I am trying to control how many z labels should be written in my contour plot plotted with contourplot() from the lattice library.
I have 30 contour lines but I only want the first 5 to be labelled. I tried a bunch of things like
contourplot(z ~ z+y, data=d3, cuts=30, font=3, xlab="x axis", ylab="y axis", scales=list(at=seq(2,10,by=2)))
contourplot(z ~ z+y, data=d3, cuts=30, font=3, xlab="x axis", ylab="y axis", at=seq(2,10,by=2))
but nothing works.
Also, is it possible to plot two contourplot() on the same graph? I tried
contourplot(z ~ z+y, data=d3, cuts=30)
par(new=T)
contourplot(z ~ z+y, data=d3, cuts=20)
but it's not working.
Thanks!
Here is my take:
library(lattice)
x <- rep(seq(-1.5,1.5,length=50),50)
y <- rep(seq(-1.5,1.5,length=50),rep(50,50))
z <- exp(-(x^2+y^2+x*y))
# here is default plot
lp1 <- contourplot(z~x*y)
# here is an enhanced one
my.panel <- function(at, labels, ...) {
# draw odd and even contour lines with or without labels
panel.contourplot(..., at=at[seq(1, length(at), 2)], col="blue", lty=2)
panel.contourplot(..., at=at[seq(2, length(at), 2)], col="red",
labels=as.character(at[seq(2, length(at), 2)]))
}
lp2 <- contourplot(z~x*y, panel=my.panel, at=seq(0.2, 0.8, by=0.2))
lp3 <- update(lp2, at=seq(0.2,0.8,by=0.1))
lp4 <- update(lp3, lwd=2, label.style="align")
library(gridExtra)
grid.arrange(lp1, lp2, lp3, lp4)
You can adapt the custom panel function to best suit your needs (e.g. other scale for leveling the z-axis, color, etc.).
You can specify the labels as a character vector argument and set the last values with rep("", 5), so perhaps for the example you offered on an earlier question about contour
x = seq(0, 10, by = 0.5)
y = seq(0, 10, by = 0.5)
z <- outer(x, y)
d3 <- expand.grid(x=x,y=y); d3$z <- as.vector(z)
contourplot(z~x+y, data=d3)
# labeled '5'-'90'
contourplot(z~x+y, data=d3,
at=seq(5,90, by=5),
labels=c(seq(5,25, by=5),rep("", 16) ),
main="Labels only at the first 5 contour lines")
# contourplot seems to ignore 'extra' labels
# c() will coerce the 'numeric' elements to 'character' if any others are 'character'
?contourplot # and follow the link in the info about labels to ?panel.levelplot
Related
I need this type of figure:
I need x on x axis, y on y axis and fx and fy in ploy area. Could you please help me in R i.e. both curves intersect to each other. My code is
gx <- expand.grid(x=seq(1,5,length=50))
fx <- function(x) { exp(-x) }
gx$fx <- apply(gx,1,fx)
plot(gx, type="l",col="red")
gy <- expand.grid(y=seq(1,5,length=50))
fy <- function(y) { y*exp(-y) }
gy$fy <- apply(gy, 1, fy)
par(new=TRUE)
plot(gy, type="l", col="green")
Not 100% sure I understand what this question means, but if you are looking to label your axes you can use the xlab and ylab graphical parameters:
ie:
plot(gx, type="l",col="red", xlab="label for x axis", ylab="label for y axis")
Here's the plot with base:
plot(gx, type="n", xlab="", ylab="")
for(i in 1:2) lines(get(c("gx", "gy")[i]), col=c("red", "green")[i])
title(xlab="x", ylab="y")
I personally prefer to do a little bit more data-manipulation to combine both data (gx and gy) into a single data.frame in long-form using "dplyr" and "tidyr" packages :
dat <- data.frame(gx, gy)
dat <- dat %>%
gather(xvar, x, x,y) %>%
gather(yvar, y, fx, fy)
head(dat)
# xvar x yvar y
# 1 x 1.000000 fx 0.3678794
# 2 x 1.081633 fx 0.3390415
# 3 x 1.163265 fx 0.3124642
# 4 x 1.244898 fx 0.2879703
# 5 x 1.326531 fx 0.2653964
# 6 x 1.408163 fx 0.2445921
This will make it easy to visualise with ggplot:
ggplot(dat, aes(x,y, col=yvar)) + geom_line()
Use of legend and lines functions along with plot would give the following figure.
plot(gx,type="l",col="red", xlab ="x", ylab="y")
lines(gy,col="green")
legend("topright", inset=.05, cex = 1, c("f(x)","f(y)"), horiz=TRUE, lty=c(1,1), lwd=c(2,2), col=c("red","green"), text.font=3)
It doesn't seem anyone so far has answered about putting f(x) and f(y) in the plot area. You can do this with text(), though it's not the most elegant solution because you have to manually give the coordinates of where to place the text.
