I am trying to plot my x-axis in the form of x^2 so that I get (1, 2, 4, 16) on my x-axis; and I would like these values to be equidistant. Does anyone know how to do this? Please let me know.
Many thanks
If you want an x-axis where x^2 values are equally spaced, then the tickmarks must be separated by sqrt(x), so you need a sqrt transformation. Here's a way to do this using ggplot:
set.seed(1) # for reprodubible example
df <- data.frame(x=1:25,y=rnorm(25))
library(ggplot2)
ggplot(df)+
geom_point(aes(x=x,y=y))+
scale_x_sqrt(breaks=c(1,4,9,16,25))
Related
For example, I want to plot this function y1 = function(x) 2*x/sqrt(x^2 + 1) in range (-2, 12)
But I tried using plot(y1(-2:12), type='l'), the plot comes out like a graph made up from several straight lines. How could I make it smoother?
Also, how could I define my own range of x and y shown on the plot? Thanks!
(sorry I can't insert any pictures of my plot because lacking reputation points...)
Use the curve function:
curve(2*x/sqrt(x^2 + 1), -2, 12)
To create a parallel coordinate plot I wanted to use ggparcoord() function in package GGally. The following codes show a reproducible example.
set.seed(3674)
k <- rep(1:3, each=30)
x <- k + rnorm(mean=10, sd=.2,n=90)
y <- -2*k + rnorm(mean=10, sd=.4,n=90)
z <- 3*k + rnorm(mean=10, sd=.6,n=90)
dat <- data.frame(group=factor(k),x,y,z)
library(GGally)
ggparcoord(dat,columns=1:4,groupColumn = 1)
Notice in the picture that the color for group was continuous even though I have the group variable as a factor. Is there any way I can display the plot with three discrete color instead?
I have looked at some other posts where they discuss various other ways of doing parallel coordinate plots in here. But I really wanted to do this in ggparcoord() function of package GGally. I appreciate your time in thinking about this problem.
Your code was almost correct. I spotted that columns=1:4 was not right in this case. You need to drop the column for groupColumn in columns
ggparcoord(dat,columns=2:4,groupColumn = 1)
Here is my data:
t2=[89221345,87542657,90886527]
t1=[0.5,0.5,0.1]
I need to plot a graph so that the x axis has labels set to the values in t1 and the y axis are the corresponding t2 values. The x axis correspond to VIN numbers, so they need to be evenly spaced, and discrete points on the axis that are labaled. When i try plotting them by using plot(t2,t1), I see three big blocks on screen that don't make any sense to me.
Thanks!!
How about this?
require(ggplot2)
t2<-c(89221345,87542657,90886527)
t1<-c(0.5,0.5,0.1)
qplot(factor(t2),t1)+geom_point(size=5)+
theme(axis.text.x=element_text(angle=-90))
See this article.
t1=c(0.5,0.5,0.1)
t2=c(89221345,87542657,90886527)
plot(1:3,t2,xaxt="n",xlab="VIN")
axis(1,at=1:3,labels=t1)
I'm assuming that t2 are your VINs. Start of by defining the data like R would expect:
t2 <- c(89221345, 87542657, 90886527)
t1 <- c(0.5, 0.5, 0.1)
plot(t2,t1,xlab="VIN",ylab="???")
This will generate a plot with points at (89221345, 0.5), (87542657, 0.5), and (90886527, 0.1). The plot() command is looking for at minimum a vector of x values (first argument) and a vector of y values (second argument).
I have this problem. I got a heatmap, (but i suppose this applies to every plot) but I need to mirror my y-axis.
I got here some example code:
library(gstat)
x <- seq(1,50,length=50)
y <- seq(1,50,length=50)
z <- rnorm(1000)
df <- data.frame(x=x,y=y,z=z)
image(df,col=heat.colors(256))
This will generate the following heatmap
But I need the y-axis mirrored. Starting with 0 on the top and 50 on the bottom. Does anybody has a clue as to what I must do to change this?
See the help page for ?plot.default, which specifies
xlim: the x limits (x1, x2) of the plot. Note that ‘x1 > x2’ is
allowed and leads to a ‘reversed axis’.
library(gstat)
x <- seq(1,50,length=50)
y <- seq(1,50,length=50)
z <- rnorm(1000)
df <- data.frame(x=x,y=y,z=z)
So
image(df,col=heat.colors(256), ylim = rev(range(y)))
Does this work for you (it's a bit of a hack, though)?
df2<-df
df2$y<-50-df2$y #reverse oredr
image(df2,col=heat.colors(256),yaxt="n") #avoid y axis
axis(2, at=c(0,10,20,30,40,50), labels=c(50,40,30,20,10,0)) #draw y axis manually
The revaxis function in the plotrix package "reverses the sense of either or both the ‘x’ and ‘y’ axes". It doesn't solve your problem (Nick's solution is the correct one) but can be useful when you need to plot a scatterplot with reversed axes.
