I'm trying to plot lines and color the lines based on the probability of that connection. Given a vector of probabilities, I use:
colfunc <- colorRamp(c("white", "red"))
colors <- colfunc(probs)
colors is then an nx3 matrix of rgb values. However, colfunc quite often returns a 0 value, so when i attempt to plot using these colors, R complains
Error in col2rgb(colors) : numerical color values must be positive
Is there an error in the way I am defining my color function?
Your function works fine, I think, but it doesn't return colors you can use with plot, because plot wants a color, not RGB values in a matrix.
There's probably a better way, but you can simply covert the matrix:
probs <- runif(10)
colors <- colfunc(probs)
my_col = apply(colors, MARGIN = 1, function(x) rgb(x[1]/255, x[2]/255, x[3]/255))
plot(1:10, 1:10, col = my_col) # should work fine
or you could just wrap your function
better_colfunc <- function(x, ramp = colorRamp(c("white", "red"))) {
colors <- ramp(x)
colors = apply(colors, MARGIN = 1, function(x) rgb(x[1]/255, x[2]/255, x[3]/255))
return(colors)
}
plot(1:10, 1:10, col = better_colfunc(probs, ramp = colfunc))
As for "colfunc quite often returns a 0 value", and other issues, you'll need to share both some data (what do your probs look like?) as well as perhaps the actual plotting code. See here for tips on making reproducible questions.
I am a bit confused what you are trying to do...the col2rgb function returns rgb values, so if you already have those then what do you want?
Or if you want rgb, why not use:
col2rgb(c("white", "red"))
Related
Do you know how I can change a color of a surface in VineCopula::plot.BiCop function? I've tried to use a col argument but it's worked just for color of lines of a grid.
library(VineCopula)
par.gauss<-BiCopTau2Par(1, .7, check.taus = TRUE)
obj.gauss <- BiCop(family = 1, par = par.gauss)
plot(obj.gauss,zlim=c(0,12))
I've tried:
plot(obj.gauss,zlim=c(0,12), col="green")
but it's changed just a color of lines.
You could use
n <- 70
plot(obj.gauss, zlim=c(0,12), col.regions=rainbow(n))
n gives the number of colors used in your plot. rainbow is a color palette, there are several others. Look at ?rainbow, you could use
hcl.colors(n)
rainbow(n)
heat.colors(n)
terrain.colors(n)
topo.colors(n)
cm.colors(n)
and there are several parameters, so for example
plot(obj.gauss, zlim=c(0,12), col.regions=rainbow(120, s=0.5, alpha=0.75))
I have a dataframe like this:
library(lubridate)
date_list = seq(ymd('2017-01-01'),ymd('2018-12-31'),by='day')
values = sample(0:35, 730, replace=TRUE)
testframe = data.frame(Date = date_list, Value = values)
The values are between 0 and 35. I want to create a heatmap with this data with colors from lightblue1 to royalblue. My problem is, that I want to highlight the values that are 0. They should stand out with a red color. So I dont want to have a gradient from 0 to the next numbers, I completely want to seperate 0 from 1. So all 0 should be red and all other values should be colored in a gradient from lightblue1 to royalblue.
I am using a heatmap, that contains the colorRampPalette, the full code is here: Heatmap Code
When I add btb<- c("lightblue1", "royalblue") in line 83 of the code and change the color in line 46 aswell I get the gradient without the red. How can I add now the red color for all 0?
Any suggestions on how I can solve that problem?
Updated Answer
In addition to adding the line
b2b <- c("lightblue1", "royalblue")
you also need to change
calendar.pal <- colorRampPalette((col.sty), space = "Lab")
to something like
calendar.pal1 <- colorRampPalette((col.sty), space = "Lab")
calendar.pal <- function(n) c("red", calendar.pal1(n-1))
Then, we can get the red highlighting you want
calendarHeat(date_list, values, color = "b2b", ncolors = 15)
Note that I've decreased the number of colors there. If the ncolors value is too high, the red disappears, so you have to watch for that.
Old Answer
The code you linked was pretty lengthy, so I didn't spend time studying it. Instead, I generate the following solution to your general problem:
First, create a function giving the basic color ramp:
foo <- colorRampPalette(c("lightblue1", "royalblue"))
Then, create a function that calls foo() for the color ramp, but modifies it according to your highlight rule here:
bar <- function(x) {
pal <- foo(length(x))
pal[which(x == 0)] <- "red"
return(pal)
}
I generate data as in your post (setting the seed for reproducibility):
library(lubridate)
date_list = seq(ymd('2017-01-01'),ymd('2018-12-31'),by='day')
set.seed(123)
values = sample(0:35, 730, replace=TRUE)
And try out the function:
plot(date_list, values, col = bar(values), pch = 19)
Does anyone have an idea how is this kind of chart plotted? It seems like heat map. However, instead of using color, size of each cell is used to indicate the magnitude. I want to plot a figure like this but I don't know how to realize it. Can this be done in R or Matlab?
