I have the following dataframe that I would like to plot. I was wondering if it is possible to color portions of the lines connecting my outcome variable(stackOne$y) in a different color, depending on whether it is less than a certain value or not. For example, I would like portions of the lines falling below 2.2 to be red in color.
set.seed(123)
stackOne = data.frame(id = rep(c(1, 2, 3), each = 3),
y = rnorm(9, 2, 1),
x = rep(c(1, 2, 3), 3))
ggplot(stackOne, aes(x = x, y = y)) +
geom_point() +
geom_line(aes(group = id))
Thanks!
You have at least a couple of options here. The first is quite simple, general (in that it's not limited to straight-line segments) and precise, but uses base plot rather than ggplot. The second uses ggplot, but is slightly more complicated, and colour transition will not be 100% precise (but near enough, as long as you specify an appropriate resolution... read on).
base:
If you're willing to use base plotting functions rather than ggplot, you could clip the plotting region to above the threshold (2.2), then plot the segments in your preferred colour, and subsequently clip to the region below the threshold, and plot again in red. While the first clip is strictly unnecessary, it prevents overplotting different colours, which can look a bit dud.
threshold <- 2.2
set.seed(123)
stackOne=data.frame(id=rep(c(1,2,3),each=3),
y=rnorm(9,2,1),
x=rep(c(1,2,3),3))
# create a second df to hold segment data
d <- stackOne
d$y2 <- c(d$y[-1], NA)
d$x2 <- c(d$x[-1], NA)
d <- d[-findInterval(unique(d$id), d$id), ] # remove last row for each group
plot(stackOne[, 3:2], pch=20)
# clip to region above the threshold
clip(min(stackOne$x), max(stackOne$x), threshold, max(stackOne$y))
segments(d$x, d$y, d$x2, d$y2, lwd=2)
# clip to region below the threshold
clip(min(stackOne$x), max(stackOne$x), min(stackOne$y), threshold)
segments(d$x, d$y, d$x2, d$y2, lwd=2, col='red')
points(stackOne[, 3:2], pch=20) # plot points again so they lie over lines
ggplot:
If you want or need to use ggplot, you can consider the following...
One solution is to use geom_line(aes(group=id, color = y < 2.2)), however this will assign colours based on the y-value of the point at the beginning of each segment. I believe you want to have the colour change not just at the nodes, but wherever a line crosses your given threshold of 2.2. I'm not all that familiar with ggplot, but one way to achieve this is to make a higher-resolution version of your data by creating new points along the lines that connect your existing points, and then use the color = y < 2.2 argument to achieve the desired effect.
For example:
threshold <- 2.2 # set colour-transition threshold
yres <- 0.01 # y-resolution (accuracy of colour change location)
d <- stackOne # for code simplification
# new cols for point coordinates of line end
d$y2 <- c(d$y[-1], NA)
d$x2 <- c(d$x[-1], NA)
d <- d[-findInterval(unique(d$id), d$id), ] # remove last row for each group
# new high-resolution y coordinates between each pair within each group
y.new <- apply(d, 1, function(x) {
seq(x['y'], x['y2'], yres*sign(x['y2'] - x['y']))
})
d$len <- sapply(y.new, length) # length of each series of points
# new high-resolution x coordinates corresponding with new y-coords
x.new <- apply(d, 1, function(x) {
seq(x['x'], x['x2'], length.out=x['len'])
})
id <- rep(seq_along(y.new), d$len) # new group id vector
y.new <- unlist(y.new)
x.new <- unlist(x.new)
d.new <- data.frame(id=id, x=x.new, y=y.new)
p <- ggplot(d.new, aes(x=x,y=y)) +
geom_line(aes(group=d.new$id, color=d.new$y < threshold))+
geom_point(data=stackOne)+
scale_color_discrete(sprintf('Below %s', threshold))
p
There may well be a way to do this through ggplot functions, but in the meantime I hope this helps. I couldn't work out how to draw a ggplotGrob into a clipped viewport (rather it seems to just scale the plot). If you want colour to be conditional on some x-value threshold instead, this would obviously need some tweaking.
Encouraged by people in my answer to a newer but related question, I'll also share a easier to use approximation to the problem here.