The data you gave:
gx <- expand.grid(x=seq(1,5,length=50))
fx <- function(x) { exp(-x) }
gx$fx <- apply(gx,1,fx)
gy <- expand.grid(y=seq(1,5,length=50))
fy <- function(y) { y*exp(-y) }
gy$fy <- apply(gy, 1, fy)
Creating the plot:
plot(gx,type="l",col="red", xlab ="x", ylab="y")
lines(gy,col="green")
text(x=c(3,2),y=c(0.18,0.1), labels=c("f(y)","f(x)"))
Gives this:
I hope this helps! The part that is confusing about your question is when you say that you need the lines to intersect. That's an issue with your data and/or the functions you are applying, and not one we can answer without some clarity.
Is there a way to reproduce the following plot in R?
EDIT
This is what I could do with persp() in base R and plot_ly in plotly. Also a bit ugly.
x <- seq(0,1,0.01)
y <- seq(0,1,0.01)
f <- function(x,y){ z <- -x - y + 1 }
z <- outer(x,y,f)
z <- ifelse(z<0,NA,z)
persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue")
plot_ly(x=x,y=y,z=z,type="surface") %>% layout(xaxis=list(range=c(0,1)), yaxis=list(range=c(0,1)), zaxis=list(range=c(0,1)))
BTW...the matplotlib plots were obtained here:
http://blog.bogatron.net/blog/2014/02/02/visualizing-dirichlet-distributions/
Using persp in base R I was able to get this far:
persp(0:1, 0:1,
matrix(c(1,0,0,NA), nrow=2),
col="green", theta=60,
xlab= "theta_1",
ylab = "theta_2",
zlab="theata_3")
But I could not figure out how to do a few things, including greek symbols on axes.
I am turning this into a wiki in case any persp experts out there want to finish the job.
This is a little ugly/still incomplete but at least shows one way to get Greek labels in.
pp <- persp(0:1, 0:1,
matrix(c(2,0,0,NA), nrow=2),
col="green", theta=60,
xlab= "",
ylab ="",
zlab="",
ticktype="detailed",
nticks=1)
text(trans3d(0.5,-0.1,-0.1,pp),labels=expression(theta[1]))
I'm trying to create a scatterplot with marginal histograms as in this question.
My data are two (numeric) variables which share seven discrete (somewhat) logarithmically-spaced levels.
I've successfully done this with the help of ggMarginal in the ggExtra package, however I'm not happy with the outcome as when plotting the marginal histograms using the same data as for the scatterplots, things don't line up.
As can be seen below, the histogram bars are biased a little to the right or left of the datapoints themselves.
library(ggMarginal)
library(ggplot2)
x <- rep(log10(c(1,2,3,4,5,6,7)), times=c(3,7,12,18,12,7,3))
y <- rep(log10(c(1,2,3,4,5,6,7)), times=c(3,1,13,28,13,1,3))
d <- data.frame("x" = x,"y" = y)
p1 <- ggMarginal(ggplot(d, aes(x,y)) + geom_point() + theme_bw(), type = "histogram")
A possible solution for this may be change the variables used in the histograms into factors, so they are nicely aligned with the scatterplot axes.
This works well when creating histograms using ggplot:
p2 <- ggplot(data.frame(lapply(d, as.factor)), aes(x = x)) + geom_histogram()
However, when I try to do this using ggMarginal, I do not get the desired result - it appears that the ggMarginal histogram is still treating my variables as numeric.
p3 <- ggMarginal(ggplot(d, aes(x,y)) + geom_point() + theme_bw(),
x = as.factor(x), y = as.factor(y), type = "histogram")
How can I ensure my histogram bars are centred over the data points?
I'm absolutely willing to accept an answer which does not involve use of ggMarginal.
Not sure if it is a good idea to replicate here the answer I gave to the question you mentioned but I have no rights to comment still, please let me know otherwise.
I've found the package (ggpubr) that seems to work very well for this problem and it considers several possibilities to display the data.
The link to the package is here, and in this link you will find a nice tutorial to use it. For completeness, I attach one of the examples I reproduced.
