I need a plot of different density lines, each in another color. This is an example code (but much smaller), using the built-in data.fame USArrests. I hope it is ok to use it?
colors <- heat.colors(3)
plot(density(USArrests[,2], bw=1, kernel="epanechnikov", na.rm=TRUE),col=colors[1])
lines1E <- function(x)lines(density(x,bw=1,kernel="epanechnikov",na.rm=TRUE))
lines1EUSA <- colwise(lines1E)(USArrests[,3:4])`
Currently the code produces with colwise() just one color. How can I get each line with another color? Or is there ab better way to plot several density lines with different colors?
I don't quite follow your example, so I've created my own example data set. First, create a matrix with three columns:
m = matrix(rnorm(60), ncol=3)
Then plot the density of the first column:
plot(density(m[,1]), col=2)
Using your lines1E function as a template:
lines1E = function(x) {lines(density(x))}
We can add multiple curves to the plot:
colwise(lines1E)(as.data.frame(m[ ,2:3]))
Personally, I would just use:
##Added in NA for illustration
m = matrix(rnorm(60), ncol=3)
m[1,] = NA
plot(density(m[,1], na.rm=T))
sapply(2:ncol(m), function(i) lines(density(m[,i], na.rm=T), col=i))
to get:
Related
I am attempting to produce an NMDS plot in vegan, but really struggling with the code. I am trying to display the site points and species points differently, with the site points coloured according to treatment. Both lines work individually, but I cannot work out how to combine these two lines of code into one line to form one graph. I am using ordipointlabel to prevent overlap. These are the two lines of code I want to combine into one.
ordipointlabel(NMDS10, scaling=2, display="species", select=sel)
ordipointlabel(NMDS10,display="sites", col=c(rep("darkgreen",4),rep("blue4",4)),cex=0.75)
You can access directly to ordinpointlabel object and make it look like you wish. Please see the sample:
library(vegan)
data(dune)
NMDS10 <- metaMDS(dune[1:8, ])
pdf(file = NULL)
y <- ordipointlabel(NMDS10, display=c("sites", "species"))
dev.off()
# select sites & species
sel <- unlist(dimnames(dune[1:8, ]))[-(20:ncol(dune))]
# messing with ordipointlabel object
y$points <- y$points[rownames(y$points) %in% sel, ]
y$args$pcol[] = rep("red", length(y$args$pcol))
y$args$pcol[1:8] <- c(rep("darkgreen", 4), rep("blue4", 4))
y$par$cex <- 0.75
plot(y)
This is my code to create a boxplot in R that has 4 boxplots in one.
psnr_x265_256 <- c(39.998,39.998, 40.766, 38.507,38.224,40.666,38.329,40.218,44.746,38.222)
psnr_x264_256 <- c(39.653, 38.106,37.794,36.13,36.808,41.991,36.718,39.26,46.071,36.677)
psnr_xvid_256 <- c(33.04564,33.207269,32.715427,32.104696,30.445141,33.135261,32.669766, 31.657039,31.53103,31.585865)
psnr_mpeg2_256 <- c(32.4198,32.055051,31.424819,30.560274,30.740421,32.484694, 32.512268,32.04659,32.345848, 31)
all_errors = cbind(psnr_x265_256, psnr_x264_256, psnr_xvid_256,psnr_mpeg2_256)
modes = cbind(rep("PSNR",10))
journal_linear_data <-data.frame(psnr_x265_256, psnr_x264_256, psnr_xvid_256,psnr_mpeg2_256)
yvars <- c("psnr_x265_256","psnr_x264_256","psnr_xvid_256","psnr_mpeg2_256")
xvars <- c("x265","x264","xvid","mpeg2")
bmp(filename="boxplot_PSNR_256.bmp")
boxplot(journal_linear_data[,yvars], xlab=xvars, ylab="PSNR")
dev.off()
This is the image I get.
I want to have the corresponding values for each boxplot in x axis "x265","x264","xvid","mpeg2".
Do you have any idea how to fix this?