Instead of interpolating the correct values exactly, one can use ggforce::geom_link2() to interpolate lines and use after_stat() to assign the correct colours after interpolation. If you want more precision you can increase the n of that function.
library(ggplot2)
library(ggforce)
#> Warning: package 'ggforce' was built under R version 4.0.3
set.seed(123)
stackOne = data.frame(id = rep(c(1, 2, 3), each = 3),
y = rnorm(9, 2, 1),
x = rep(c(1, 2, 3), 3))
ggplot(stackOne, aes(x = x, y = y)) +
geom_point() +
geom_link2(
aes(group = id,
colour = after_stat(y < 2.2))
) +
scale_colour_manual(
values = c("black", "red")
)
Created on 2021-03-26 by the reprex package (v1.0.0)
Related
i need to overlay multiple subplots onto a single plot which is already contained inside a multirow figure (see image)
the reason why i need subplots instead of screen layout is because the figure will be possibly multicolumn, also (a 5 by 3 plot, in total)
there are packages which assist in doing subplots, but they break when you use multirow figures, and sequential subplots, except the first one, are rendered next to the overall figure border, not relative to the current row/column plot borders
i understand large packages such as ggplot2 allow this relatively easily, but base R plots are highly preferable
UPD:
the minimum reproducible example depicting the problem is here:
require(Hmisc)
COL.1 <- c('red','orange','yellow'); COL.2 <- c('blue','green','turquoise')
SUBPLOT.FUN <- function(COL) {plot(rnorm(100), type='l', col=COL)}
PLOT.FUN <- function(i) {
plot(rnorm(100),ylim=c(-1,1))
subplot(SUBPLOT.FUN(COL.1[i]), 100,1, vadj=1,hadj=1,pars=list(mfg=c(1,i)))
subplot(SUBPLOT.FUN(COL.2[i]), 100,-1,vadj=0,hadj=1,pars=list(mfg=c(1,i)))
}
plot.new(); par(mfrow=c(1,3))
for (i in 1:3) {
PLOT.FUN(i)
}
which looks like that:
while what is required is shown on the first image (meaning, EACH of the three plots must contain 3 subplots in their respective locations (along the right border, arranged vertically))
N.B. either the figure is multirow or multicolumn (as depicted) does not matter
Something like this? Inspired in this R-bloggers post.
# reproducible test data
set.seed(2022)
x <- rnorm(1000)
y <- rbinom(1000, 1, 0.5)
z <- rbinom(1000, 4, 0.5)
# save default values and prepare
# to create custom plot areas
old_par <- par(fig = c(0,1,0,1))
# set x axis limits based on data
h <- hist(x, plot = FALSE)
xlim <- c(h$breaks[1] - 0.5, h$breaks[length(h$breaks)] + 2)
hist(x, xlim = xlim)
# x = c(0.6, 1) right part of plot
# y = c(0.5, 1) top part of plot
par(fig = c(0.6, 1, 0.5, 1), new = TRUE)
boxplot(x ~ y)
# x = c(0.6, 1) right part of plot
# y = c(0.1, 0.6) bottom part of plot
par(fig = c(0.6, 1, 0.1, 0.6), new = TRUE)
boxplot(x ~ z)
# put default values back
par(old_par)
Created on 2022-08-18 by the reprex package (v2.0.1)
I've been trying this out but I cannot find a solution. The best I can do is plotting the first 15846 values in 1 colour and then using the lines() function to add the remaining 841 points. But these then appear at the start of the graph and does not continue from the 15846th datapoint.
str(as.numeric(sigma.in.fr))
num [1:15846] 0.000408 0.000242 0.000536 0.000274 0.000476 ...
str(as.numeric(sigma.out.fr))
num [1:841] 0.002558 0.000428 0.000255 0.000549 0.00028 ...
plot(as.numeric(sigma.in.fr),type="l",col=c("tomato4"))
lines(as.numeric(sigma.out.fr), type="l",col="tomato1")
This returns the plot below:
Lets make some dummy data to demonstrate:
sigma.ins.fr = sin((1:800)/20) + rnorm(800)
sigma.outs.fr = sin((801:1000)/20) + rnorm(200)
Now, put all the data together into a single sequence
sigma.all = c(sigma.ins.fr, sigma.outs.fr)
And create an x vector which simply counts along the data. We'll need this in the segments call below.
x = seq_along(sigma.all)
Now create a vector of colors for the trace. It is the same length as the full data, with a color for each segment.
cols = c(rep("tomato4", length(sigma.ins.fr)), rep("blue", length(sigma.outs.fr)))
Now create a blank canvass on which to draw the data.