I first installed the package (it requires devtools)
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
For the particular example of displaying different histograms for different groups, it mentions in relation with ggExtra: "One limitation of ggExtra is that it can’t cope with multiple groups in the scatter plot and the marginal plots. In the R code below, we provide a solution using the cowplot package." In my case, I had to install the latter package:
install.packages("cowplot")
And I followed this piece of code:
# Scatter plot colored by groups ("Species")
sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
color = "Species", palette = "jco",
size = 3, alpha = 0.6)+
border()
# Marginal density plot of x (top panel) and y (right panel)
xplot <- ggdensity(iris, "Sepal.Length", fill = "Species",
palette = "jco")
yplot <- ggdensity(iris, "Sepal.Width", fill = "Species",
palette = "jco")+
rotate()
# Cleaning the plots
sp <- sp + rremove("legend")
yplot <- yplot + clean_theme() + rremove("legend")
xplot <- xplot + clean_theme() + rremove("legend")
# Arranging the plot using cowplot
library(cowplot)
plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv",
rel_widths = c(2, 1), rel_heights = c(1, 2))
Which worked fine for me:
If you are willing to give baseplotting a try, here is a function:
plots$scatterWithHists <- function(x, y, histCols=c("lightblue","lightblue"), lhist=20, xlim=range(x), ylim=range(y), ...){
## set up layout and graphical parameters
layMat <- matrix(c(1,4,3,2), ncol=2)
layout(layMat, widths=c(5/7, 2/7), heights=c(2/7, 5/7))
ospc <- 0.5 # outer space
pext <- 4 # par extension down and to the left
bspc <- 1 # space between scatter plot and bar plots
par. <- par(mar=c(pext, pext, bspc, bspc), oma=rep(ospc, 4)) # plot parameters
## barplot and line for x (top)
xhist <- hist(x, breaks=seq(xlim[1], xlim[2], length.out=lhist), plot=FALSE)
par(mar=c(0, pext, 0, 0))
barplot(xhist$density, axes=FALSE, ylim=c(0, max(xhist$density)), space=0, col=histCols[1])
## barplot and line for y (right)
yhist <- hist(y, breaks=seq(ylim[1], ylim[2], length.out=lhist), plot=FALSE)
par(mar=c(pext, 0, 0, 0))
barplot(yhist$density, axes=FALSE, xlim=c(0, max(yhist$density)), space=0, col=histCols[2], horiz=TRUE)
## overlap
dx <- density(x)
dy <- density(y)
par(mar=c(0, 0, 0, 0))
plot(dx, col=histCols[1], xlim=range(c(dx$x, dy$x)), ylim=range(c(dx$y, dy$y)),
lwd=4, type="l", main="", xlab="", ylab="", yaxt="n", xaxt="n", bty="n"
)
points(dy, col=histCols[2], type="l", lwd=3)
## scatter plot
par(mar=c(pext, pext, 0, 0))
plot(x, y, xlim=xlim, ylim=ylim, ...)
}
Just do:
scatterWithHists(x,y, histCols=c("lightblue","orange"))
And you get:
If you absolutely want to use ggMargins then look up xparams and yparams. It says you can send additional arguments to x-margin and y-margin using those. I was only successful in sending trivial things like color. But maybe sending something like xlim would help.
the type argument to xyplot() can take "s" for "steps." From help(plot):
The two step types differ in their x-y preference: Going from
(x1,y1) to (x2,y2) with x1 < x2, 'type = "s"' moves first
horizontal, then vertical, whereas 'type = "S"' moves the other
way around.
i.e. if you use type="s", the horizontal part of the step has its left end attached to the data point, while type="S" has its right end attached to the data point.
library(lattice)
set.seed(12345)
num.points <- 10
my.df <- data.frame(x=sort(sample(1:100, num.points)),
y=sample(1:40, num.points, replace=TRUE))
xyplot(y~x, data=my.df, type=c("p","s"), col="blue", main='type="s"')
xyplot(y~x, data=my.df, type=c("p","S"), col="red", main='type="S"')
How could one achieve a "step" plot, where the vertical motion happens between data points points, i.e. at x1 + (x2-x1)/2, so that the horizontal part of the step is centered on the data point?
Edited to include some example code. better late than never I suppose.
I am using excellent #nico answer to give its lattice version. Even I am ok with #Dwin because the question don't supply a reproducible example, but customizing lattice panel is sometimes challenging.