There are multiple ways of changing the labels for your boxplot variables. Probably the simplest way is changing the column names of your data frame:
colnames(journal_linear_data) <- c("x265","x264","xvid","mpeg2")
Even simpler: you could do this right at the creation of your data frame too:
journal_linear_data <- data.frame(x265=psnr_x265_256, x264=psnr_x264_256, xvid=psnr_xvid_256, mpeg2=psnr_mpeg2_256)
If you run into the problem of your labels not being shown or overlapping due to too few space, try rotating the x labels using the las parameter, e.g. las=2 or las=3.
I'm plotting some Q-Q plots using the qqplot function. It's very convenient to use, except that I want to color the data points based on their IDs. For example:
library(qualityTools)
n=(rnorm(n=500, m=1, sd=1) )
id=c(rep(1,250),rep(2,250))
myData=data.frame(x=n,y=id)
qqPlot(myData$x, "normal",confbounds = FALSE)
So the plot looks like:
I need to color the dots based on their "id" values, for example blue for the ones with id=1, and red for the ones with id=2. I would greatly appreciate your help.
You can try setting col = myData$y. I'm not sure how the qqPlot function works from that package, but if you're not stuck with using that function, you can do this in base R.
Using base R functions, it would look something like this:
# The example data, as generated in the question
n <- rnorm(n=500, m=1, sd=1)
id <- c(rep(1,250), rep(2,250))
myData <- data.frame(x=n,y=id)
# The plot
qqnorm(myData$x, col = myData$y)
qqline(myData$x, lty = 2)
Not sure how helpful the colors will be due to the overplotting in this particular example.
Not used qqPlot before, but it you want to use it, there is a way to achieve what you want. It looks like the function invisibly passes back the data used in the plot. That means we can do something like this:
# Use qqPlot - it generates a graph, but ignore that for now
plotData <- qqPlot(myData$x, "normal",confbounds = FALSE, col = sample(colors(), nrow(myData)))
# Given that you have the data generated, you can create your own plot instead ...
with(plotData, {
plot(x, y, col = ifelse(id == 1, "red", "blue"))
abline(int, slope)
})
Hope that helps.
I'm working with some custom functions and I need to draw contours for them based on multiple values for the parameters.
Here is an example function:
I need to draw such a contour plot:
Any idea?
Thanks.
First you construct a function, fourvar that takes those four parameters as arguments. In this case you could have done it with 3 variables one of which was lambda_2 over lambda_1. Alpha1 is fixed at 2 so alpha_1/alpha_2 will vary over 0-10.
fourvar <- function(a1,a2,l1,l2){
a1* integrate( function(x) {(1-x)^(a1-1)*(1-x^(l2/l1) )^a2} , 0 , 1)$value }
The trick is to realize that the integrate function returns a list and you only want the 'value' part of that list so it can be Vectorize()-ed.
Second you construct a matrix using that function:
mat <- outer( seq(.01, 10, length=100),
seq(.01, 10, length=100),
Vectorize( function(x,y) fourvar(a1=2, x/2, l1=2, l2=y/2) ) )
Then the task of creating the plot with labels in those positions can only be done easily with lattice::contourplot. After doing a reasonable amount of searching it does appear that the solution to geom_contour labeling is still a work in progress in ggplot2. The only labeling strategy I found is in an external package. However, the 'directlabels' package's function directlabel does not seem to have sufficient control to spread the labels out correctly in this case. In other examples that I have seen, it does spread the labels around the plot area. I suppose I could look at the code, but since it depends on the 'proto'-package, it will probably be weirdly encapsulated so I haven't looked.
require(reshape2)
mmat <- melt(mat)
str(mmat) # to see the names in the melted matrix
g <- ggplot(mmat, aes(x=Var1, y=Var2, z=value) )
g <- g+stat_contour(aes(col = ..level..), breaks=seq(.1, .9, .1) )
g <- g + scale_colour_continuous(low = "#000000", high = "#000000") # make black
install.packages("directlabels", repos="http://r-forge.r-project.org", type="source")
require(directlabels)
direct.label(g)
Note that these are the index positions from the matrix rather than the ratios of parameters, but that should be pretty easy to fix.
