I am working on a script where I need to calculate the coordinates for a beeswarm plot without immediately plotting. When I use beeswarm, I get x-coordinates that aren't swarmed, and more or less the same value:
But if I generate the same plot again it swarms correctly:
And if I use dev.off() I again get no swarming:
The code I used:
n <- 250
df = data.frame(x = floor(runif(n, 0, 5)),
y = rnorm(n = n, mean = 500, sd = 100))
#Plot 1:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
#Plot 2:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
dev.off()
#Plot 3:
A = with(df, beeswarm(y ~ x, do.plot = F))
plot(x = A$x, y=A$y)
It seems to me like beeswarm uses something like the current plot parameters (or however it is called) to do the swarming and therefore chokes when a plot isn't showing. I have tried to play around with beeswarm parameters such as spacing, breaks, corral, corralWidth, priority, and xlim, but it does not make a difference. FYI: If do.plot is set to TRUE the x-coordinates are calculated correctly, but this is not helpful as I don't want to plot immediately.
Any tips or comments are greatly appreciated!
You're right; beeswarm uses the current plot parameters to calculate the amount of space to leave between points. It seems that setting "do.plot=FALSE" does not do what one would expect, and I'm not sure why I included this parameter.
If you want to control the parameters manually, you could use the functions swarmx or swarmy instead. These functions must be applied to each group separately, e.g.
dfsplitswarmed <- by(df, df$x, function(aa) swarmx(aa$x, aa$y, xsize = 0.075, ysize = 7.5, cex = 1, log = ""))
dfswarmed <- do.call(rbind, dfsplitswarmed)
plot(dfswarmed)
In this case, I set the xsize and ysize values based on what the function would default to for this particular data set. If you can find a set of xsize/ysize values that work for your data, this approach might work for you.
Otherwise, perhaps a simpler approach would be to leave do.plot=TRUE, and then discard the plots.
Related
I tried to run the following script to change the x axis. I followed other answers given in Stack Overflow; however I don’t get what I’m looking for. The original x axis goes from 0 to 1 but I want to change it from 0 to 20.
x<-c(0,0.1,0.2,0.25,0.30,0.35,0.40,0.60,0.90,1)
y<-c(0,0.014,0.41165,0.258,0.57,0.57,0.1803,0.5844,0.10185,0.085)
da<-cbind(x,y)
dat=data.frame(da)
plot(y~x,data=dat,xaxt="n")
pas=c(0,1,2,3,4,5,6,7,8,10,11,12,13,14,15,16,17,18,19,20)
axis(1, at=1:20,labels=pas)
This script produces a figure with just a 0 at the last mark. I need a x axis from 0 to 20. Thanks
I am not sure why you are trying to do this but the following would work.
x <- c(0,0.1,0.2,0.25,0.30,0.35,0.40,0.60,0.90,1)
y <- c(0,0.014,0.41165,0.258,0.57,0.57,0.1803,0.5844,0.10185,0.085)
dat <- data.frame(x = x, y = y)
x.range <- c(0,20)
x.labels <- x.range[1]:x.range[2]
pas <- seq(0, 1, length.out = length(x.labels))
plot(y ~ x, data = dat, xaxt = "n", xlim = c(0,1))
axis(1, at = pas, labels = x.labels, cex.axis=0.65)
You may have missed the xlim/ylim.
Did you mean y axis? Then just use :
plot(y~x,data=dat,ylim=c(0,20))
plot(y~x,data=dat,xlim=c(0,20))# you need to remove the 'xaxt="n"' to see the x-axis
Although either way I am not sure this is the best idea given the range of the data.
Using ggplot2
library(ggplot2)
qplot(x,y,data=dat)+ scale_x_continuous(labels=c("0"="fake0","0.25"="fake5","0.5"="fake10",".75"="fake15","1"="fake20"))# replace 'fake#' with anything
Although I am not sure why are you trying to do this.
I am needing to produce normally distributed density plots with different total areas (summing to 1). Using the following function, I can specify the lambda - which gives the relative area:
sdnorm <- function(x, mean=0, sd=1, lambda=1){lambda*dnorm(x, mean=mean, sd=sd)}
I then want to plot up the function using different parameters. Using ggplot2, this code works:
require(ggplot2)
qplot(x, geom="blank") + stat_function(fun=sdnorm,args=list(mean=8,sd=2,lambda=0.7)) +
stat_function(fun=sdnorm,args=list(mean=18,sd=4,lambda=0.30))
but I really want to do this in base R graphics, for which I think I need to use the "curve" function. However, I am struggling to get this to work.
