In my function, I need to store some objects in the workspace using e.g.
matrix <<- mean(matrix)
as I am referring to that object in other functions nested within the "global function". However, I would like to delete these objects at the end of the "global function". How can this be achieved? rm() does not work...
UPDATE:
I outsourced the plotting part of my function and am sourcing this within the "higher order function".
The plotting function sort of looks like this
plot_minaverage <- function(minaverage){
for_minaverage_plot.time <- rep(seq(1,1440),2)
seq <- seq(start.time*60, length.out = 1440)
minaverage_plot_time <- for_minaverage_plot.time[seq]
minaverage_plot_df <- data.frame (minaverage_plot_time, minaverage)
pp <- ggplot(minaverage_plot_df, aes(x=minaverage_plot_time, y = minaverage))+
geom_bar(stat="identity", width = 1, position = position_dodge(width = 0.5))+
theme_bw()+
print(pp)
}
The problem I have is that minverage is computed in the "higher order" function and when I do not store it in workspace as mentioned above, the plotting function cannot access it.
Thanks so much for your thoughts on this!
Related
I am currently trying to plot some density distributions functions with R's ggplot2. I have the following code:
f <- stat_function(fun="dweibull",
args=list("shape"=1),
"x" = c(0,10))
stat_F <- stat_function(fun="pweibull",
args=list("shape"=1),
"x" = c(0,10))
S <- function() 1 - stat_F
h <- function() f / S
wei_h <- ggplot(data.frame(x=c(0,10))) +
stat_function(fun=h) +
...
Basically I want to plot hazard functions based on a Weibull Distribution with varying parameters, meaning I want to plot:
The above code gives me this error:
Computation failed in stat_function():
unused argument (x_trans)
I also tried to directly use
S <- 1 - stat_function(fun="pweibull", ...)
instead of above "workaround" with the custom function construction. This threw another error, since I was trying to do numeric arithmetics on an object:
non-numeric argument for binary operator
I get that error, but I have no idea for a solution.
I have done some research, but without success. I feel like this should be straightforward. Also I would like to do it "manually" as much as possible, but if there is no simple way to do this, then a packaged solution is just fine aswell.
Thanks in advance for any suggestions!
PS: I basically want to recreate the graph you can find in Kiefer, 1988 on page 10 of the linked PDF file.
Three comments:
stat_function is a function statistic for ggplot2, you cannot divide two stat_function expressions by each other or otherwise use them in mathematical expressions, as in S <- 1 - stat_function(fun="pweibull", ...). That's a fundamental misunderstanding of what stat_function is. stat_function always needs to be added to a ggplot2 plot, as in the example below.
The fun argument for stat_function takes a function as an argument, not a string. You can define functions on the fly if you need ones that don't exist already.
You need to set up an aesthetic mapping, via the aes function.
This code works:
args = list("shape" = 1.2)
ggplot(data.frame(x = seq(0, 10, length.out = 100)), aes(x)) +
stat_function(fun = dweibull, args = args, color = "red") +
stat_function(fun = function(...){1-pweibull(...)}, args = args, color = "green") +
stat_function(fun = function(...){dweibull(...)/(1-pweibull(...))},
args = args, color = "blue")
I need to wrap ggplot2 into another function, and want to be able to parse variables in the same manner that they are accepted, can someone steer me in the correct direction.
Lets say for example, we consider the below MWE.
#Load Required libraries.
library(ggplot2)
##My Wrapper Function.
mywrapper <- function(data,xcol,ycol,colorVar){
writeLines("This is my wrapper")
plot <- ggplot(data=data,aes(x=xcol,y=ycol,color=colorVar)) + geom_point()
print(plot)
return(plot)
}
Dummy Data:
##Demo Data
myData <- data.frame(x=0,y=0,c="Color Series")
Existing Usage which executes without hassle:
##Example of Original Function Usage, which executes as expected
plot <- ggplot(data=myData,aes(x=x,y=y,color=c)) + geom_point()
print(plot)
Objective usage syntax:
##Example of Intended Usage, which Throws Error ----- "object 'xcol' not found"
mywrapper(data=myData,xcol=x,ycol=y,colorVar=c)
The above gives an example of the 'original' usage by the ggplot2 package, and, how I would like to wrap it up in another function. The wrapper however, throws an error.