plot(sigma.all, type="l", col=NA)
At last, we can plot the data. Unfortunately, lines does not allow for a separate color in different segments. So instead we can use segments
segments(head(x,-1), head(sigma.all,-1), x[-1], sigma.all[-1], type="l", col=cols)
Or, if you really prefer to use two separate traces uning lines, then we can achieve this by adding the x coordinates to each call:
plot(sigma.all, type="l", col=NA)
lines(seq_along(sigma.ins.fr), sigma.ins.fr, col=c("tomato4"))
lines(seq_along(sigma.outs.fr) + length(sigma.ins.fr), sigma.outs.fr, col="tomato1")
Please provide a reproducible example. Using the packages ggplot2 and dplyr you can do something like:
df <- tibble(x = seq(1,1000, 1), y = seq(1, 500.5, 0.5))
ggplot() +
geom_line(data = df %>% filter(x < 800),
aes(x = x, y = y), color = "red", size =2) +
geom_line(data = df %>% filter(x >= 800),
aes(x = x, y = y),
color = "black", size = 2)
Note that I put the cut off at 800 (as I only created 1000 points), but you can easily change that.
So what I do is putting the data in geom_line, as you can also use this if you have different dataframes (with overlapping x and y) you want to plot in the same graph. However, I do filter the data at different points, so that different lines are drawn by the geom_line.
I am trying to recreate an image found in a textbook in R, the original of which was built in MATLAB:
I have generated each of the graphs seperately, but what would be best practice them into an image like this in ggplot2?
Edit: Provided code used. This is just a transformation of normally distributed data.
library(ggplot2)
mean <- 6
sd <- 1
X <- rnorm(100000, mean = mean, sd = sd)
Y <- dnorm(X, mean = mean, sd = sd)
Y_p <- pnorm(X, mean = mean, sd = sd)
ch_vars <- function(X){
nu_vars <- c()
for (x in X){
nu_vars <- c(nu_vars, (1/(1 + exp(-x + 5))))
}
return(nu_vars)
}
nu_X <- ch_vars(X)
nu_Y <- ch_vars(Y)
data <- data.frame(x = X, y = Y, Y_p = Y_p, x = nu_X, y = nu_Y)
# Cumulative distribution
ggplot(data = data) +
geom_line(aes(x = X, y = Y_p))
# Distribution of initial data
ggplot(data = data_ch, aes(x = X)) +
geom_histogram(aes(y = ..density..), bins = 25, fill = "red", color = "black")
# Distribution of transformed data
ggplot(data = data, aes(x = nu_X)) +
geom_histogram(aes(y = ..density..), bins = 25, fill = "green", color = "black")
In short, you can't, or rather, you shouldn't.
ggplot is a high-level plotting packaging. More than a system for drawing shapes and lines, it's fairly "opinionated" about how data should be represented, and one of its opinions is that a plot should express a clear relationship between its axes and marks (points, bars, lines, etc.). The axes essentially define a coordinate space, and the marks are then plotted onto the space in a straightforward and easily interpretable manner.
The plot you show breaks that relationship -- it's a set of essentially arbitrary histograms all drawn onto the same box, where the axis values become ambiguous. The x-axis represents the values of 1 histogram and the y-axis represents another (and thus neither axis represents the histograms' heights).
It is of course technically possible to force ggplot to render something like your example, but it would require pre-computing the histograms, normalizing their values and bin heights to a common coordinate space, converting these into suitable coordinates for use with geom_rect, and then re-labeling the plot axes. It would be a very large amount of manual effort and ultimately defeats the point of using a high-level plotting grammar like ggplot.
I would like to plot y1 and y2 in the same plot.
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x, 1, 1)
plot(x, y1, type = "l", col = "red")
plot(x, y2, type = "l", col = "green")
But when I do it like this, they are not plotted in the same plot together.
In Matlab one can do hold on, but does anyone know how to do this in R?
lines() or points() will add to the existing graph, but will not create a new window. So you'd need to do
plot(x,y1,type="l",col="red")
lines(x,y2,col="green")
You can also use par and plot on the same graph but different axis. Something as follows:
plot( x, y1, type="l", col="red" )
par(new=TRUE)
plot( x, y2, type="l", col="green" )
If you read in detail about par in R, you will be able to generate really interesting graphs. Another book to look at is Paul Murrel's R Graphics.
When constructing multilayer plots one should consider ggplot package. The idea is to create a graphical object with basic aesthetics and enhance it incrementally.
ggplot style requires data to be packed in data.frame.
# Data generation
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x,1,1)
df <- data.frame(x,y1,y2)
Basic solution:
require(ggplot2)
ggplot(df, aes(x)) + # basic graphical object
geom_line(aes(y=y1), colour="red") + # first layer
geom_line(aes(y=y2), colour="green") # second layer
Here + operator is used to add extra layers to basic object.
