textdf$size,xlim=c(min(textdf$x),max(textdf$x)),ylim=c(min(textdf$y),max(textdf$y)))
plotdata<-cbind(data.frame(rownames(w)),w)
colnames(plotdata)=c("word","x","y","w","h")
# PLOT WORDCLOUD DATA
qplot(0:1000,0:1000,geom="blank") +
alply(plotdata,1,function(x){
annotation_custom(textGrob(label=x$word,0,0,c("center","center"),gp=gpar(cex=x$h*40)),x$x,x$x,x$y,x$y)
})
Here's a cheat if you just want to overplot other ggplot functions on top of it (although the co-ords don't seem to match up exactly between the data and the plot). It basically images the wordcloud, removes the margins, and under-plots it at the same scale:
# make a png file of just the panel
plot.new()
png(filename="bgplot.png")
par(mar=c(0.01,0.01,0.01,0.01))
textplot(textdf$x,textdf$y,textdf$label,textdf$size,xaxt="n",yaxt="n",xlab="",ylab="",asp=1)
dev.off()
# library to get PNG file
require(png)
# then plot it behind the panel
qplot(0:1000,0:1000,geom="blank") +
annotation_custom(rasterGrob(readPNG("bgplot.png"),0,0,1,1,just=c("left","bottom")),0,1000,0,1000) +
coord_fixed(1,c(0,1000),c(0,1000))
I would like to plot two histograms and add a table to a pdf file. With the layout function I managed to plot the histograms (plotted them using hist function) where I want them to be but when I used grid.table function from the gridExtra package to add the table the table is laid out on the histograms and I am not able to position them properly. I have tried addtable2plot function but I dont find it visually appealing.
Any thoughts on How do I get around this?
I want my pdf to look like this
histogram1 histogram2
t a b l e
Essentially, one row with two columns and another row with just one column. This is what I did.
require(gridExtra)
layout(matrix(c(1,2,3,3),2,2,byrow=T),heights=c(1,1))
count_table=table(cut(tab$Longest_OHR,breaks=c(0,0.05,0.10,0.15,0.20,0.25,0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.70,0.75,0.80,0.85,0.90,0.95,1.00)))
ysize=max(count_table)+1000
hist(tab$Longest_OHR,xlab="OHR longest",ylim=c(0,ysize))
count_table=table(cut(tab$Sum_of_OHR.s,breaks=c(0,0.05,0.10,0.15,0.20,0.25,0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.70,0.75,0.80,0.85,0.90,0.95,1.00)))
ysize=max(count_table)+1000
hist(tab$Sum_of_OHR.s,xlab="OHR Sum",ylim=c(0,ysize))
tmp <- table(cut(tab$Length_of_Gene.Protein, breaks = c(0,100,200,500,1000,2000,5000,10000,1000000000)), cut(tab$Sum_of_OHR.s, breaks = (0:10)/10))
grid.table(tmp)
dev.off()
Any help will be appreciated.
Ram
Here's an example of how to combine two base plots and a grid.table in the same figure.
library(gridExtra)
layout(matrix(c(1,0,2,0), 2))
hist(iris$Sepal.Length, col="lightblue")
hist(iris$Sepal.Width, col="lightblue")
pushViewport(viewport(y=.25,height=.5))
grid.table(head(iris), h.even.alpha=1, h.odd.alpha=1,
v.even.alpha=0.5, v.odd.alpha=1)
The coordinates sent to viewport are the center of the panel. Too see exactly where its boundaries are you can call grid.rect().
I am trying to move subplots closer together with R.
What I am doing is basically not important, but just for the sake of fast reproducing, here is the code:
library(igraph)
library(plyr)
g<-graph.ring(10)
setEPS()
postscript( 'out.eps', horizontal=F, onefile=F, paper="special", fonts=c("serif", "Palatino"))
par( mfrow = c( 1, 5 ) )
for (i in 1:5){
plot(g)
title(main='title', cex.main=1.2)
}
dev.off()
and as an output I am getting:
I know that I can organize it in a 2x3 layout, not 1x5, but this is not important.
The thing is, that there is a lot of free space between each subplots, and I would like to place them as tight as possible.
Is there a way to achieve it?
P.S. this question sounds like relevant, but in fact it is not.
You just need to add:
par(mar = c(5,0,4,0))
to adjust the margins of each plot. There is another way of adjusting the margins by setting mai
What I usually do is use the omi ('c(bottom, left, top, right)') and plt ('c(x1, x2, y1, y2)') arguments in par. omi sizes the outer margins in inches and plt gives the coordinates of the plot region as a fraction of the figure region. Use ?par for a more detailed explanation and more arguments.
par(mfrow = c(2,3), omi=c(0.5,0.3,0,0), plt=c(0.1,0.9,0,0.7))
I would like to plot the results from a quantile regression, but am not able to:
control the dimensions/size of the plots and
save the plots as png.
Here is my code:
require(quantreg)
data(engel)
attach(engel)
xx <- income - mean(income)
zz <- c(120, diff(income))
fit1 <- summary(rq(foodexp~xx+zz, tau=2:98/100))
Then:
png('res.png')
plot(fit1, mfrow=c(1,2))
Only the zz plot is saved to the res.png file.. Is there any way I can save the plots in separate files (two and one)?
and how do I control the width/height of the plots? I like all the individual plots to have width=height (square) when i save them to the .png file?
You can control the image dimensions by png argument.
png("image.png", width = 800, height = 600)
plot(...)
dev.off()
To "finish" the image, use dev.off.
For subdividing the plots:
plot(fit1,parm=1:2)
plot(fit1,parm=3)
Note that you could have found the answer by careful reading of ?plot.summary.rqs, but this may not have been obvious: in order to know where to look you would need to do class(fit1) to figure out which plot method was being used.
Roman's answer takes care of the image dimension stuff.