This, on the other hand, is how easilyy one can construct it in lattice (and I think it looks "cleaner":
require(lattice)
contourplot(mat, at=seq(.1,.9,.1))
As I think the question is still relevant, there have been some developments in the contour plot labeling in the metR package. Adding to the previous example will give you nice contour labeling also with ggplot2
require(metR)
g + geom_text_contour(rotate = TRUE, nudge_x = 3, nudge_y = 5)
I am trying to cluster a protein dna interaction dataset, and draw a heatmap using heatmap.2 from the R package gplots. My matrix is symmetrical.
Here is a copy of the data-set I am using after it is run through pearson:DataSet
Here is the complete process that I am following to generate these graphs: Generate a distance matrix using some correlation in my case pearson, then take that matrix and pass it to R and run the following code on it:
library(RColorBrewer);
library(gplots);
library(MASS);
args <- commandArgs(TRUE);
matrix_a <- read.table(args[1], sep='\t', header=T, row.names=1);
mtscaled <- as.matrix(scale(matrix_a))
# location <- args[2];
# setwd(args[2]);
pdf("result.pdf", pointsize = 15, width = 18, height = 18)
mycol <- c("blue","white","red")
my.breaks <- c(seq(-5, -.6, length.out=6),seq(-.5999999, .1, length.out=4),seq(.100009,5, length.out=7))
#colors <- colorpanel(75,"midnightblue","mediumseagreen","yellow")
result <- heatmap.2(mtscaled, Rowv=T, scale='none', dendrogram="row", symm = T, col=bluered(16), breaks=my.breaks)
dev.off()
The issue I am having is once I use breaks to help me control the color separation the heatmap no longer looks symmetrical.
Here is the heatmap before I use breaks, as you can see the heatmap looks symmetrical:
Here is the heatmap when breaks are used:
I have played with the cutoff's for the sequences to make sure for instance one sequence does not end exactly where the other begins, but I am not able to solve this problem. I would like to use the breaks to help bring out the clusters more.
Here is an example of what it should look like, this image was made using cluster maker:
I don't expect it to look identical to that, but I would like it if my heatmap is more symmetrical and I had better definition in terms of the clusters. The image was created using the same data.
After some investigating I noticed was that after running my matrix through heatmap, or heatmap.2 the values were changing, for example the interaction taken from the provided data set of
Pacdh-2
and
pegg-2
gave a value of 0.0250313 before the matrix was sent to heatmap.
After that I looked at the matrix values using result$carpet and the values were then
-0.224333135
-1.09805379
for the two interactions
So then I decided to reorder the original matrix based on the dendrogram from the clustered matrix so that I was sure that the values would be the same. I used the following stack overflow question for help:
Order of rows in heatmap?
Here is the code used for that:
rowInd <- rev(order.dendrogram(result$rowDendrogram))
colInd <- rowInd
data_ordered <- matrix_a[rowInd, colInd]
I then used another program "matrix2png" to draw the heatmap:
I still have to play around with the colors but at least now the heatmap is symmetrical and clustered.
Looking into it even more the issue seems to be that I was running scale(matrix_a) when I change my code to just be mtscaled <- as.matrix(matrix_a) the result now looks symmetrical.
I'm certainly not the person to attempt reproducing and testing this from that strange data object without code that would read it properly, but here's an idea:
..., col=bluered(20)[4:20], ...
Here's another though which should return the full rand of red which tha above strategy would not:
shift.BR<- colorRamp(c("blue","white", "red"), bias=0.5 )((1:16)/16)
heatmap.2( ...., col=rgb(shift.BR, maxColorValue=255), .... )
Or you can use this vector:
> rgb(shift.BR, maxColorValue=255)
[1] "#1616FF" "#2D2DFF" "#4343FF" "#5A5AFF" "#7070FF" "#8787FF" "#9D9DFF" "#B4B4FF" "#CACAFF" "#E1E1FF" "#F7F7FF"
[12] "#FFD9D9" "#FFA3A3" "#FF6C6C" "#FF3636" "#FF0000"
There was a somewhat similar question (also today) that was asking for a blue to red solution for a set of values from -1 to 3 with white at the center. This it the code and output for that question:
test <- seq(-1,3, len=20)
shift.BR <- colorRamp(c("blue","white", "red"), bias=2)((1:20)/20)
tpal <- rgb(shift.BR, maxColorValue=255)
barplot(test,col = tpal)
(But that would seem to be the wrong direction for the bias in your situation.)