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I made a phylogenetic tree plot with the igraph package.
The problem is, that my tree is too big to fit into the saved image.
I'm using Rstudio and usually saving my plots manually.
With smaller trees, I was able to enlarge the plot window of Rstudio to make the plot fit into the image, without the nodes overlapping each other.
Unfortunately, this isn't working with my latest plot.
I tried to save it with png(), pdf() and jpeg() with different width and height, but it still doesn't fit properly.
Either the nodes overlap each other or only a part of the plot is visible in the image.
The only solution I found so far was to decrease the label size. But in the end, you can't read anything.
Thank you in advance for your help.
Here is my code:
id <- c("Spirochaetota","Brachyspirae","Brevinematia","Leptospirae","Spirochaetia","sk6","Brachyspirales","Brachyspiraceae","Brachyspira",
"Brevinematales","bo1","Brevinemataceae","Brevinema","bf2","Leptospirales","Leptonemataceae","Leptonema","lg2","Leptospiraceae","Leptospira",
"LeptospiraA","LeptospiraB","lg3","lf1","Turneriellales","Turneriellaceae","Turneriella","tf1","Borreliales","Borreliaceae","Borrelia",
"Borreliella","Sphaerochaetales","Sphaerrochaetaceae","Sphaerochaeta","SphaerochaetaA","sg15","SpirochaetalesA","SpirochaetaceaeA",
"SpirochaetaA","sf1","SpirochaetalesC","Alkalispirochaetaceae","Alkalispirochaeta","Salinispiraceae","Salinispira","SpirochaetaD","sg1",
"sf6","SpirochaetalesD","sdf1","SpirochaetalesE","SpirochaetaceaeB","Oceanispirochaeta","SpirochaetaE","SpirochaetaF","SpirochaetaG","sg2",
"Treponematales","Treponemataceae","Treponema","TreponemaB","TreponemaC","TreponemaD","TreponemaF","tg7","TreponemataceaeB","TreponemaE",
"TreponemaG","TreponemaH","tg5","tf3","so11")
links <- data.frame(from = c("Spirochaetota","Spirochaetota","Spirochaetota","Spirochaetota","Spirochaetota","Brachyspirae",
"Brachyspirales","Brachyspiraceae","Brevinematia","Brevinematia","Brevinematales","Brevinemataceae",
"Brevinematales","Leptospirae","Leptospirales","Leptonemataceae","Leptonemataceae","Leptospirales",
"Leptospiraceae","Leptospiraceae","Leptospiraceae","Leptospiraceae","Leptospirales","Leptospirae",
"Turneriellales","Turneriellaceae","Turneriellales","Spirochaetia","Borreliales","Borreliaceae",
"Borreliaceae","Spirochaetia","Sphaerochaetales","Sphaerrochaetaceae","Sphaerrochaetaceae","Sphaerrochaetaceae",
"Spirochaetia","SpirochaetalesA","SpirochaetaceaeA","SpirochaetalesA","Spirochaetia","SpirochaetalesC",
"Alkalispirochaetaceae","SpirochaetalesC","Salinispiraceae","Salinispiraceae","Salinispiraceae","SpirochaetalesC",
"Spirochaetia","SpirochaetalesD","Spirochaetia","SpirochaetalesE","SpirochaetaceaeB","SpirochaetaceaeB",
"SpirochaetaceaeB","SpirochaetaceaeB","SpirochaetaceaeB","Spirochaetia","Treponematales","Treponemataceae",
"Treponemataceae","Treponemataceae","Treponemataceae","Treponemataceae","Treponemataceae","Treponematales",
"TreponemataceaeB","TreponemataceaeB","TreponemataceaeB","TreponemataceaeB","Treponematales","Spirochaetia"),
to = c("Brachyspirae","Brevinematia","Leptospirae","Spirochaetia","sk6","Brachyspirales",
"Brachyspiraceae","Brachyspira","Brevinematales","bo1","Brevinemataceae","Brevinema",
"bf2","Leptospirales","Leptonemataceae","Leptonema","lg2","Leptospiraceae",
"Leptospira","LeptospiraA","LeptospiraB","lg3","lf1","Turneriellales",
"Turneriellaceae","Turneriella","tf1","Borreliales","Borreliaceae","Borrelia",
"Borreliella","Sphaerochaetales","Sphaerrochaetaceae","Sphaerochaeta","SphaerochaetaA","sg15",
"SpirochaetalesA","SpirochaetaceaeA","SpirochaetaA","sf1","SpirochaetalesC","Alkalispirochaetaceae",
"Alkalispirochaeta","Salinispiraceae","Salinispira","SpirochaetaD","sg1","sf6",
"SpirochaetalesD","sdf1","SpirochaetalesE","SpirochaetaceaeB","Oceanispirochaeta","SpirochaetaE",
"SpirochaetaF","SpirochaetaG","sg2","Treponematales","Treponemataceae","Treponema",
"TreponemaB","TreponemaC","TreponemaD","TreponemaF","tg7","TreponemataceaeB",
"TreponemaE","TreponemaG","TreponemaH","tg5","tf3","so11"))
net <- graph_from_data_frame(d = links, vertices = id, directed = T)
lay = layout.reingold.tilford(net)
plot(net, vertex.shape = "none",
vertex.label.font = 3,
vertex.label.cex = 0.3,
edge.arrow.size = 0.3,
rescale = F,
ylim = c(0.7,3.4),xlim = c(-8,23.2), asp = 0,
layout = lay)
If everything works, it should look similar to this smaller tree. this one does not include the nodes of the code above:
This is my result of the big tree so far. It contains the nodes in the code above:
The only solution I came up with so far is to somehow fit the plot into the plot panel and save it as a SVG file. Afterwards, I edited it using inkscape:
You can plot the tree graph as a dendrogram.