I would use rev like so:
df <- data.frame(x=x,y=rev(y),z=z)
In case you were not aware, notice that df is actually a function. You might want to be careful when overwriting. If you rm(df), things will go back to normal.
Don't forget to relabel the y axis as Nick suggests.
For the vertical axis increasing in the downward direction, I provided two ways (two different answers) for the following question:
R - image of a pixel matrix?
I have come across a number of situations where I want to plot more points than I really ought to be -- the main holdup is that when I share my plots with people or embed them in papers, they occupy too much space. It's very straightforward to randomly sample rows in a dataframe.
if I want a truly random sample for a point plot, it's easy to say:
ggplot(x,y,data=myDf[sample(1:nrow(myDf),1000),])
However, I was wondering if there were more effective (ideally canned) ways to specify the number of plot points such that your actual data is accurately reflected in the plot. So here is an example.
Suppose I am plotting something like the CCDF of a heavy tailed distribution, e.g.
ccdf <- function(myList,density=FALSE)
{
# generates the CCDF of a list or vector
freqs = table(myList)
X = rev(as.numeric(names(freqs)))
Y =cumsum(rev(as.list(freqs)));
data.frame(x=X,count=Y)
}
qplot(x,count,data=ccdf(rlnorm(10000,3,2.4)),log='xy')
This will produce a plot where the x & y axis become increasingly dense. Here it would be ideal to have fewer samples plotted for large x or y values.
Does anybody have any tips or suggestions for dealing with similar issues?
Thanks,
-e
I tend to use png files rather than vector based graphics such as pdf or eps for this situation. The files are much smaller, although you lose resolution.
If it's a more conventional scatterplot, then using semi-transparent colours also helps, as well as solving the over-plotting problem. For example,
x <- rnorm(10000); y <- rnorm(10000)
qplot(x, y, colour=I(alpha("blue",1/25)))
Beyond Rob's suggestions, one plot function I like as it does the 'thinning' for you is hexbin; an example is at the R Graph Gallery.
Here is one possible solution for downsampling plot with respect to the x-axis, if it is log transformed. It log transforms the x-axis, rounds that quantity, and picks the median x value in that bin:
downsampled_qplot <- function(x,y,data,rounding=0, ...) {
# assumes we are doing log=xy or log=x
group = factor(round(log(data$x),rounding))
d <- do.call(rbind, by(data, group,
function(X) X[order(X$x)[floor(length(X)/2)],]))
qplot(x,count,data=d, ...)
}
Using the definition of ccdf() from above, we can then compare the original plot of the CCDF of the distribution with the downsampled version:
myccdf=ccdf(rlnorm(10000,3,2.4))
qplot(x,count,data=myccdf,log='xy',main='original')
downsampled_qplot(x,count,data=myccdf,log='xy',rounding=1,main='rounding = 1')
downsampled_qplot(x,count,data=myccdf,log='xy',rounding=0,main='rounding = 0')
In PDF format, the original plot takes up 640K, and the downsampled versions occupy 20K and 8K, respectively.
I'd either make image files (png or jpeg devices) as Rob already mentioned, or I'd make a 2D histogram. An alternative to the 2D histogram is a smoothed scatterplot, it makes a similar graphic but has a more smooth cutoff from dense to sparse regions of space.
If you've never seen addictedtor before, it's worth a look. It has some very nice graphics generated in R with images and sample code.
Here's the sample code from the addictedtor site:
2-d histogram:
require(gplots)
# example data, bivariate normal, no correlation
x <- rnorm(2000, sd=4)
y <- rnorm(2000, sd=1)
# separate scales for each axis, this looks circular
hist2d(x,y, nbins=50, col = c("white",heat.colors(16)))
rug(x,side=1)
rug(y,side=2)
box()
smoothscatter:
library("geneplotter") ## from BioConductor
require("RColorBrewer") ## from CRAN
x1 <- matrix(rnorm(1e4), ncol=2)
x2 <- matrix(rnorm(1e4, mean=3, sd=1.5), ncol=2)
x <- rbind(x1,x2)
layout(matrix(1:4, ncol=2, byrow=TRUE))
op <- par(mar=rep(2,4))
smoothScatter(x, nrpoints=0)
smoothScatter(x)
smoothScatter(x, nrpoints=Inf,
colramp=colorRampPalette(brewer.pal(9,"YlOrRd")),
bandwidth=40)
colors <- densCols(x)
plot(x, col=colors, pch=20)
par(op)