Please note that im not interested in any kind of interpolation algorithms where you expand the amount of pixels and interpolate the new values.
I want to leave the world of pixel based images and am looking for some scalable vector image solution.
Is there a way to turn a pixel image in a ggplot into a color meshed smooth vector graphic?
The following pictures demonstrate what im aiming for.
and then smooth it out.
The images are taken from the following wikipedia article HERE
Please note that the original images from the article are SVG files. You can zoom in as much as you want and you always have smooth color transitions and no edges.
Some additional images and infos: HERE2
Here is some example Data of something that meets the first image "nearest"
library(ggplot2)
n = 5
pixelImg <- expand.grid(x=1:n,y=1:n)
pixelImg$value <- sample(1:n^2,n^2,replace = T)
ggplot(pixelImg, aes(x, y)) +
geom_raster(aes(fill = value)) +
scale_fill_gradientn(colours=c("#FFCd94", "#FF69B4", "#FF0000","#4C0000","#000000"))
If not in ggplot is there a way to do it outside of ggplot?
Look into the ggsave() function. It supports .svg files for vector graphics.
Related
What I would like to do is make a series of plots that combine an image with a scale that is related to the height of the image, but is multiplied by a factor.
So far I've been able to make a plot using the following code:
library(imager)
im <- load.image("ZAB17_4.png")
plot(im)
Image is attached here
But what I would like to do is to have the y-axis be scaled by a value:
pixel_size <- 0.0596
I would also get rid of the x-axis. (xaxt="n" doesn't seem to do the trick). Ideally, I would like the Y-axis to be directly adjacent to the image. I've been trying to poke around with scales in the plot function, but haven't found what I need to do this, but I'm sure there is a simple solution.
*Edited to add image. I should note that the goal is really just to add a scale to the image. The pixel size represents the size of each pixel in reality in mm. I basically want to add a ruler along the side of the image. Maybe r is not the best tool for this, but I plan to also plot data taken from the imaged object and plot it alongside.
Any help is appreciated.
I have a plot generated from the following code:
bars <- ggplot(plottingFrame, aes(x=X, y=as.factor(Y))) +
geom_raster(aes(fill=colour)) + scale_fill_identity())
Where plottingFrame is a load of colour values precomputed. geom_raster is used with scale_fill_identity to make a "heatmap" of different colours:
Ignore the squashed axis and cutoff titles, I made the image smaller so it's not too big.
The above image is what happens when ggsave is used on the bars object and it is saved as a png.
When I try to use ggsave to save as svg however, it blurs the colours between the three coloured rows.
Why does saving a geom_raster based plot blur the three rows - Seq1, Seq2, and Seq3, when png keeps them distinct as in the above image? And how can I stop saving to sag blurring them?
Thanks,
Ben W.
I recently installed ggplot2 and tried a qplot. The plot comes out like this,
If you notice you can see that the markers seem to look deformed rather than circles. Is there a way to correct this?
Here is my code:
require(ggplot2)
set.seed(1410)
qplot(carat, price, data = diamonds)
EDIT: The plot looks fine when exported to a pdf.
I am using R3.0.2 ggplot2_0.9.3.1 on elementary OS.Thanks.
I usually don't worry about the look of the picture in the "window view" / "r view" -- it is just a graphical view of what the picture will look given the current setting for the resolution.
If possible, I save a picture as an eps or pdf file. They are vector based picture and scale well regardless of size.
If I don't use an eps or pdf file, I use png files to save my pictures:
g1 <- ggplot(data, aes(x=X1, y=Y1)+
geom_point(x)
png("high_res_png.png", width = 10000, height = 7000, res = 1300)
print(g1)
dev.off()
Using a lot of pixels on a png will prevent the "saved" picture from looking "fuzzy" or "oddly" shaped (the size of it will be large, but the png looks good even when you zoom in). Hope that helps.
The goofy looking circles is an artifact of your window system; on my Mac OS X (Quartz) setup, default qplot gives great-looking circles. However, I recall that the points do look a little like yours on my Linux box at home...
Shape is customizable, though. The following gives squares rotated 45 degrees, for instance:
qplot(carat, price, data = diamonds) + scale_shape_identity() + geom_point(shape = 23)
Does anyone know if its possible to map to hexagon size with ggplot? Size is listed as an argument in the geom_hex documentation, but there are no examples of size mapping in stat_hexbin, so this just seems to relate to bin size.
Take for example:
ggplot(economics, aes(x=uempmed, y=unemploy)) + geom_hex()
But looking for instance at population distribution (below) it might be useful to map binned mean population to hexagon size, but I've not found a formula for this (if one exists).
ggplot(economics, aes(x=uempmed, y=unemploy, col=pop)) + geom_point()
Any ideas?
Apparently the official answer is that ggplot does not have functionality to map to hexagon area. But as you can see a workaround solution is possible, now posted in a gist at github.
I have a primary graph and some secondary information that I want to facet in another graph below it. Facetting works great except I do not know how to control the relative space used by one facet versus another. Am aware of space='free' but this is only useful if the ranges correspond to the desired relative sizing.
So for instance, I may want a graph where the first facet occupies 80% and the second 20%. Here is an example:
data <- rbind(
data.frame(x=1:500, y=rnorm(500,sd=1), type='A'),
data.frame(x=1:500, y=rnorm(500,sd=5), type='B'))
ggplot() +
geom_line(aes(x=x, y=y, colour=type), data=data) +
facet_grid(type ~ ., scale='free_y')
The above creates 2 facets of equal vertical dimension. Adding in space='free' in the facet_grid function changes the dimensions such that the lower facet is roughly 5x larger than the upper (as expected).
Supposing I want the upper to be 2x as large, with the same data set and ordering of facets. How can I accomplish this?
Is the only way to do this with some trickery in rescaling the data set and manually overriding axis labels (and if so, how)?
Alternative
As indicated below can use viewports to render as multiple graphs. I had considered this and in-fact had implemented using this approach in the past with standard plot and viewports.
The problem is that it is very difficult to get x-axis to align with this approach. So if there is a way to fix the size of the y-axis label region and the size of the legend region, can produce 2 graphs that have the same rendering area.
You don't need to use facets for this - you can also do this by using the viewport function.
> ratio = 1/3
> v1 = viewport(width=1,height=ratio,y=1-ratio/2)
> v2 = viewport(width=1,height=1-ratio,y=(1-ratio)/2)
> print(qplot(1:10,11:20,geom="point"),vp=v1)
> print(qplot(1:10,11:20,geom="line"),vp=v2)
Ratio is the proportion of the top panel to the whole page. Try 2/3 and 4/5 as well.
This approach can get ugly if your legend or axis labels in the two plots are different sizes, but for a fix, see the align.plots function in the ggExtra package and ggplot2 author Hadley Wickam's notes on this very topic.
There's no easy way to do this with facets currently, although if you are prepared to go down to editing the Grid, you can modify the ggplot graph after it has been plotted to get this effect.
See also this question on using grid and ggplot2 to create join plots using R.
Kohske Takahashi posted a patch to facet_grid that allows specification of the relative sizing of facets. See the thread:
http://groups.google.com/group/ggplot2/browse_thread/thread/7c5454dcc04bc7b8
With luck we'll see this in a future version of ggplot2.