Here is a minimal working code from Julia Discourse:
using LightGraphs
using GraphPlot
using Colors
g = graphfamous("karate")
membership = [1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,1,1,2,1,2,1,2,1,1,1,1,2,1,1,2,1,1,1]
nodelabels = 1:34
nodecolor = [colorant"lightgrey", colorant"orange"]
nodefillc = nodecolor[membership]
colors = [colorant"lightgray" for i in 1:78]
colors[42] = colorant"orange"
gplot(g, nodefillc=nodefillc, layout=circular_layout, edgestrokec=colors, nodelabel=nodelabels)
Which produces:
I succeed to have node labels, from 1 to 34, however, I need to display another type of labels for some specific nodes. e.g., the weight of some nodes. That is, I need, for instance, the weight of node 19 is 100 and the weight of node 1 is 0.001.
Is there a way to display such data? I could node find a relevant keyword in GraphPlot (only nodelabel only accepts a Vector) and I could not find another Julia package that could do it for plotting graphs.
EDIT thanks to #Dan Getz, before posting on SE, I had the same idea as he suggested: try to label the nodes with a string of the format "$i\n $weight"
However, the result is highly unsatisfying as you can see in this picture of one of my actual graphs. Node 12 in Orange, separated from its weight 177.0 with \n is not really nice to read!
EDIT thanks to #Przemyslaw Szufel maybe my question could be resolved with Compose (that I actually already use) which is a graphic backend for GraphPlot. Unfortunately it is a bit undocumented despite I and other people asking about it!
You could use GraphMakie.jl, which is also compatible with (Light)Graphs.jl and possibly a bit more flexible than GraphPlot.jl.
using Graphs, GraphMakie, Colors
g = smallgraph(:karate)
membership = [1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,1,1,2,1,2,1,2,1,1,1,1,2,1,1,2,1,1,1]
nodelabels = repr.(collect(1:34))
nodecolor = [colorant"lightgrey", colorant"orange"]
nodefillc = nodecolor[membership]
colors = [colorant"lightgray" for i in 1:78]
colors[42] = colorant"orange"
fig = Figure(resolution=(500,500))
ax = Axis(fig[1,1])
pos = Shell()(g) # = circular layout
graphplot!(ax, g,
layout=_->pos,
edge_color=colors,
node_color=nodefillc,
node_size=30,
nlabels=nodelabels,
nlabels_align=(:center, :center)
)
hidedecorations!(ax)
hidespines!(ax)
# add additional annotation to node 17
weightOffset = Point2(0, 0.045)
text!(ax, "0.001", position=pos[17] - weightOffset, space=:data, align=(:center, :top), fontsize=10)
display(fig)
Related
I am working on plotting a Network and it contains two different types of Nodes which I want to visualise with different shapes. For that I made an additional table in which I specified which structure is which type using a binary system. Now I want to specify in my plot function that the structures with 1 are to be triangles and the ones with 0 as circles.
My data for the Network is in the format of an adjacency matrix (I use igraph) and I am using ggnet2 for the plotting of it.
this is how I imported the data:
am <- as.matrix(read.csv2("mydata.csv", header = T, row.names = 1))
g <- graph_from_adjacency_matrix(am, mode = "undirected")
attr <- read.csv2("myattributes.csv", header = T, row.names = 1)
this is how I would plot it but I dont know how to specify the shape function
ggnet2(g, size = "degree", node.color = "darkgreen", shape = ??????)
Thanks in advance for your help!
Note that the package-requirements for plotting igraphs with ggnet2 include ggplot2, sna and network as well as intergraph as a bridge.
ggnet2 is prettier, sure, but the igraph-way is this:
g <- erdos.renyi.game(100,100,'gnm')
V(g)$shape <- sample(c('csquare','circle'), 100, replace=T)
plot(g, vertex.label = NA)
Note that I added two igraph-style shapes as vertex-attributes to g above. In ggent2 you can provide a vector with shapes, but they can be any values (even a factor), or numbers (the usual gray circle is 19. Try this out to plot in ggnet2
ggnet2(g, shape=19)
ggnet2(g, shape=10+round(1:100/10))
ggnet2(g, shape=factor(V(g)$shape))
V(g)$shape <- sample(c('One shape','Another shape'), 100, replace=T)
ggnet2(g, shape=V(g)$shape, size = "degree", node.color = "darkgreen")
Note that, if you add attributes to your vertices after separately loading attribute data (as you do above), it may be so that the very order of your data matters. Make sure your table import actually works as intended with the correct attribute being assigned to the correct vertex. I find it a good practice to tie all values as attributes on the igraph-object (edge- and vertex attributes alike) rather than letting the network data live in different dataframes or loose vectors to be combined in order to correctly visualise a network.
I would like to create a stacked area chart, similar to this for example, in Julia using Plots.
I know / suppose that you can do this if you directly use the Gadfly or PyPlot backends in Julia, but I was wondering if there was a recipe for this. If not, how can you contribute to the Plots Recipes? Would be a useful addition.