The idea is to use panel.segments which is the equivalent of segments of base graphics.
library(lattice)
xyplot(y~x,
panel =function(...){
ll <- list(...)
x <- ll$x
y <- ll$y
x.start <- x - (c(0, diff(x)/2))
x.end <- x + (c(diff(x)/2, 0))
panel.segments(x.start, y, x.end, y, col="orange", lwd=2)
panel.segments(x.end[-length(x.end)], y[1:(length(y)-1)],
x.end[-length(x.end)], y[-1], col="orange", lwd=2)
## this is optional just to compare with type s
panel.xyplot(...,type='s')
## and type S
panel.xyplot(...,type='S')
})
This is a base graphics solution, as I am not too much of an expert in lattice.
Essentially you can use segments to draw first the horizontal, then the vertical steps, passing the shifted coordinates as a vector.
Here is an example:
set.seed(12345)
# Generate some data
num.points <- 10
x <- sort(sample(1:100, num.points))
y <- sample(1:40, num.points, replace=T)
# Plot the data with style = "s" and "S"
par(mfrow=c(1,3))
plot(x, y, "s", col="red", lwd=2, las=1,
main="Style: 's'", xlim=c(0, 100))
points(x, y, pch=19, col="red", cex=0.8)
plot(x, y, "S", col="blue", lwd=2, las=1,
main="Style: 'S'", xlim=c(0, 100))
points(x, y, pch=19, col="blue", cex=0.8)
# Now plot our points
plot(x, y, pch=19, col="orange", cex=0.8, las=1,
main="Centered steps", xlim=c(0, 100))
# Calculate the starting and ending points of the
# horizontal segments, by shifting the x coordinates
# by half the difference with the next point
# Note we leave the first and last point as starting and
# ending points
x.start <- x - (c(0, diff(x)/2))
x.end <- x + (c(diff(x)/2, 0))
# Now draw the horizontal segments
segments(x.start, y, x.end, y, col="orange", lwd=2)
# and the vertical ones (no need to draw the last one)
segments(x.end[-length(x.end)], y[1:(length(y)-1)],
x.end[-length(x.end)], y[-1], col="orange", lwd=2)
Here is the result:
I was just wondering if there is a way to get rid of axis values, either the x-axis or y-axis respectively, in an r-plot graph.
I know that axes = false will get rid of the entire axis, but I would only like to get rid of the numbering.
Remove numbering on x-axis or y-axis:
plot(1:10, xaxt='n')
plot(1:10, yaxt='n')
If you want to remove the labels as well:
plot(1:10, xaxt='n', ann=FALSE)
plot(1:10, yaxt='n', ann=FALSE)
Using base graphics, the standard way to do this is to use axes=FALSE, then create your own axes using Axis (or axis). For example,
x <- 1:20
y <- runif(20)
plot(x, y, axes=FALSE, frame.plot=TRUE)
Axis(side=1, labels=FALSE)
Axis(side=2, labels=FALSE)
The lattice equivalent is
library(lattice)
xyplot(y ~ x, scales=list(alternating=0))
#Richie Cotton has a pretty good answer above. I can only add that this page provides some examples. Try the following:
x <- 1:20
y <- runif(20)
plot(x,y,xaxt = "n")
axis(side = 1, at = x, labels = FALSE, tck = -0.01)
you can also put labels inside plot:
plot(spline(sub$day, sub$counts), type ='l', labels = FALSE)
you'll get a warning. i think this is because labels is actually a parameter that's being passed down to a subroutine that plot runs (axes?). the warning will pop up because it wasn't directly a parameter of the plot function.
Change the axis_colour to match the background and if you are modifying the background dynamically you will need to update the axis_colour simultaneously.
* The shared picture shows the graph/plot example using mock data ()
### Main Plotting Function ###
plotXY <- function(time, value){
### Plot Style Settings ###
### default bg is white, set it the same as the axis-colour
background <- "white"
### default col.axis is black, set it the same as the background to match
axis_colour <- "white"
plot_title <- "Graph it!"
xlabel <- "Time"
ylabel <- "Value"
label_colour <- "black"
label_scale <- 2
axis_scale <- 2
symbol_scale <- 2
title_scale <- 2
subtitle_scale <- 2
# point style 16 is a black dot
point <- 16
# p - points, l - line, b - both
plot_type <- "b"
plot(time, value, main=plot_title, cex=symbol_scale, cex.lab=label_scale, cex.axis=axis_scale, cex.main=title_scale, cex.sub=subtitle_scale, xlab=xlabel, ylab=ylabel, col.lab=label_colour, col.axis=axis_colour, bg=background, pch=point, type=plot_type)
}
plotXY(time, value)