Try scatter:
scatter(x,y,sz,c,'s','filled');
where x and y are the positions of each square, sz is the size (must be a vector of the same length as x and y), and c is a 3xlength(x) matrix with the color value for each entry. The labels for the plot can be input with set(gcf,properties) or xticklabels:
X=30;
Y=10;
[x,y]=meshgrid(1:X,1:Y);
x=reshape(x,[size(x,1)*size(x,2) 1]);
y=reshape(y,[size(y,1)*size(y,2) 1]);
sz=50;
sz=sz*(1+rand(size(x)));
c=[1*ones(length(x),1) repmat(rand(size(x)),[1 2])];
scatter(x,y,sz,c,'s','filled');
xlab={'ACC';'BLCA';etc}
xticks(1:X)
xticklabels(xlab)
set(get(gca,'XLabel'),'Rotation',90);
ylab={'RAPGEB6';etc}
yticks(1:Y)
yticklabels(ylab)
EDIT: yticks & co are only available for >R2016b, if you don't have a newer version you should use set instead:
set(gca,'XTick',1:X,'XTickLabel',xlab,'XTickLabelRotation',90) %rotation only available for >R2014b
set(gca,'YTick',1:Y,'YTickLabel',ylab)
in R, you should use ggplot2 that allows you to map your values (gene expression in your case?) onto the size variable. Here, I did a simulation that resembles your data structure:
my_data <- matrix(rnorm(8*26,mean=0,sd=1), nrow=8, ncol=26,
dimnames = list(paste0("gene",1:8), LETTERS))
Then, you can process the data frame to be ready for ggplot2 data visualization:
library(reshape)
dat_m <- melt(my_data, varnames = c("gene", "cancer"))
Now, use ggplot2::geom_tile() to map the values onto the size variable. You may update additional features of the plot.
library(ggplot2)
ggplot(data=dat_m, aes(cancer, gene)) +
geom_tile(aes(size=value, fill="red"), color="white") +
scale_fill_discrete(guide=FALSE) + ##hide scale
scale_size_continuous(guide=FALSE) ##hide another scale
In R, corrplotpackage can be used. Specifically, you have to use method = 'square' when creating the plot.
Try this as an example:
library(corrplot)
corrplot(cor(mtcars), method = 'square', col = 'red')
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 am trying to create a simple heatmap in R, using a diverging colour palette. I want to use a gradient so that all numbers below a threshold N are designated a color (say purple), and all numbers above the threshold are designated another color (say orange). The further away the number is from the threshold, the darker the color should be.
Here is a sample dataset:
Division,COL1,COL2,COL3,COL4,COL5,COL6,COL7
Division 1,31.9221884012222,75.8181694429368,97.0480443444103,96.295954938978,70.5677134916186,63.0451830103993,93.0396212730557
Division 2,85.7012346852571,29.0621076244861,16.9130333233625,94.6443660184741,19.9103083927184,61.9562198873609,72.3791105207056
Division 3,47.1665125340223,99.4153356179595,8.51091076619923,79.1276383213699,41.915355855599,7.45079894550145,24.6946100145578
Division 4,66.0743870772421,24.6163331903517,78.694460215047,42.04714265652,50.2694897353649,73.0409651994705,87.3745442833751
Division 5,29.6664374880493,35.4036891367286,19.2967326845974,5.48460693098605,32.4517334811389,15.5926876701415,76.0523204226047
Division 6,95.4969164915383,8.63230894319713,61.7535551078618,24.5590241160244,25.5453423131257,56.397921172902,44.4693325087428
Division 7,87.5015622004867,28.7770316936076,56.5095080062747,34.6680747810751,28.1923673115671,65.0204187724739,13.795713102445
Division 8,70.1077231671661,72.4712177179754,38.4903231170028,36.1821102909744,97.0875509083271,17.184783378616,78.2292529474944
Division 9,47.3570406902581,90.2257485780865,65.6037972308695,77.0234781783074,25.6294377148151,84.900529962033,82.5080851092935
Division 10,58.0811711959541,0.493217632174492,58.5604055318981,53.5780876874924,9.12552657537162,20.313960686326,78.1371118500829
Division 11,34.6708688884974,76.711881859228,22.6064443588257,22.1724311355501,5.48891355283558,79.1159523651004,56.8405059166253
Division 12,33.6812808644027,44.1363711375743,70.6362190190703,3.78900407813489,16.6075889021158,9.12654218263924,39.9711143691093
Here is a simple snippet to produce a heatmap from the above data
data <- read.csv("dataset.csv", sep=",")
row.names(data) <- data$Division
data <- data[,2:7]
data_matrix <- data.matrix(data)
heatmap(data_matrix, Rowv=NA, Colv=NA, col = heat.colors(256), scale="column", margins=c(5,10))
How can I modify the above code to produce:
a color gradient (orange) for all numbers ABOVE 50 (darker the further the number is from 50)
a color gradient (purple) for all numbers BELOW 50 (darker the further the number is from 50)
Nice to have (but optional) write the number value in the grid cell
Nice to have (but optional), use a different color for grid cell that is EXACTLY the threshold number (50 in this case)
[[Edit]]
I have just seen this question on SO, which seems to be very similar. The answer uses ggplot (which I have no experience of), and I have so far, been unable to adapt the ggplot solution to my slightly more complicated data.