There's a recipe for something similar in
https://docs.juliaplots.org/latest/examples/pgfplots/#portfolio-composition-maps
For some reason the thumbnail looks broken now though (but the code works).
The exact plot in the matlab example can be produced by
plot(cumsum(Y, dims = 2)[:,end:-1:1], fill = 0, lc = :black)
As a recipe that would look like
#userplot AreaChart
#recipe function f(a::AreaChart)
fillto --> 0
linecolor --> :black
seriestype --> :path
cumsum(a.args[1], dims = 2)[:,end:-1:1]
end
If you want to contribute a recipe to Plots you can open a pull request on Plots, or, eg. on StatsPlots - there's a good description of contributing here: https://docs.juliaplots.org/latest/contributing/
It's a bit of reading, but very generally useful as an introduction to contributing to Julia packages.
You can read this thread in the Julia discourse forum where the question is developed in deep.
One solution posted there using Plots is :
# a simple "recipe" for Plots.jl to get stacked area plots
# usage: stackedarea(xvector, datamatrix, plotsoptions)
#recipe function f(pc::StackedArea)
x, y = pc.args
n = length(x)
y = cumsum(y, dims=2)
seriestype := :shape
# create a filled polygon for each item
for c=1:size(y,2)
sx = vcat(x, reverse(x))
sy = vcat(y[:,c], c==1 ? zeros(n) : reverse(y[:,c-1]))
#series (sx, sy)
end
end
a = [1,1,1,1.5,2,3]
b = [0.5,0.6,0.4,0.3,0.3,0.2]
c = [2,1.8,2.2,3.3,2.5,1.8]
sNames = ["a","b","c"]
x = [2001,2002,2003,2004,2005,2006]
plotly()
stackedarea(x, [a b c], labels=reshape(sNames, (1,3)))
(by user NiclasMattsson)
Other ways presented there include using the VegaLite.jl package.
I figured out a way to accomplish this but it requires a lot of guesswork and all the Venn or Euler diagram packages seem to only allow you to place the total number of occurrences inside the circle.
The data:
name=c('itm1','itm2','itm3','itm4','itm5','itm6','itm7','itm8','itm9','itm0')
x=c(5,2,3,5,6,7,7,8,9,2)
y=c(6,9,9,7,6,5,2,3,2,4)
z=data.frame(name,x,y)
Plotting the points and labeling them:
plot(z$x,z$y,type='n')
text(z$x,z$y,z$name)
Manually placing the circles over the points:
par(new=T)
symbols(3,7,circles=2.5,add=T,bg='#34692499',inches=F)
symbols(6,6,circles=1.5,add=T,bg='#64392499',inches=F)
symbols(8,3,circles=2,add=T,bg='#24399499',inches=F)
So this is a real tedious process of giving each item an x and y coordinate and then guessing where to place the circles and what radius to give them.
Ideally I would like to use the dataset I initially had which looks like this:
cat1=c('itm2','itm3','itm0')
cat2=c('itm1','itm4','itm5','itm6')
cat3=c('itm6','itm7','itm8','itm9')
And then just assign the points into the right circle. Is there a better way of doing this?
My sense, based on the thread discussion is to recommend using the UnSetR R package?
OK, why?
My personal feeling is that if we have more than five or seven groups the Venn diagram approach breaks down. For an overview of the various options available in this context I recommend you review:
Information Engineering Group Web Page
Resources on Set Visualization
the other useful website in my view is:
Venn Diagrams on R Studio
together they give good coverage of the options available.
Thus, my sense is that the core challenge here is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. So how to address?
Proposed Solution UnSet
Well, UnSet is focused on creating a task-driven aggregate view of the data relationships, it communicates the size and properties of aggregates and intersections. For me at least this seems a better way - it is a recommendation.
That and at the very least an alternate approach - I hope it helps.
