I have been trying to perform some of the raster related operations using R and whenever I am loading raster files in R in the plot window it is being displayed at a different scale which is hard to notice. I'm a little confused about how to bring it back to a standard size. As I'm new to the R language I'm not able to figure it out. Little help would be appreciated. Thanks in advance.
It was due to the default values set in par() function. By changing the "mar" parameter in the function I was able to resize it to my own convenience.enter image description here
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I am not sure if this is possible, but I was wondering if I could expand the parameters of a base R plot. I am aware you can change the resolution/sizing of the graphs when you go to save them, but this isn't what I want to do. I was wondering if there is a command to stretch out the x/y-axis without increasing the range. I attached a picture of the graph I produced, which I feel is quite cramped and small. Below is also my code, which I don't think is an issue but wanted to attach anyways just to be safe. I'd appreciate any help, thank you so much!
plot(O2water, -1*O2rate,xlim=c(19,21.25))
abline(v=20.1)
I am using the WaveletComp package in R. I first run analyze.coherency to get the cross-wavelet transform and store the result in my.wc.
Next, I want to get a figure of the cross-wavelet power levels and apply wc.image to the previously stored results. The figure is plotted but missing the cone of influence. I did not change the default setting (which is to display the cone of influence). Even explicitly providing the argument for inclusion of the cone of influence does not work.
Did anyone experience similar issues and is there any advice how to fix this issue? Any ideas would be greatly appreciated.
Many thanks, I figured out the issue: The cone of influence is not displayed in the plot window. Only saving the graphic as svg or pdf file will show the cone of influence. All other file extensions won't do the job.
I'd like to create a heat map in R that I want to use on a website. I stumbled upon the SVGAnnotation package which seems to be very nice to process SVG graphics in R to make them more interactive. First, I was planning to add tool tips for each cell in the heatmap - if the user hovers over the cell, the value of this cell should pop up. However, I am fighting with SVGAnnotation for more than 3 hours now, reading and trying things, and I can't get it to work.
I would appreciate any help on the SVGAnnotation tool tip function. But I would also very much appreciate alternatives to SVGAnnotation to add some activity to my R SVG heatmap.
So, what I have got so far looks like this:
library(SVGAnnotation)
data(mtcars)
cars <- as.matrix(mtcars)
map <- svgPlot(heatmap(cars))
addToolTips(map, ...) # problem
saveXML(map, "cars.svg")
My problem is the addToolTips function itself, I guess. Intuitively, I would simply insert the data matrix, i.e., cars, but this does not work and R gets stuck (it's calculating, but doesn't return anything, I waited 50 minutes)
EDIT:
After some more online research, I found a good example of what I want to achieve: http://online.wsj.com/article/SB125993225142676615.html#articleTabs=interactive
This heat map looks really great, and the interactive features (tool tips) work very well. I am wondering how they did that. To me, it looks like the graphic was done in R using the ggplot package.
I wrote a command line tool that can do exactly that if you are still interested to add tool tips to your heat map. It runs in Windows/Linux/MacOS terminals. All you need as input is the heat map as svg file and the data table/matrix that you used as input to create your heat map as csv or other text file.
I am trying to render 739455 data point on a graph using R, but on the x-axis I can not view all those numbers, is there a way I can do that?
I am new to R.
Thank you
As others suggested, try hist, hexbin, plot(density(node)), as these are standard methods for dealing with more points than pixels. (I like to set hist with the parameter breaks = "FD" - it tends to have better breakpoints than the default setting.)
Where you may find some joy is in using the iplots package, an interactive plotting package. The corresponding commands include ihist, iplot, and more. As you have a Mac, the more recent Acinonyx package may be even more fun. You can zoom in and out quite easily. I recommend starting with the iplots package as it has more documentation and a nice site.
If you have a data frame with several variables, not just node, then being able to link the different plots such that brushing points in one plot highlights them in another will make the whole process more stimulating and efficient.
That's not to say that you should ignore hexbin and the other ideas - those are still very useful. Be sure to check out the options for hexbin, e.g. ?hexbin.
When plotting oscillations in R, e.g., using the package desolve,
df1 <-function(t,y,mu)( list(c(y[2],mu*y[1]^3-y[1]+0.005*cos(t))))
library (deSolve)
yini<-c(y1=0,y2=0)
df2 <-ode(y=yini,func=df1, times=0:520,parms=0.1667)
plot(df2,type="l",which="y1",ylab="Displacement",xlab="Time", main="")
I get raggedy plots such as:
instead of a smooth plot (not done in R) such as:
Does anyone know of a way to obtain a smoother plot in R instead of a raggedy one when displaying oscillations? Note that it is not just a matter of the difference in scale and I am not looking for a smoothing filter.
Thanks,
I generated your plot in R and exported it as PDF. I zoomed in on it and it's quite lovely. I can't see the problem you're talking about there. Therefore, there are some scaling issues or something with a raster format that are causing the issue. Perhaps you're pasting into Word and that's giving you a raster image that's bad. The plot that R is making, at a logical level, is great in spite of the one you posted. It's even better than the comparison plot you put up.
It's possible that you're generating the plot in a raster format and not setting a high enough resolution and size. Try tiff('filname', 1200, 1200, 300) for a good raster image of it. I did notice that when exporting to raster formats it was easy to make your plot into a fine mess with default png or jpg settings that would just smear things.
Maybe you really wanted to sample in your function at a higher resolution, something not done in the comparison plot. If that's the case then it's relatively easy. Change 0:520 to seq(0, 520, 0.1). That's an even nicer plot, as shown below (much better than shown as PDF, EPS, or SVG).