If you take a look at the help file for ? curve, you'll see that the first argument can be a number of different things:
The name of a function, or a call or an expression written as a function of x which will evaluate to an object of the same length as x.
This means you can specify the first argument as either a function name or an expression, so you could just do:
curve(sdnorm)
to get a plot of the function with its default arguments. Otherwise, to recreate your ggplot2 representation you would want to do:
curve(sdnorm(x, mean=8,sd=2,lambda=0.7), from = 0, to = 30)
curve(sdnorm(x, mean=18,sd=4,lambda=0.30), add = TRUE)
The result:
You can do the following in base R
x <- seq(0, 50, 1)
plot(x, sdnorm(x, mean = 8, sd = 2, lambda = 0.7), type = 'l', ylab = 'y')
lines(x, sdnorm(x, mean = 18, sd = 4, lambda = 0.30))
EDIT I added ylab = 'y' and updated the picture to have the y-axis re-labeled.
This should get you started.
I am trying to visualize some data and in order to do it I am using R's hist.
Bellow are my data
jancoefabs <- as.numeric(as.vector(abs(Janmodelnorm$coef)))
jancoefabs
[1] 1.165610e+00 1.277929e-01 4.349831e-01 3.602961e-01 7.189458e+00
[6] 1.856908e-04 1.352052e-05 4.811291e-05 1.055744e-02 2.756525e-04
[11] 2.202706e-01 4.199914e-02 4.684091e-02 8.634340e-01 2.479175e-02
[16] 2.409628e-01 5.459076e-03 9.892580e-03 5.378456e-02
Now as the more cunning of you might have guessed these are the absolute values of some model's coefficients.
What I need is an histogram that will have for axes:
x will be the number (count or length) of coefficients which is 19 in total, along with their names.
y will show values of each column (as breaks?) having a ylim="" set, according to min and max of those values (or something similar).
Note that Janmodelnorm$coef simply produces the following
(Intercept) LON LAT ME RAT
1.165610e+00 -1.277929e-01 -4.349831e-01 -3.602961e-01 -7.189458e+00
DS DSA DSI DRNS DREW
-1.856908e-04 1.352052e-05 4.811291e-05 -1.055744e-02 -2.756525e-04
ASPNS ASPEW SI CUR W_180_270
-2.202706e-01 -4.199914e-02 4.684091e-02 -8.634340e-01 -2.479175e-02
W_0_360 W_90_180 W_0_180 NDVI
2.409628e-01 5.459076e-03 -9.892580e-03 -5.378456e-02
So far and consulting ?hist, I am trying to play with the code bellow without success. Therefore I am taking it from scratch.
# hist(jancoefabs, col="lightblue", border="pink",
# breaks=8,
# xlim=c(0,10), ylim=c(20,-20), plot=TRUE)
When plot=FALSE is set, I get a bunch of somewhat useful info about the set. I also find hard to use breaks argument efficiently.
Any suggestion will be appreciated. Thanks.
Rather than using hist, why not use a barplot or a standard plot. For example,
## Generate some data
set.seed(1)
y = rnorm(19, sd=5)
names(y) = c("Inter", LETTERS[1:18])
Then plot the cofficients
barplot(y)
Alternatively, you could use a scatter plot
plot(1:19, y, axes=FALSE, ylim=c(-10, 10))
axis(2)
axis(1, 1:19, names(y))
and add error bars to indicate the standard errors (see for example Add error bars to show standard deviation on a plot in R)
Are you sure you want a histogram for this? A lattice barchart might be pretty nice. An example with the mtcars built-in data set.
> coef <- lm(mpg ~ ., data = mtcars)$coef
> library(lattice)
> barchart(coef, col = 'lightblue', horizontal = FALSE,
ylim = range(coef), xlab = '',
scales = list(y = list(labels = coef),
x = list(labels = names(coef))))
A base R dotchart might be good too,
> dotchart(coef, pch = 19, xlab = 'value')
> text(coef, seq(coef), labels = round(coef, 3), pos = 2)
I want to plot two different data sets in a scatterplot matrix.
I know that I can use upper.panel and lower.panel to differentiate the plot function. However, I don’t succeed in putting my data in a suitable format to harness this.