With ggplot you have access to graphical object on every stage of plotting. Say, usual step-by-step setup can look like this:
g <- ggplot(df, aes(x))
g <- g + geom_line(aes(y=y1), colour="red")
g <- g + geom_line(aes(y=y2), colour="green")
g
g produces the plot, and you can see it at every stage (well, after creation of at least one layer). Further enchantments of the plot are also made with created object. For example, we can add labels for axises:
g <- g + ylab("Y") + xlab("X")
g
Final g looks like:
UPDATE (2013-11-08):
As pointed out in comments, ggplot's philosophy suggests using data in long format.
You can refer to this answer in order to see the corresponding code.
I think that the answer you are looking for is:
plot(first thing to plot)
plot(second thing to plot,add=TRUE)
Use the matplot function:
matplot(x, cbind(y1,y2),type="l",col=c("red","green"),lty=c(1,1))
use this if y1 and y2 are evaluated at the same x points. It scales the Y-axis to fit whichever is bigger (y1 or y2), unlike some of the other answers here that will clip y2 if it gets bigger than y1 (ggplot solutions mostly are okay with this).
Alternatively, and if the two lines don't have the same x-coordinates, set the axis limits on the first plot and add:
x1 <- seq(-2, 2, 0.05)
x2 <- seq(-3, 3, 0.05)
y1 <- pnorm(x1)
y2 <- pnorm(x2,1,1)
plot(x1,y1,ylim=range(c(y1,y2)),xlim=range(c(x1,x2)), type="l",col="red")
lines(x2,y2,col="green")
Am astonished this Q is 4 years old and nobody has mentioned matplot or x/ylim...
tl;dr: You want to use curve (with add=TRUE) or lines.
I disagree with par(new=TRUE) because that will double-print tick-marks and axis labels. Eg
The output of plot(sin); par(new=T); plot( function(x) x**2 ).
Look how messed up the vertical axis labels are! Since the ranges are different you would need to set ylim=c(lowest point between the two functions, highest point between the two functions), which is less easy than what I'm about to show you---and way less easy if you want to add not just two curves, but many.
What always confused me about plotting is the difference between curve and lines. (If you can't remember that these are the names of the two important plotting commands, just sing it.)
Here's the big difference between curve and lines.
curve will plot a function, like curve(sin). lines plots points with x and y values, like: lines( x=0:10, y=sin(0:10) ).
And here's a minor difference: curve needs to be called with add=TRUE for what you're trying to do, while lines already assumes you're adding to an existing plot.
Here's the result of calling plot(0:2); curve(sin).
Behind the scenes, check out methods(plot). And check body( plot.function )[[5]]. When you call plot(sin) R figures out that sin is a function (not y values) and uses the plot.function method, which ends up calling curve. So curve is the tool meant to handle functions.
if you want to split the plot into two columns (2 plots next to each other), you can do it like this:
par(mfrow=c(1,2))
plot(x)
plot(y)
Reference Link
As described by #redmode, you may plot the two lines in the same graphical device using ggplot. In that answer the data were in a 'wide' format. However, when using ggplot it is generally most convenient to keep the data in a data frame in a 'long' format. Then, by using different 'grouping variables' in the aesthetics arguments, properties of the line, such as linetype or colour, will vary according to the grouping variable, and corresponding legends will appear.
In this case, we can use the colour aessthetics, which matches colour of the lines to different levels of a variable in the data set (here: y1 vs y2). But first we need to melt the data from wide to long format, using e.g. the function 'melt' from reshape2 package. Other methods to reshape the data are described here: Reshaping data.frame from wide to long format.
library(ggplot2)
library(reshape2)
# original data in a 'wide' format
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x, 1, 1)
df <- data.frame(x, y1, y2)
# melt the data to a long format
df2 <- melt(data = df, id.vars = "x")
# plot, using the aesthetics argument 'colour'
ggplot(data = df2, aes(x = x, y = value, colour = variable)) + geom_line()
If you are using base graphics (i.e. not lattice/ grid graphics), then you can mimic MATLAB's hold on feature by using the points/lines/polygons functions to add additional details to your plots without starting a new plot. In the case of a multiplot layout, you can use par(mfg=...) to pick which plot you add things to.