UnSet Reference Materials:
UnSet Overview http://caleydo.org/tools/upset/
Shiny Application: https://gehlenborglab.shinyapps.io/upsetr/
R Package Source Code: https://github.com/hms-dbmi/UpSetR
Further reading pertaining to alternnate options: http://www.cvast.tuwien.ac.at/SetViz
UnSetR Vignettes
There are currently four vignettes that explain how to use the features included in the UpSetR package:
Basic Usage
Queries
Attribute Plots
Set Metadata
Unset Movie DataSet Example 1
if (!require(UpSetR)) install.packages("UpSetR")
movies <- read.csv(system.file("extdata", "movies.csv", package = "UpSetR"),
header = T, sep = ";")
upset(movies, nsets = 6, number.angles = 30, point.size = 3.5, line.size = 2,
mainbar.y.label = "Genre Intersections", sets.x.label = "Movies Per Genre",
text.scale = c(1.3, 1.3, 1, 1, 2, 0.75))
Unset Movie DataSet Example 2
upset(movies, sets = c("Action", "Adventure", "Comedy", "Drama", "Mystery",
"Thriller", "Romance", "War", "Western"), mb.ratio = c(0.55, 0.45), order.by = "freq")
If you don't mind doing this manually, you can speed the process up a lot by using locator:
points <- locator(2)
# click first at the circle centre, then somewhere on the circle edge
symbols(points$x[1], points$y[1],
circles = sqrt(sum(points$x - points$y)^2),
add = TRUE, bg = alpha('orange', .2), inches = F)
I am trying to create circular phylogenetic tree. I have this part of code:
fit<- hclust(dist(Data[,-4]), method = "complete", members = NULL)
nclus= 3
color=c('red','blue','green')
color_list=rep(color,nclus/length(color))
clus=cutree(fit,nclus)
plot(as.phylo(fit),type='fan',tip.color=color_list[clus],label.offset=0.2,no.margin=TRUE, cex=0.70, show.node.label = TRUE)
And this is result:
Also I am trying to show label for each node and to color branches. Any suggestion how to do that?
Thanks!
When you say "color branches" I assume you mean color the edges. This seems to work, but I have to think there's a better way.
Using the built-in mtcars dataset here, since you did not provide your data.
plot.fan <- function(hc, nclus=3) {
palette <- c('red','blue','green','orange','black')[1:nclus]
clus <-cutree(hc,nclus)
X <- as.phylo(hc)
edge.clus <- sapply(1:nclus,function(i)max(which(X$edge[,2] %in% which(clus==i))))
order <- order(edge.clus)
edge.clus <- c(min(edge.clus),diff(sort(edge.clus)))
edge.clus <- rep(order,edge.clus)
plot(X,type='fan',
tip.color=palette[clus],edge.color=palette[edge.clus],
label.offset=0.2,no.margin=TRUE, cex=0.70)
}
fit <- hclust(dist(mtcars[,c("mpg","hp","wt","disp")]))
plot.fan(fit,3); plot.fan(fit,5)
Regarding "label the nodes", if you mean label the tips, it looks like you've already done that. If you want different labels, unfortunately, unlike plot.hclust(...) the labels=... argument is rejected. You could experiment with the tiplabels(....) function, but it does not seem to work very well with type="fan". The labels come from the row names of Data, so your best bet IMO is to change the row names prior to clustering.
If you actually mean label the nodes (the connection points between the edges, have a look at nodelabels(...). I don't provide a working example because I can't imagine what labels you would put there.
Due to static graph prepared by ggplot, we are shifting our graphs to googleVis with interactive charts. But when it comes to categorization we are facing many problems. Let me give example which will help you understand:
#dataframe
df = data.frame( x = sample(1:100), y = sample(1:100), cat = sample(c('a','b','c'), 100, replace=TRUE) )
ggplot2 provides parameter like alpha, colour, linetype, size which we can use with categories like shown below:
ggplot(df) + geom_line(aes(x = x, y = y, colour = cat))
Not just line chart, but majority of ggplot2 graphs provide categorization based on column values. Now I would like to do the same in googleVis, based on value df$cat I would like parameters to get changed or grouping of line or charts.
Note:
I have already tried dcast to make multiple columns based on category column and use those multiple columns as Y input, but that it not what I would like to do.
Can anyone help me regarding this?
Let me know if you need more information.
vrajs5 you are not alone! We struggled with this issue. In our case we wanted to fill bar charts like in ggplot. This is the solution. You need to add specifically named columns, linked to your variables, to your data table for googleVis to pick up.
In my fill example, these are called roles, but once you see my syntax you can abstract it to annotations and other cool features. Google has them all documented here (check out superheroes example!) but it was not obvious how it applied to r.
#mages has this documented on this webpage, which shows features not in demo(googleVis):
http://cran.r-project.org/web/packages/googleVis/vignettes/Using_Roles_via_googleVis.html
EXAMPLE ADDING NEW DIMENSIONS TO GOOGLEVIS CHARTS
# in this case
# How do we fill a bar chart showing bars depend on another variable?
# We wanted to show C in a different fill to other assets
suppressPackageStartupMessages(library(googleVis))
library(data.table) # You can use data frames if you don't like DT
test.dt = data.table(px = c("A","B","C"), py = c(1,4,9),
"py.style" = c('silver', 'silver', 'gold'))
# Add your modifier to your chart as a new variable e.g. py1.style
test <-gvisBarChart(test.dt,
xvar = "px",
yvar = c("py", "py.style"),
options = list(legend = 'none'))
plot(test)
We have shown py.style deterministically here, but you could code it to be dependent on your categories.
The secret is myvar.googleVis_thing_youneed linking the variable myvar to the googleVis feature.
RESULT BEFORE FILL (yvar = "py")
RESULT AFTER FILL (yvar = c("py", "py.style"))
Take a look at mages examples (code also on Github) and you will have cracked the "categorization based on column values" issue.