Drawing it left to right allows to put all of the taxon labels in a readable column:
library(igraph)
net <- graph_from_data_frame(d = links, vertices = id, directed = F)
lay <- layout.reingold.tilford(net)
plot_dendrogram(cluster_fast_greedy(net))
There is also:
links %>%
graph_from_data_frame() %>%
as_adjacency_matrix() %>%
dist() %>%
hclust() %>%
as.dendrogram() %>%
plot()
I am trying to draw a standard triangular mediation diagram using DiagrammeR in R (it can also interpret graphviz code). On the whole, it's working fine but the edge label text gets placed oddly. The bottom edge label is not centered and the two angled edge labels are positioned at different heights (see the red lines in the diagram below). Is there a way to manually assign positions to edge text or get something more consistent?`
library(DiagrammeR)
# Create a node data frame (ndf)
ndf <- create_node_df(
n = 3,
label = c("Experimental\nTreatment", "Some\nMediator", "Outcome\nof Interest"),
shape = rep("rectangle", 3),
style = "empty",
fontsize = 6,
fixedsize = TRUE,
height = .5,
width = .75,
color = "gray40",
x = c(1, 2, 3),
y = c(1, 2, 1)
)
# Create an edge data frame (edf)
edf <- create_edge_df(
from = c(1, 1, 2),
to = c(2, 3, 3),
label = c("1.1*", "2.0*", "-0.33***"),
fontsize = 6,
minlen = 1,
color = "gray40",
)
# Create a graph with the ndf and edf
graph <- create_graph(
nodes_df = ndf,
edges_df = edf
)
graph %>%
render_graph()
I would also love to know the answer to this.
I have tried to delete the default "layout" graph attribute and set direction of graph layout via:
graphAttr <- DiagrammeR::get_global_graph_attrs(graph)
But, I get the following error:
Error: 'get_global_graph_attrs' is not an exported object from 'namespace:DiagrammeR'
....which it is, consequently, I am stuck.
After fiddling with DiagrammeR for a while, I ended up switching to the LaTeX diagramming package TikZ. It allows for enormous control of every aspect of the diagram but can be overwhelming. TikZ would require a bit of tweaking, also, to get diagrams into HTML output if that's important. For a presentation using LaTeX's Beamer class for slides, generating TikZ code with a function in R worked well. That TikZ code was then written to a text file that could be imported automatically via the LaTeX command \input{path/to/file.txt} or just copied and pasted into my slides.
See the code for two DiagrammeR solutions and the TikZ solution, here: https://stackoverflow.com/a/64886536/893399
I have data where a participant had positional data on each hand:
left:
right:
positional data has
pos.x
pos.y
pos.z
at some time t
The initial question was about importing OBJ to work with RGL: https://stackoverflow.com/posts/46626767/revisions
I have figured that part out. I have written functions to improve rgl mesh framework for open-source OBJ files.
I placed the required functions online: https://gist.github.com/MonteShaffer/d142210cddf346c86aeab1ea2d1d7e9d
The positional data should be captured on a wrist watch, so I want to be able to show two or more 3D data objects and animate each independently based on positional/time data.
That is, treat the hand like a rigid object with the wrist-watch region moving appropriately.