You can use points for the overplot, that is.
plot(x1, y1,col='red')
points(x2,y2,col='blue')
Idiomatic Matlab plot(x1,y1,x2,y2) can be translated in R with ggplot2 for example in this way:
x1 <- seq(1,10,.2)
df1 <- data.frame(x=x1,y=log(x1),type="Log")
x2 <- seq(1,10)
df2 <- data.frame(x=x2,y=cumsum(1/x2),type="Harmonic")
df <- rbind(df1,df2)
library(ggplot2)
ggplot(df)+geom_line(aes(x,y,colour=type))
Inspired by Tingting Zhao's Dual line plots with different range of x-axis Using ggplot2.
Rather than keeping the values to be plotted in an array, store them in a matrix. By default the entire matrix will be treated as one data set. However if you add the same number of modifiers to the plot, e.g. the col(), as you have rows in the matrix, R will figure out that each row should be treated independently. For example:
x = matrix( c(21,50,80,41), nrow=2 )
y = matrix( c(1,2,1,2), nrow=2 )
plot(x, y, col("red","blue")
This should work unless your data sets are of differing sizes.
You could use the ggplotly() function from the plotly package to turn any of the gggplot2 examples here into an interactive plot, but I think this sort of plot is better without ggplot2:
# call Plotly and enter username and key
library(plotly)
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x, 1, 1)
plot_ly(x = x) %>%
add_lines(y = y1, color = I("red"), name = "Red") %>%
add_lines(y = y2, color = I("green"), name = "Green")
You can also create your plot using ggvis:
library(ggvis)
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x,1,1)
df <- data.frame(x, y1, y2)
df %>%
ggvis(~x, ~y1, stroke := 'red') %>%
layer_paths() %>%
layer_paths(data = df, x = ~x, y = ~y2, stroke := 'blue')
This will create the following plot:
Using plotly (adding solution from plotly with primary and secondary y axis- It seems to be missing):
library(plotly)
x <- seq(-2, 2, 0.05)
y1 <- pnorm(x)
y2 <- pnorm(x, 1, 1)
df=cbind.data.frame(x,y1,y2)
plot_ly(df) %>%
add_trace(x=~x,y=~y1,name = 'Line 1',type = 'scatter',mode = 'lines+markers',connectgaps = TRUE) %>%
add_trace(x=~x,y=~y2,name = 'Line 2',type = 'scatter',mode = 'lines+markers',connectgaps = TRUE,yaxis = "y2") %>%
layout(title = 'Title',
xaxis = list(title = "X-axis title"),
yaxis2 = list(side = 'right', overlaying = "y", title = 'secondary y axis', showgrid = FALSE, zeroline = FALSE))
Screenshot from working demo:
we can also use lattice library
library(lattice)
x <- seq(-2,2,0.05)
y1 <- pnorm(x)
y2 <- pnorm(x,1,1)
xyplot(y1 + y2 ~ x, ylab = "y1 and y2", type = "l", auto.key = list(points = FALSE,lines = TRUE))
For specific colors
xyplot(y1 + y2 ~ x,ylab = "y1 and y2", type = "l", auto.key = list(points = F,lines = T), par.settings = list(superpose.line = list(col = c("red","green"))))
Use curve for mathematical functions.
And use add=TRUE to use the same plot and axis.
curve( log2 , to=5 , col="black", ylab="log's(.)")
curve( log , add=TRUE , col="red" )
curve( log10, add=TRUE , col="blue" )
abline( h=0 )
I have following data and plot:
pos <- rep(1:2000, 20)
xv =c(rep(1:20, each = 2000))
# colrs <- unique(xv)
colrs <- xv # edits
yv =rnorm(2000*20, 0.5, 0.1)
xv = lapply(unique(xv), function(x) pos[xv==x])
to.add = cumsum(sapply(xv, max) + 1000)
bp <- c(xv[[1]], unlist(lapply(2:length(xv), function(x) xv[[x]] + to.add[x-1])))
plot (bp,yv, pch = "*", col = colrs)
I have few issues in this plot I could not figure out.
(1) I want to use different color for different group or two different color for different groups (i.e xv), but when I tried color function in terms to be beautiful mixture. Although I need to highlight some points (for example bp 4000 to 4500 for example with blue color)
(2) Instead of bp positions I want to put a tick mark and label with the group.
Thank you, appreciate your help.
Edits: with help of the following answer (with slight different approach in case I have unbalanced number in each group will work) I could get the similar plot. But still question remaining regarding colors is what if I want to use two alternate colors in alternate group ?
