Using ggplot, is there a way of graphing several functions on the same plot? I want to use parameters from a text file as arguments for my functions and overlay these on the same plot.
I understand this but I do not know how to add the visualized function together if I loop through.
Here is an implementation of Hadley's idea.
library(ggplot2)
funcs <- list(log,function(x) x,function(x) x*log(x),function(x) x^2, exp)
cols <-heat.colors(5,1)
p <-ggplot()+xlim(c(1,10))+ylim(c(1,10))
for(i in 1:length(funcs))
p <- p + stat_function(aes(y=0),fun = funcs[[i]], colour=cols[i])
print(p)
Related
In desperate need of a sanity check. I am struggling to see why the result of plot_grid (cowplot) of N plots in my code is producing N identical plots. From the list I provide, I've taken out each data frame to verify that each plot should be different, however, when I pass in the complete list to plot_grid they all look identical.
p <- vector("list",length(dataList))
for(i in 1:length(dataList)) {
df <- dataList[[i]]
p[[i]] <- ggplot(df, aes(df$base)) + geom_bar()
}
multi <- plot_grid(plotlist=p, align="hv")
save_plot(paste("data_freqs.tiff",sep=""), multi, dpi=300, base_aspect_ratio=1.5)
For example, when type the following I can see the data is different:
a<-dataList[[1]]
b<-dataList[[2]]
sum(a$base=="T")
>1245
sum(b$base=="T")
>1034
However, I end up with multiple plots of identical T values (all fixed to 1245).
Any help is much appreciated.
Thanks
I intended to generate multi-plot per page using something like this:
d=data.frame(label=c(rep('a',3),rep('b',3)) )
d$x = sample(6,nrow(d))
d$y = sample(10,nrow(d))
plotList <- lapply(c("a","b"), function(i) {
plot(df[i,2],df[i,3])
})
library(gridExtra)
do.call(grid.arrange, c(plotList, nrow=2))
Intended effect is one point plot of label a data by the side ne point plot of label b data. I know ggplot panel can work here
d %>% ggplot(aes(x, y)) + geom_point() + facet_grid(.~label)
but the specific case I need to plot which i don't have a good way to share raw data here I'll need to use apply to plot it and grid.arrange to put on same page. I saw grid.arrange example here. Wondering how can i make apply function work? thanks
I'd like to spawn several graphics windows from within a function in R using ggplot graphics...
testf <- function(a, b) {
devAskNewPage(TRUE)
qplot(a, b);
# grid.newpage(recording = TRUE)
dev.new()
qplot(a, a+a);
# grid.newpage(recording = TRUE)
dev.new()
qplot(b, b+b);
}
library(ggplot2)
x <- rnorm(50)
y <- rnorm(50)
testf(x, y)
However, neither dev.new() nor grid.newpage() seems to flush the preceding plot.
I know that, in R, functions normally only produce the last thing they evaluate, but I'd like to understand the process better and to learn of any possible workarounds.
Thoughts?
The grid-based graphics functions in lattice and ggplot2 create a graph object, but do not display it. The print() method for the graph object produces the actual display, i.e.,
print(qplot(x, y))
solves the problem.
See R FAQ 7.22.
I have a family of functions that are all the same except for one adjustable parameter, and I want to plot all these functions on one set of axes all superimposed on one another. For instance, this could be sin(n*x), with various values of n, say 1:30, and I don't want to have to type out each command individually -- I figure there should be some way to do it programatically.
library(ggplot2)
define trig functions as a function of frequency: sin(x), sin(2x), sin(3x) etc.
trigf <- function(i)(function(x)(sin(i*x)))
Superimpose two function plots -- this works manually of course
ggplot(data.frame(x=c(0,pi)), aes(x)) + stat_function(fun=trigf(1)) + stat_function(fun=trigf(2))
now try to generalize -- my idea was to make a list of the stat_functions using lapply
plotTrigf <- lapply(1:5, function(i)(stat_function(fun=function(x)(sin(i*x))) ))
try using the elements of the list manually but it doesn't really work -- only the i=5 plot is shown and I'm not sure why when that's not what I referenced
ggplot(data.frame(x=c(0,pi)), aes(x)) +plotTrigf[[1]] + plotTrigf[[2]]
I Thought this Reduce might handle the 'generalized sum' to add to a ggplot() but it doesn't work -- it complains of a non-numeric argument to binary operator
Reduce("+", plotTrigf)
So I'm kind of stuck both in executing this strategy, or perhaps there's some other way to do this.
