how to plot a figure with specific distance between each line - r

Question

Actually I try to plot a figure but it puts and shows all the columns(lines) on each other so it is not representative. I try to make a simulated data and show you how I plot it, and also show you what I want
I don't know how to make a data like example i show below but here 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 making the data frame, I will plot all columns versus the first one by 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 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 column. I don't care about the function , I just want the photo

Answer 1

This solution uses rgl and produces this plot:
It uses this function that accepts 3 arguments:
df : a data.frame just like your 'M' above
x : a numeric vector (or a 1-coldata.frame`) for the x-axis
cols : (optionnal) a vector of colours to repeat. If missing, black line are 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 your script, eg at the beginning.
If you don't have rgl installed, then install.packages("rgl").
Black lines, the default, may produce some moiré effect, but a repeating color palette is worse. This may be brain-dependant. A single colour would also avoid introducing an artificial dimension (and a strong one).
An 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 navigated with the mouse.
Do you need something else?
EDIT
You can build your second plot with the function below. The range of your data is so large, and I think the whole idea behind shifting upwards every line, prevent having an y-axis with a reliable scale. Here I have normalized all signals (0 <= signal <= 1). Also the parameter gap can be use to play with this. We could disconnect the two behaviors but I think it's nice. Try different values of gap and see examples below.
df : a data.frame just like your 'M' above
x : a numeric vector (or a 1-coldata.frame`) for the x-axis
cols : (optionnal) a vector of colours to repeat. If missing, black line are drawn
gap : gap factor between individual lines
more_gap_each: every n lines, a bigger gap is produced...
more_gap_relative: ... and will be 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 obtain this plot:
or:
nik_plot2D(M, x, 50)
or, with colors:
nik_plot2D(M, x, gap=20, cols=1:3)
nik_plot2D(M, x, gap=20, cols=rep(1:3, each=5))
or, still with colours and but with larger 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 other have pointed out, your data have very large peaks and it's not clear whether you want to allow some curves to overlap,
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, some outlier/peak detection scheme could be used to filter them out before calculating the shift between 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 plotting I prefer the rgl package. This should be close to your desired solution.
The color of each scan changes on every third one.
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")
Do the huge differences in magnitude of the data points, I needed to scale some factors to make a readable graph. The intensity of the goes from 0 to nearly 1,000,000, thus distorting the graph. Attempted to normalize by taking the ln, but plot became unreadable.