This should get you most of the way. (Note that you'll need to set scale="none" if you want the plotted colors to correspond to the actual (rather than the rescaled) values of the cells).
ncol <- 100
## Make a vector with n colors
cols <- RColorBrewer:::brewer.pal(11,"PuOr") # OR c("purple","white","orange")
rampcols <- colorRampPalette(colors = cols, space="Lab")(ncol)
rampcols[(n/2) + 1] <- rgb(t(col2rgb("green")), maxColorValue=256)
## Make a vector with n+1 breaks
rampbreaks <- seq(0, 100, length.out = ncol+1)
## Try it out
heatmap(data_matrix, Rowv = NA, Colv = NA, scale="none",
col = rampcols, breaks = rampbreaks)
EDIT
For finer control over the placement of the threshold, I'd suggest creating two separate palettes -- one for values less than the threshold and one for values above the threshold -- and then "suturing" them together. Try something like this, playing around with different values for Min, Max, Thresh, etc.:
nHalf <- 50
Min <- 0
Max <- 100
Thresh <- 50
## Make vector of colors for values below threshold
rc1 <- colorRampPalette(colors = c("purple", "white"), space="Lab")(nHalf)
## Make vector of colors for values above threshold
rc2 <- colorRampPalette(colors = c("white", "orange"), space="Lab")(nHalf)
rampcols <- c(rc1, rc2)
## In your example, this line sets the color for values between 49 and 51.
rampcols[c(nHalf, nHalf+1)] <- rgb(t(col2rgb("green")), maxColorValue=256)
rb1 <- seq(Min, Thresh, length.out=nHalf+1)
rb2 <- seq(Thresh, Max, length.out=nHalf+1)[-1]
rampbreaks <- c(rb1, rb2)
heatmap(data_matrix, Rowv = NA, Colv = NA, scale="none",
col = rampcols, breaks = rampbreaks)
I found this thread very useful and also pulled some ideas from here, but for my purposes I needed to generalize some things and wanted to use the RColorBrewer package. While I was working on it Dr. Brewer (of Color Brewer fame) stopped in my office and told me I needed to interpolate within the smaller color breaks rather than just pick the end points. I thought others might find this useful so I am posting my function here for posterity.
The function takes in your data vector, the name of a diverging colorBrewer palette, and the center point for your color scheme (default is 0). It outputs a list containing 2 objects: a classIntervals object and a vector of colors: The function is set to interpolate a total of 100 colors but that can be modified with some care.
diverge.color <- function(data,pal_choice="RdGy",centeredOn=0){
nHalf=50
Min <- min(data,na.rm=TRUE)
Max <- max(data,na.rm=TRUE)
Thresh <- centeredOn
pal<-brewer.pal(n=11,pal_choice)
rc1<-colorRampPalette(colors=c(pal[1],pal[2]),space="Lab")(10)
for(i in 2:10){
tmp<-colorRampPalette(colors=c(pal[i],pal[i+1]),space="Lab")(10)
rc1<-c(rc1,tmp)
}
rb1 <- seq(Min, Thresh, length.out=nHalf+1)
rb2 <- seq(Thresh, Max, length.out=nHalf+1)[-1]
rampbreaks <- c(rb1, rb2)
cuts <- classIntervals(data, style="fixed",fixedBreaks=rampbreaks)
return(list(cuts,rc1))
}
in my work I am using this scheme to plot a raster layer (rs) using spplot like so:
brks<-diverge.color(values(rs))
spplot(rs,col.regions=brks[[2]],at=brks[[1]]$brks,colorkey=TRUE))