I am sure this applies to many other applications, and it has probably been answered a thousand times, however, I am not sure what this subject is 'called' within R.
The problem here is that ggplot looks for a column named xcol in the data object. I would recommend to switch to using aes_string and passing the column names you want to map using a string, e.g.:
mywrapper(data = myData, xcol = "x", ycol = "y", colorVar = "c")
And modify your wrapper accordingly:
mywrapper <- function(data, xcol, ycol, colorVar) {
writeLines("This is my wrapper")
plot <- ggplot(data = data, aes_string(x = xcol, y = ycol, color = colorVar)) + geom_point()
print(plot)
return(plot)
}
Some remarks:
Personal preference, I use a lot of spaces around e.g. x = 1, for me this greatly improves the readability. Without spaces the code looks like a big block.
If you return the plot to outside the function, I would not print it inside the function, but just outside the function.
This is just an addition to the original answer, and I do know that this is quite an old post, but just as an addition:
The original answer provides the following code to execute the wrapper:
mywrapper(data = "myData", xcol = "x", ycol = "y", colorVar = "c")
Here, data is provided as a character string. To my knowledge this will not execute correctly. Only the variables within the aes_string are provided as character strings, while the data object is passed to the wrapper as an object.
I've been trying to draw two plots using R's ggplot library in RStudio. Problem is, when I draw two within one function, only the last one displays (in RStudio's "plots" view) and the first one disappears. Even worse, when I run ggsave() after each plot - which saves them to a file - neither of them appear (but the files save as expected). However, I want to view what I've saved in the plots as I was able to before.
Is there a way I can both display what I'll be plotting in RStudio's plots view and also save them? Moreover, when the plots are not being saved, why does the display problem happen when there's more than one plot? (i.e. why does it show the last one but not the ones before?)
The code with the plotting parts are below. I've removed some parts because they seem unnecessary (but can add them if they are indeed relevant).
HHIplot = ggplot(pergame)
# some ggplot geoms and misc. here
ggsave(paste("HHI Index of all games,",year,"Finals.png"),
path = plotpath, width = 6, height = 4)
HHIAvePlot = ggplot(AveHHI, aes(x = AveHHI$n_brokers))
# some ggplot geoms and misc. here
ggsave(paste("Average HHI Index of all games,",year,"Finals.png"),
path = plotpath, width = 6, height = 4)
I've already taken a look here and here but neither have helped. Adding a print(HHIplot) or print(HHIAvePlot) after the ggsave() lines has not displayed the plot.
Many thanks in advance.
Update 1: The solution suggested below didn't work, although it works for the answer's sample code. I passed the ggplot objects to .Globalenv and print() gives me an empty gray box on the plot area (which I imagine is an empty ggplot object with no layers). I think the issue might lie in some of the layers or manipulators I have used, so I've brought the full code for one ggplot object below. Any thoughts? (Note: I've tried putting the assign() line in all possible locations in relation to ggsave() and ggplot().)
HHIplot = ggplot(pergame)
HHIplot +
geom_point(aes(x = pergame$n_brokers, y = pergame$HHI)) +
scale_y_continuous(limits = c(0,10000)) +
scale_x_discrete(breaks = gameSizes) +
labs(title = paste("HHI Index of all games,",year,"Finals"),
x = "Game Size", y = "Herfindahl-Hirschman Index") +
theme(text = element_text(size=15),axis.text.x = element_text(angle = 0, hjust = 1))
assign("HHIplot",HHIplot, envir = .GlobalEnv)
ggsave(paste("HHI Index of all games,",year,"Finals.png"),
path = plotpath, width = 6, height = 4)
I'll preface this by saying that the following is bad practice. It's considered bad practice to break a programming language's scoping rules for something as trivial as this, but here's how it's done anyway.