Assume I have two tissues (“brain” and “heart”) and four conditions (1–4). Now I can use e.g. pairs(data$heart) to get a scatterplot matrix for one of the data sets. Assume I have the following data:
conditions <- 1 : 4
noise <- rnorm(100)
data <- list(brain = sapply(conditions, function (x) noise + 0.1 * rnorm(100)),
heart = sapply(conditions, function (x) noise + 0.3 * rnorm(100)))
How do I get this into a format so that pairs(data, …) plots one data set above and one below the diagonal, as shown here (green = brain, violet = heart):
Just using
pairs(data, upper.panel = something, lower.panel = somethingElse)
Doesn’t work because that will plot all conditions versus all conditions without regard for different tissue – it essentially ignores the list, and the same when reordering the hierarchy (i.e. having data = (A=list(brain=…, heart=…), B=list(brain=…, heart=…), …)).
This is the best I seem to be able to do via passing arguments:
foo.upper <- function(x,y,ind.upper,col.upper,ind.lower,col.lower,...){
points(x[ind.upper],y[ind.upper],col = col.upper,...)
}
foo.lower <- function(x,y,ind.lower,col.lower,ind.upper,col.upper,...){
points(x[ind.lower],y[ind.lower],col = col.lower,...)
}
pairs(dat[,-5],
lower.panel = foo.lower,
upper.panel = foo.upper,
ind.upper = dat$type == 'brain',
ind.lower = dat$type == 'heart',
col.upper = 'blue',
col.lower = 'red')
Note that each panel needs all arguments. ... is a cruel mistress. If you include only the panel specific arguments in each function, it appears to work, but you get lots and lots of warnings from R trying to pass these arguments on to regular plotting functions and obviously they won't exist.
This was my quick first attempt, but it seems ugly:
dat <- as.data.frame(do.call(rbind,data))
dat$type <- rep(c('brain','heart'),each = 100)
foo.upper <- function(x,y,...){
points(x[dat$type == 'brain'],y[dat$type == 'brain'],col = 'red',...)
}
foo.lower <- function(x,y,...){
points(x[dat$type == 'heart'],y[dat$type == 'heart'],col = 'blue',...)
}
pairs(dat[,-5],lower.panel = foo.lower,upper.panel = foo.upper)
I'm abusing R's scoping here in this second version a somewhat ugly way. (Of course, you could probably do this more cleanly in lattice, but you probably knew that.)
The only other option I can think of is to design your own scatter plot matrix using layout, but that's probably quite a bit of work.
Lattice Edit
Here's at least a start on a lattice solution. It should handle varying x,y axis ranges better, but I haven't tested that.
dat <- do.call(rbind,data)
dat <- as.data.frame(dat)
dat$grp <- rep(letters[1:2],each = 100)
plower <- function(x,y,grp,...){
panel.xyplot(x[grp == 'a'],y[grp == 'a'],col = 'red',...)
}
pupper <- function(x,y,grp,...){
panel.xyplot(x[grp == 'b'],y[grp == 'b'],...)
}
splom(~dat[,1:4],
data = dat,
lower.panel = plower,
upper.panel = pupper,
grp = dat$grp)
I have a large data set which I would like to make a 3D surface from. I would like the x-axis to be the date, the y-axis to be the time (24h) and the z-axis (height) to be a value I have ($). I am a beginner with R, so the simpler the better!
http://www.quantmod.com/examples/chartSeries3d/ has a nice example, but the code is way to complicated for my skill level!
Any help would be much appreciated - anything I have researched so far needs to have the data sorted, which is not suitable I think.
Several options present themselves, persp() and wireframe(), the latter in package lattice.
First some dummy data:
set.seed(3)
dat <- data.frame(Dates = rep(seq(Sys.Date(), Sys.Date() + 9, by = 1),
each = 24),
Times = rep(0:23, times = 10),
Value = rep(c(0:12,11:1), times = 10) + rnorm(240))
persp() needs the data as the x and y grid locations and a matrix z of observations.
new.dates <- with(dat, sort(unique(Dates)))
new.times <- with(dat, sort(unique(Times)))
new.values <- with(dat, matrix(Value, nrow = 10, ncol = 24, byrow = TRUE))
and can be plotted using:
persp(new.dates, new.times, new.values, ticktype = "detailed", r = 10,
theta = 35, scale = FALSE)
The facets can be coloured using the col argument. You could do a lot worse than study the code for chartSeries3d0() at the page you linked to. Most of the code is just drawing proper axes as neither persp() nor wireframe() handle Date objects easily.
As for wireframe(), we
require(lattice)
wireframe(Value ~ as.numeric(Dates) + Times, data = dat, drape = TRUE)
You'll need to do a bit or work to sort out the axis labelling as wireframe() doesn't work with objects of class "Date" at the moment (hence the cast as numeric).