You can solve your colour issue by repeating the colour index however many times each group has a point plotted, like so:
plot (bp,yv, pch = "*", col = rep(colrs,each=2000))
The default colour palette (see ?palette or palette() ) will wrap around itself and you might want to specify your own to get 20 distinct colours.
To relabel the x axis, try plotting without the axis and then specifying the points and labels manually.
plot (bp,yv, pch = "*", col = rep(colrs,each=2000),xaxt="n")
axis(1,at=seq(1000,58000,3000),labels=1:20)
If you are trying to squeeze a lot of labels in there, you might have to shrink the text (cex.axis)or spin the labels 90 degrees (las=2).
plot (bp,yv, pch = "*", col = rep(colrs,each=2000),xaxt="n")
axis(1,at=seq(1000,58000,3000),labels=1:20,cex.axis=0.7,las=2)
Result:
One way is you could use a nested ifelse.
I'm still learning R, but one way it could be done would look something like:
plot(whatev$x, whatev$y, col=ifelse(xv<2000,red,ifelse(2000<xv & xv<4000,yellow,blue)))
You could nest as many of these as you want to have specificity on the colors and the intervals. The ifelse command is of form ifelse(TEST, True, False).
A simpler way would be to use the unique groups in xv to assign rainbow colors.
colrs=rainbow(length(unique(xv))) #Or colrs=rainbow(length(xv)) if xv is unique.
plot(whatev$x, whatev$y, col=colrs)
I hope I got all that right. I'm still learning R myself.
I'm going to go out on a limb and guess that your real data are something like 2000 values of things from 20 different groups. For instance, heights of 2000 plants of 20 different species. In such a case, you might want to look at the dotplot() function (or as illustrated below, dotplot.table()) in the lattice package.
Generate matrix of hypothetical values:
set.seed(1)
myY <- sapply( seq_len(20), function(x) rnorm(2000, x^(1/3)))
Transpose matrix to get groups as rows
myY <- t(myY)
Provide names of groups to matrix:
dimnames(myY)[[1]]<-paste("group", seq_len(nrow(myY)))
Load lattice package
library(lattice)
Generate dotplot
dotplot(myY, horizontal = FALSE, panel = function(x, y, horizontal, ...) {
panel.dotplot(x = x, y = y, horizontal = horizontal, jitter.x = TRUE,
col = seq_len(20)[x], pch = "*", cex = 1.5)
}, scales = list(x = list(rot = 90))
)
Which looks like (with unfortunate y-axis labeling):
Seeing that #JohnCLK is requesting a way of colouring by values on the x axis, I tried these demos in ggplot2-- each uses a dummy variable that is coded based on values or ranges to be highlighted in the other variables.
So, first set up the data, as in the question:
pos <- rep(1:2000, 20)
xv <- c(rep(1:20, each = 2000))
yv <- (2000*20, 0.5, 0.1)
xv <- lapply(unique(xv), function(x) pos[xv==x])
to.add <- cumsum(sapply(xv, max) + 1000)
bp <- c(xv[[1]], unlist(lapply(2:length(xv), function(x) xv[[x]] + to.add[x-1])))
Then load ggplot2, prepare a couple of utility functions, and set the default theme:
library("ggplot2")
make.png <- function(p, fName) {
png(fName, width=640, height=480, units="px")
print(p)
dev.off()
}
make.plot <- function(df) {
p <- ggplot(df,
aes(x = bp,
y = yv,
colour = highlight))
p <- p + geom_point()
p <- p + opts(legend.position = "none")
return(p)
}
theme_set( theme_bw() )
Draw a plot which highlights values in a defined range on the vertical axis:
# highlight a horizontal band
df <- data.frame(cbind(bp, yv))
df$highlight <- 0
df$highlight[ df$yv >= 0.4 & df$yv < 0.45 ] <- 1
p <- make.plot(df)
print(p)
make.png(p, "demo_horizontal.png")
Next draw a plot which highlights values in a defined range on the x axis, a vertical band:
# highlight a vertical band
df$highlight <- 0
df$highlight[ df$bp >= 38000 & df$bp < 42000 ] <- 1
p <- make.plot(df)
print(p)
make.png(p, "demo_vertical.png")
And finally draw a plot which highlights alternating vertical bands, by x value:
# highlight alternating bands
library("gtools")
alt.band.width <- 2000
df$highlight <- as.integer(df$bp / alt.band.width)
df$highlight <- ifelse(odd(df$highlight), 1, 0)
p <- make.plot(df)
print(p)
make.png(p, "demo_alternating.png")
Hope this helps; it was good practice anyway.