So within the body of your function you'll create both plots and put them into variables. Then you'll use ggsave() to write them out. Finally, you'll use assign() to push the variables to the global scope.
library(ggplot2)
myFun <- function() {
#some sample data that you should be passing into the function via arguments
df <- data.frame(x=1:10, y1=1:10, y2=10:1)
p1 <- ggplot(df, aes(x=x, y=y1))+geom_point()
p2 <- ggplot(df, aes(x=x, y=y2))+geom_point()
ggsave('p1.jpg', p1)
ggsave('p2.jpg', p2)
assign('p1', p1, envir=.GlobalEnv)
assign('p2', p2, envir=.GlobalEnv)
return()
}
Now, when you run myFun() it will write out your two plots to .jpg files, and also drop the plots into your global environment so that you can just run p1 or p2 on the console and they'll appear in RStudio's Plot pane.
ONCE AGAIN, THIS IS BAD PRACTICE
Good practice would be to not worry about the fact that they're not popping up in RStudio. They wrote out to files, and you know they did, so go look at them there.
I am going to plot a boxplot from a 4-column matrix pl1 using ggplot with dots on each box. The instruction for plotting is like this:
p1 <- ggplot(pl1, aes(x=factor(Edge_n), y=get(make.names(y_label)), ymax=max(get(make.names(y_label)))*1.05))+
geom_boxplot(aes(fill=method), outlier.shape= NA)+
theme(text = element_text(size=20), aspect.ratio=1)+
xlab("Number of edges")+
ylab(y_label)+
scale_fill_manual(values=color_box)+
geom_point(aes(x=factor(Edge_n), y=get(make.names(true_des)), ymax=max(get(make.names(true_des)))*1.05, color=method),
position = position_dodge(width=0.75))+
scale_color_manual(values=color_pnt)
Then, I use print(p1) to print it on an opened pdf. However, this does not work for me and I get the below error:
Error in make.names(true_des) : object 'true_des' not found
Does anyone can help?
Your example is not very clear because you give a call but you don't show the values of your variables so it's really hard to figure out what you're trying to do (for instance, is method the name of a column in the data frame pl1, or is it a variable (and if it's a variable, what is its type? string? name?)).
Nonetheless, here's an example that should help set you on the way to doing what you want:
Try something like this:
pl1 <- data.frame(Edge_n = sample(5, 20, TRUE), foo = rnorm(20), bar = rnorm(20))
y_label <- 'foo'
ax <- do.call(aes, list(
x=quote(factor(Edge_n)),
y=as.name(y_label),
ymax = substitute(max(y)*1.05, list(y=as.name(y_label)))))
p1 <- ggplot(pl1) + geom_boxplot(ax)
print(p1)
This should get you started to figuring out the rest of what you're trying to do.
Alternately (a different interpretation of your question) is that you may be running into a problem with the environment in which aes evaluates its arguments. See https://github.com/hadley/ggplot2/issues/743 for details. If this is the issue, then the answer might to override the default value of the environment argument to aes, for instance: aes(x=factor(Edge_n), y=get(make.names(y_label)), ymax=max(get(make.names(y_label)))*1.05, environment=environment())
Many R objects have S3 methods to plot associated with them. For instance, every R regression tutorial contains something like this:
dat <- data.frame(x=runif(10))
dat$y <- dat$x+runif(10)
my.lm <- lm( y~x, dat )
plot(my.lm)
Which displays regression diagnostics.
Similarly, I have an S3 object for a package which consists of a list which basically holds a few time series. I have a plot.myobject method for it which reaches into the list, yanks out the time series, and plots them on the same graph. I would like to rewrite this as a ggplot2 function so that it will be prettier and perhaps more extensible as well.