I placed the hand.OBJ file online: https://gist.github.com/MonteShaffer/6c0057b1431364caf120220db77dde4b
I am aware of basic graphing, updating, spinning:
library(rgl)
mymesh = buildBasicMeshFromOBJ(parseFileOBJ("hand.OBJ"));
open3d()
shade3d(mymesh, col = "pink")
par3d(userMatrix = rotate3d(par3d("userMatrix"), 0.1, 1,0,0))
play3d(spin3d(axis = c(1, 0, 0)))
My goal is to plot multiple objects on the same rgl canvas.
e.g.,
righthand = mymesh
lefthand = mymesh
head = buildBasicMeshFromOBJ(parseFileOBJ("head.obj"));
and have each element move independently as an animation over time based on positional/time data for each element. For now the head stays still, but each hand moves based on the rigid idea with the position representing the wrist.
It would be a bonus that the mouse drag could still occur (to change 3D views around the hands and head) while the animation is going on.
You can move objects within the scene by using a function like rotate3d. Despite its name, it allows for quite general kinds of movement: see the help page.
So instead of using par3d to move things, move the objects themselves.
rgl doesn't provide a way to modify objects that are already in the scene (though the WebGL display of rgl objects does...), so the basic idea is the following:
Plot the object, and save the ids (e.g. ids <- shade3d(mymesh))
Then, in a loop:
Turn off updates (using par3d(skipRedraw=TRUE))
Delete the object (e.g. rgl.pop(id=ids))
Move and replot the object
Turn on updates.
The play3d function provides a framework to automate this, but you don't need to use it. For example, this plots two icosahedra and randomly rotates them independently:
orig1 <- icosahedron3d()
id1 <- shade3d(orig1, col = "green")
orig2 <- translate3d(orig1, 4, 0, 0)
id2 <- shade3d(orig2, col = "blue")
repeat {
orig1 <- rotate3d(orig1, 0.01, rnorm(1, 1), rnorm(1), rnorm(1))
orig2 <- rotate3d(orig2, 0.05, rnorm(1, -1), rnorm(1), rnorm(1))
par3d(skipRedraw = TRUE)
rgl.pop(id = c(id1, id2))
id1 <- shade3d(orig1, col = "green")
id2 <- shade3d(orig2, col = "blue")
par3d(skipRedraw = FALSE)
}
Another way to do this is to set up two separate subscenes. Then each
can be controlled by its own par3d() setting. For example,
icos <- icosahedron3d()
ids <- mfrow3d(1,2)
shade3d(icos, col = "red")
next3d()
shade3d(icos, col = "green")
Now each of the icosahedra can be manipulated independently with the mouse. If you want to do it with par3d, use something like par3d(..., subscene = ids[1]) to affect the left pane, par3d(..., subscene = ids[2]) for the right pane.
I'm wondering if anyone knows of a way to export plotly 3d charts as a video (more specifically if this can be done natively or requires bodging)?
Exporting a static image is simple, and exporting the interactive plots is fine for embedding in HTML etc.
Lets say I have a 3D chart that I want so simply rotate slowly, this seems like it could be pretty straight forward if the image can be rotated a given interval, an image taken, rotated further ad infinitum, perhaps in a loop - but I'm wondering if this isn't somehow supported natively?
Anyone know of a good strategy?
Solution ideally for R/RStudio, but since plotly is cross-platform, any solutions considered.
For future reference:
The key to being able to iterate multiple perspectives turns out to lie in the camera control "eye", the plotly help centre pointed me toward this:
https://plot.ly/r/reference/#layout-scene-camera
camera = list(eye = list(x = 1.25, y = 1.25, z = 1.25))) #1.25 is default
This is kind of answered here, though searching for my specific query as above didn't find it:
enter link description here
I've used a for loop in the script to pass iterators to a trigonometric function that plots out a circle for the camera co-ordinates, rendering a new image at each step.
(x,y) = cos(theta) + sin(theta)
The eventual code looked like this:
# assume dataset read in, manipulated and given as a matrix in "matrix"
matrix.list <- list(x = temp, y = scan, z = matrix)
font.pref <- list(size=12, family="Arial, sans-serif", color="black")
x.list <- list(title = "X", titlefont = font.pref)
y.list <- list(title = "Y", titlefont = font.pref)
z.list <- list(title = "Z",titlefont = font.pref)
zoom <- 2
for(i in seq(0,6.3,by=0.1){
# 6.3 is enough for a full 360 rotation
outfile <- paste(file,"plot",i, sep = "_")
graph <- plot_ly(matrix.list, x = temp, y = scan, z = z,
type="surface") %>%
layout(scene=list(xaxis = x.list,
yaxis = y.list,
zaxis = z.list,
camera = list(eye = list(x = cos(i)*zoom, y = sin(i)*zoom, z= 0.25))))
# The above camera parameters should orbit
# horizontally around the chart.
# The multiplier controls how far out from
# from the graph centre the camera is (so
# is functionally a 'zoom' control).
graph
plotly_IMAGE(graph, username="xxx", key="xxx",
out_file = paste(outfile,"png", sep="."))
