How to plot with a specific distance between each line

Question

I'm actually trying to build a drawing, but it puts and shows all the columns (rows) on top of each other, so it is not representative. I am trying to make simulated data and show you how I earned it, and also show you what I want.

I don’t know how to make an example like below, but here is what I do

set.seed(1) M <- matrix(rnorm(20),20,5) x <- as.matrix(sort(runif(20, 5.0, 7.5))) df <- as.data.frame(cbind(x,M)) 

After creating the data frame, I will build all the columns compared to the first, melting it and using ggplot

 require(ggplot2) require(reshape) dff <- melt(df , id.vars = 'V1') b <- ggplot(dff, aes(V1,value)) + geom_line(aes(colour = variable)) 

I want to have a specific distance between each line (in this case we have 6) something like below. in one dimension it is V1, in another dimension it is the number of columns. I don't care about the function, I just want a photo

Answer 1

This solution uses rgl and creates this graph:

It uses this function, which takes 3 arguments:

• df : a data.frame exactly the same as your "M" above
• x : a numeric vector (or a 1-col data.frame`) for the x axis
• cols : (optionnal) a vector of colors to repeat. If absent, a black line is drawn

Here is the function:

 nik_plot <- function(df, x, cols){ require(rgl) # if a data.frame is if (is.data.frame(x) && ncol(x)==1) x <- as.numeric(x[, 1]) # prepare a vector of colors if (missing(cols)) cols <- rep_len("#000000", nrow(df)) else cols <- rep_len(cols, nrow(df)) # initialize an empty 3D plot plot3d(NA, xlim=range(x), ylim=c(1, ncol(df)-1), zlim=range(df), xlab="Mass/Charge (M/Z)", ylab="Time", zlab="Ion Spectra", box=FALSE) # draw lines, silently silence_please <- sapply(1:ncol(df), function(i) lines3d(x=x, y=i, z=df[, i], col=cols[i])) } 

Note that you can remove require(rgl) from the function and library(rgl) somewhere in the script, for example, at the beginning.

If you do not have rgl , then install.packages("rgl") .

Black lines by default can cause a moire effect, but a repeating color palette is worse. It may be brain dependent. One color will also avoid artificial measurement (and strong).

Example below:

 # black lines nik_plot(M, x) # as in the image above nik_plot(M, x, "grey40") # an unreadable rainbow nik_plot(M, x, rainbow(12)) 

The 3D window can be moved with the mouse.

Do you need something else?

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EDIT

You can build your second plot using the function below. The range of your data is so large, and I think the whole idea of ​​shifting up and up on each line prevents the presence of the Y axis with a reliable scale. Here I normalized all signals (0 <= signal <= 1). In addition, the gap parameter can be used for this. We could disable two behaviors, but I think it’s nice. Try different gap values ​​and see examples below.

• df : a data.frame exactly the same as your "M" above
• x : a numeric vector (or a 1-col data.frame`) for the x axis
• cols : (optionnal) a vector of colors to repeat. If absent, a black line is drawn
• gap : gap coefficient between individual lines
• more_gap_each : every n lines get a bigger gap ...
• more_gap_relative : ... and there will be a gap x more_gap_relative wide

Here is the function:

 nik_plot2D <- function(df, x, cols, gap=10, more_gap_each=1, more_gap_relative=0){ if (is.data.frame(x) && ncol(x)==1) x <- as.numeric(x[, 1]) # we normalize ( 0 <= signal <= 1) df <- df-min(df) df <- (df/max(df)) # we prepare a vector of colors if (missing(cols)) cols <- rep_len("#00000055", nrow(df)) else cols <- rep_len(cols, nrow(df)) # we prepare gap handling. there is probably more elegant gaps <- 1 for (i in 2:ncol(df)) gaps[i] <- gaps[i - 1] + 1/gap + ifelse((i %% more_gap_each) == 0, (1/gap)*more_gap_relative, 0) # we initialize the plot plot(NA, xlim=range(x), ylim=c(min(df), 1+max(gaps)), xlab="Time", ylab="", axes=FALSE, mar=rep(0, 4)) axis(1) # finally, the lines silent <- lapply(1:ncol(df), function(i) lines(x, df[, i] + gaps[i], col=cols[i])) } 

We can use it with (default):

 nik_plot2D(M, x) # gap=10 

And you get this plot:

or

 nik_plot2D(M, x, 50) 

or, with flowers:

 nik_plot2D(M, x, gap=20, cols=1:3) nik_plot2D(M, x, gap=20, cols=rep(1:3, each=5)) 

or, still with flowers, but with big gaps:

 nik_plot2D(M, x, gap=20, cols=terrain.colors(10), more_gap_each = 1, more_gap_relative = 0) # no gap by default nik_plot2D(M, x, gap=20, cols=terrain.colors(10), more_gap_each = 10, more_gap_relative = 4) # large gaps every 10 lines nik_plot2D(M, x, gap=20, cols=terrain.colors(10), more_gap_each = 5, more_gap_relative = 2) # small gaps every 5 lines 

Answer 2

As others have pointed out, your data has very large peaks, and it's unclear if you want to overlap some curves,

 m <- read.table("~/Downloads/M.txt", head=T) fudge <- 0.05 shifty <- function(m, fudge=1){ shifts <- fudge * max(abs(apply(m, 2, diff))) * seq(0, ncol(m)-1) m + matrix(shifts, nrow=nrow(m), ncol=ncol(m), byrow=TRUE) } par(mfrow=c(1,2), mar=c(0,0,1,0)) cols <- colorRampPalette(blues9[4:9])(ncol(m)) matplot(shifty(m), t="l", lty=1, bty="n", yaxt="n", xaxt="n", ylab="", col=cols) title("no overlap") matplot(shifty(m, 0.05), t="l", lty=1, bty="n", yaxt="n", xaxt="n", ylab="", col=cols) title("some overlap") 

Alternatively, you can use some outlier / peak detection scheme to filter them before calculating the offset between the curves,

 library(outliers) shifty2 <- function(m, outliers = 10){ tmp <- m for(ii in seq_len(outliers)) tmp <- rm.outlier(tmp, median = TRUE) shifts <- max(abs(apply(tmp, 2, diff))) * seq(0, ncol(m)-1) m + matrix(shifts, nrow=nrow(m), ncol=ncol(m), byrow=TRUE) } matplot(shifty2(m), t="l", lty=1, bty="n", yaxt="n", xaxt="n", ylab="", col=cols) 

(there are probably good algorithms to decide which points to remove, but I don't know them)

Answer 3

For 3D building, I prefer the rgl package. This should be close to your desired solution.
The color of each scan changes every third.

 library(rgl) M<-read.table("M.txt", sep="\t", header = TRUE, colClasses = "numeric") x<-read.table("x.txt", sep="\t", header = TRUE) n<-ncol(M) M[M<1]<-1 plot3d(x='', xlim=range(x$Time), ylim=c(1, n), zlim=(range(M)), box=FALSE) sapply(seq(1,n), function(t){lines3d(x$Time, y=t*10, z=(M[,t])/10000, col=t/3+1)}) title3d(xlab="scan", ylab="time", zlab="intensity") title3d(main ="Extracted Spectra Subset") axes3d() #axis3d(edge="x") #axis3d(edge="y") #axis3d(edge="z") 

Whether there are huge differences in the size of the data points, I needed to scale some factors to make a readable graph. The intensity ranges from 0 to almost 1,000,000, which distorts the graph. An attempt to normalize by taking ln, but the graph became unreadable.