Because this package is intended to get people without much R experience up and running quickly, I'd like this to be a one-liner with one argument, as in plot(myobject), ggplot(myobject), or whatever the appropriate version might be. Then once they get hooked, they can learn more about ggplot2 and customize the graph to their heart's content.
My initial temptation was to simply replace the internals of the plot.myobject method to use ggplot2. This, however, seems like it might lose me major style points.
Is this a bad idea, and if so why and what alternative should I use?
There is an existing idiom in ggplot2 to do exactly what you propose. It is called fortify. It takes an object and produces a version of the object in a form that ggplot can work with, i.e. a data.frame. Section 9.3 in Hadley's ggplot2 book describes how to do this, using the S3 object class lm as an example. To see this in action, type fortify.lm into your console to get the following code:
function (model, data = model$model, ...)
{
infl <- influence(model, do.coef = FALSE)
data$.hat <- infl$hat
data$.sigma <- infl$sigma
data$.cooksd <- cooks.distance(model, infl)
data$.fitted <- predict(model)
data$.resid <- resid(model)
data$.stdresid <- rstandard(model, infl)
data
}
<environment: namespace:ggplot2>
Here is my own example of writing a fortify method for tree, originally published on the ggplot2 mailing list
fortify.tree <- function(model, data, ...){
require(tree)
# Uses tree:::treeco to extract data frame of plot locations
xy <- tree:::treeco(model)
n <- model$frame$n
# Lines copied from tree:::treepl
x <- xy$x
y <- xy$y
node = as.numeric(row.names(model$frame))
parent <- match((node%/%2), node)
sibling <- match(ifelse(node%%2, node - 1L, node + 1L), node)
linev <- data.frame(x=x, y=y, xend=x, yend=y[parent], n=n)
lineh <- data.frame(x=x[parent], y=y[parent], xend=x,
yend=y[parent], n=n)
rbind(linev[-1,], lineh[-1,])
}
theme_null <- opts(
panel.grid.major = theme_blank(),
panel.grid.minor = theme_blank(),
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.ticks = theme_blank(),
axis.title.x = theme_blank(),
axis.title.y = theme_blank(),
legend.position = "none"
)
And the plot code. Notice that the data passed to ggplot is not a data.frame but a tree object.
library(ggplot2)
library(tree)
data(cpus, package="MASS")
cpus.ltr <- tree(log10(perf) ~ syct+mmin+mmax+cach+chmin+chmax, cpus)
p <- ggplot(data=cpus.ltr) +
geom_segment(aes(x=x,y=y,xend=xend,yend=yend,size=n),
colour="blue", alpha=0.5) +
scale_size("n", to=c(0, 3)) +
theme_null
print(p)
As per Hadley's suggestion in comments, I have submitted a generic S3 autoplot() to the ggplot2 Github repository. So if it's accepted and checks out, there should be an autoplot available for this use in the future.
Update
autoplot is now available in ggplot2.
Using plot.myobject is easy to remember and execute. However, if you're talking about myobjects that already have plot.myobject functions, you have to possibly worry about the different versions in the different namespaces. But if it's just for your own myobjects, you don't lose any style points with me. The nlme package, for one, does this extensively, though with lattice graphs instead of ggplot.
Using ggplot.myobject is an alternative; you shouldn't have to worry about other versions, unless other people start doing the same thing. However, as you note, it does break the ggplot usage paradigm.
Another alternative is to use a new name, say, gsk3plot; you never have to worry about other versions, it's not too hard to remember, and you can make alternatives to plot to your heart's content without having to worry about conflicts. This is probably what I'd choose as it makes it clear to the audience that these plots are customizable and this is a function that makes the plot the way that you prefer, and that if they are so inclined, they could dig in and do the same thing.
ggplot and ggplot2 methods generally expect the data to come to them in melt()-ed form. So your methods may need to do a melt (from package plyr) and then "map" the resulting column names to arguments in the ggplot methods.