}
NB Number of files and the resolution of them could end up taking up a fair amount of space.
NB 2 I had forgotten while constructing this that plotly's free API limits you to 50 API calls per day, so if you want to render a video, adjust your frames etc, accordingly...
I have a grid and I want to produce a map out of this grid with some map elements (scale, north arrow, etc). I have no problem drawing the grid and the coloring I need, but the additional map elements won't show on the map. I tried putting first=TRUE to the sp.layout argument according to the sp manual, but still no success.
I reproduced the issue with the integrated meuse dataset, so you may just copy&paste that code. I use those package versions: lattice_0.20-33 and sp_1.2-0
library(sp)
library(lattice) # required for trellis.par.set():
trellis.par.set(sp.theme()) # sets color ramp to bpy.colors()
alphaChannelSupported = function() {
!is.na(match(names(dev.cur()), c("pdf")))
}
data(meuse)
coordinates(meuse)=~x+y
data(meuse.riv)
library(gstat, pos = match(paste("package", "sp", sep=":"), search()) + 1)
data(meuse.grid)
coordinates(meuse.grid) = ~x+y
gridded(meuse.grid) = TRUE
v.uk = variogram(log(zinc)~sqrt(dist), meuse)
uk.model = fit.variogram(v.uk, vgm(1, "Exp", 300, 1))
meuse[["ff"]] = factor(meuse[["ffreq"]])
meuse.grid[["ff"]] = factor(meuse.grid[["ffreq"]])
zn.uk = krige(log(zinc)~sqrt(dist), meuse, meuse.grid, model = uk.model)
zn.uk[["se"]] = sqrt(zn.uk[["var1.var"]])
meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
rv = list("sp.polygons", meuse.sr, fill = "lightblue")
sampling = list("sp.points", meuse.riv, color = "black")
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4)
text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4)
text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(181300,329800),
scale = 400, which = 4)
library(RColorBrewer)
library(lattice)
trellis.par.set(sp.theme())
precip.pal <- colorRampPalette(brewer.pal(7, name="Blues"))
spplot(zn.uk, "var1.pred",
sp.layout = list(rv, sampling, scale, text1, text2),
main = "log(zinc); universal kriging standard errors",
col.regions=precip.pal,
contour=TRUE,
col='black',
pretty=TRUE,
scales=list(draw = TRUE),
labels=TRUE)
And that's how it looks...all naked:
So my questions:
Where is the scale bar, north arrow, etc hiding? Did I miss something? Every example I could find on the internet looks similar to that. On my own dataset I can see the scale bar and north arrow being drawn at first, but as soon as the grid is rendered, it superimposes the additional map elements (except for the scale text, that is shown on the map - not the bar and north arrow for some reason I don't seem to comprehend).
The error message appearing on the map just shows when I try to add the sampling locations sampling = list("sp.points", meuse.riv, color = "black"). Without this entry, the map shows without error, but also without additional map elements. How can I show the sampling points on the map (e.g. in circles whose size depends on the absolute value of this sampling point)?
This bothered me for many, many hours by now and I can't find any solution to this. In Bivand et al's textbook (2013) "Applied Spatial Data Analysis with R" I could read the following entry:
The order of items in the sp.layout argument matters; in principle objects
are drawn in the order they appear. By default, when the object of spplot has
points or lines, sp.layout items are drawn before the points to allow grids
and polygons drawn as a background. For grids and polygons, sp.layout
items are drawn afterwards (so the item will not be overdrawn by the grid
and/or polygon). For grids, adding a list element first = TRUE ensures that
the item is drawn before the grid is drawn (e.g. when filled polygons are added). Transparency may help when combining layers; it is available for the
PDF device and several other devices.
Function sp.theme returns a lattice theme that can be useful for plots
made by spplot; use trellis.par.set(sp.theme()) after a device is opened
or changed to make this effective.
However, also with this additional information I wasn't able to solve this problem. Glad for any hint!
The elements you miss are being drawn in panel four, which does not exist, so are not being drawn. Try removing the which = 4.
meuse.riv in your example is a matrix, which causes the error message, but should be a SpatialPoints object, so create sampling by:
sampling = list("sp.points", SpatialPoints(meuse.riv), color = "black")
When working from examples, my advice is to choose examples as close as possible to what you need, and only change one thing at a time.