Are you using version R <3.2? The problem is that you actually need to evaluate your i parameter in your lapply call. Right now it's being left as a promise and not getting evaulated till you try to plot and at that point i has the last value it had in the lapply loop which is 5. Use:
plotTrigf <- lapply(1:5, function(i) {force(i);stat_function(fun=function(x)(sin(i*x))) })
You can't just add stat_function calls together, even without Reduce() you get the error
stat_function(fun=sin) + stat_function(fun=cos)
# Error in stat_function(fun = sin) + stat_function(fun = cos) :
# non-numeric argument to binary operator
You need to add them to a ggplot object. You can do this with Reduce() if you just specify the init= parameter
Reduce("+", plotTrigf, ggplot(data.frame(x=c(0,pi)), aes(x)))
And actually the special + operator for ggplot objects allows you to add a list of objects so you don't even need the Reduce at all (see code for ggplot2:::add_ggplot)
ggplot(data.frame(x=c(0,pi)), aes(x)) + plotTrigf
The final result is
You need to use force in order to make sure the parameter is being evaluated at the right time. It's a very useful technique and a common source of confusion in loops, you should read about it in Hadley's book http://adv-r.had.co.nz/Functions.html
To solve your question: you just need to add force(i) when defining all the plots, inside the lapply function, before making the call to stat_function. Then you can use Reduce or any other method to combine them. Here's a way to combine the plots using lapply (note that I'm using the <<- operator which is discouraged)
p <- ggplot(data.frame(x=c(0,pi)), aes(x))
lapply(plotTrigf, function(x) {
p <<- p + x
return()
})
How to get graph for each column of data.frame within one plot with loop? Must be easy just can't figure it out.
Sample data:
rdata <- data.frame(y=rnorm(1000,2,2),v1=rnorm(1000,1,1),v2=rnorm(1000,3,3),
v3=rnorm(1000,4,4),v4=rnorm(1000,5,5))
What I have tried?
library(lattice)
p <- par(mfrow=c(2,2))
for(i in 2:5){
w <- xyplot(y~rdata[,i],rdata)
print(w)
}
par(p)
If you don't have to use lattice you can just use base plot instead and it should work as you want.
p <- par(mfrow=c(2,2))
for(i in 2:5){
plot(y~rdata[,i],rdata)
}
par(p)
If you want to use lattice look this answer. Lattice ignores par, so you have to do some more work to achieve what you want.
Inorder to easily arrange a bunch of lattice plots, I like to use the helper function print.plotlist. It has a layout= parameter that acts like the layout() function for base graphics. For example, you could call
rdata <- data.frame(y=rnorm(1000,2,2),v1=rnorm(1000,1,1),v2=rnorm(1000,3,3),
v3=rnorm(1000,4,4),v4=rnorm(1000,5,5))
library(lattice)
plots<-lapply(2:5, function(i) {xyplot(y~rdata[,i],rdata)})
print.plotlist(plots, layout=matrix(1:4, ncol=2))
to get
Otherwise you normally use a split= parameter to the print statement to place a plot in a subsection of the device. For example, you could also do
print(plots[[1]], split=c(1,1,2,2), more=T)
print(plots[[2]], split=c(1,2,2,2), more=T)
print(plots[[3]], split=c(2,1,2,2), more=T)
print(plots[[4]], split=c(2,2,2,2))