I'm trying to string up some patio lights. Based on another question I asked, I realize I need an algorithm to solve a Route Inspection Problem to figure out the most efficient route the lights should take so there's minimal duplicate edges covered with lights. After some searching I realized that perhaps something like this would be my best bet: Solving Chinese Postman algorithm with eulerization.
However, I'm having trouble creating the graph.
Here's what it needs to look like:
pink circles represent places in the structure I can hang lights from
"Start" is the only available electrical outlet
The yellow dots represent all the places lights should cover
And here's what my graph looks like after referencing this post: Visualizing distance between nodes according to weights - with R:
As you can see, all the nodes are in the correct place, but the edges are connecting where they shouldn't connect. Here's my code:
library(igraph)
gg<-graph.ring(20)
ll=matrix(
c( 0,0, 75.25,0, 150.5,0, 225.8125,0, 302.8125,0,
0,-87, 302.8125,-87,
0,-173.8125, 302.8125,-173.8125,
0,-260.9375, 302.8125,-260.9375,
16,-384.3125, 302.8125,-384.3125,
16,-435.9575, 302.8125,-435.9375,
16,-525.1875, 75.25,-525.1875, 150.5,-525.1875, 225.8125,-525.1875, 302.8175,-525.1875),
ncol=2,byrow=TRUE)
plot(gg,layout=ll)
I think this has something to do with the nature of graph.ring, but I am unable to figure out another way to define the graphs' edges' lengths without error.
I think you can use graph_from_edgelist for a precise specification of which nodes to connect. It is sufficient to specify which nodes to connect in which order. Nice question btw!
gg <- graph_from_edgelist(cbind(c(1:4, 6, 8, 10, 12, 14, 16:19, 1, 6, 8, 21, 12, 14, 5, 7, 9, 11, 13, 15),
c(2:5, 7, 9, 11, 13, 15, 17:20, 6, 8, 10, 12, 14, 16, 7, 9, 11, 13, 15, 20)))
ll=matrix(
c( 0,0, 75.25,0, 150.5,0, 225.8125,0, 302.8125,0,
0,-87, 302.8125,-87,
0,-173.8125, 302.8125,-173.8125,
0,-260.9375, 302.8125,-260.9375,
16,-384.3125, 302.8125,-384.3125,
16,-435.9575, 302.8125,-435.9375,
16,-525.1875, 75.25,-525.1875, 150.5,-525.1875, 225.8125,-525.1875, 302.8175,-525.1875, 16, -260.9375),
ncol=2,byrow=TRUE)
plot(gg,layout=ll, edge.arrow.size = 0, vertex.size = c(rep(18, 20), 0),
edge.color="orange")
I added a node (n 21) to allow a branching that is similar to your scheme. Does this look more or less as it should?
I had a look at the previous post on Stack Overflow (the one you suggested) to try making this an Euler cycle. Actually, the custom function does work out of the box, but you may want to double check if you can use the resulting solution or not. Maybe, you could try defining a better connection design before "eulerizing" the circuit. This is what I got.
# load custom f(x) as in
# https://stackoverflow.com/questions/40576910/solving-chinese-postman-algorithm-with-eulerization/40596816#40596816
eulerian <- make.eulerian(gg)
eulerian$info
g <- eulerian$graph
# set the layout as before to keep the circuit formatted according to your specs
par(mfrow=c(1,2))
plot(gg,layout=ll, edge.arrow.size = 0, vertex.size = c(rep(18, 20), 0),
edge.color="orange", main = "Proposed")
plot(g,layout=ll, edge.arrow.size = 0, vertex.size = c(rep(18, 20), 0),
edge.color="orange", main = "Eulerized")
Is there a cumulative graph package in R? Or how might I create a cumulative graph in R?
For example, given values of 2, 4, 2, 2, they values should be plotted as 2, 6, 8, 10 d3 example here. Then forming a stair step pattern as with other cumulative records
As per ?plot.default - there is a "stairs" plotting method, which can be combined with cumsum() to give the result you want I think:
plot(cumsum(c(2,4,2,2)), type="s")
I applied the decision tress algorithm to famous breast cancer data set from UCI after removing 16 records with missing rows. The tree I obtained is given below. As it can be seen that the small number appearing on top of the node are not in order. The numbers are 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 28 and 20. Are the missing nodes deleted due to pruning? Can I enable the option to see them? Can I correct the sequence according to the nodes appearing i.e. 1 to 13?
I am trying to calculate different networks measures such as betweenness() and constraint()in my network using Igraph in R. My problem is that I am not looking at individuals but on groups of individuals in my network. Therefore I have to contract the vertices before I calculate the different network measures. Thus far I have been able to create a basic code to calculate the measures. But I have a total of ca. 900 groups (with up to 7 members per group) in a network of ca. 70.000 nodes and 250.000 edges. So I am trying to create a loop to automate the approach and make life a little bit easier.
Now I want to present my approach to calculate the constrain().
# load package
library(igraph)
# load data and create a weighted edgelist
df <- data.frame(from=c(6, 9, 10, 1, 7, 8, 8, 4, 5, 2, 5, 10), to=c(3, 4, 2, 5, 10, 1, 9, 10, 6, 9, 3, 6), weight=c(4, 2, 1, 2, 3, 3, 1, 1, 4, 5, 2, 2))
g <- graph.data.frame(df, directed =FALSE)
#import groups
groups <- "
1 5 8
2
10 7 "
subv <- read.table(text = groups, fill = TRUE, header = FALSE)
I would like to loop the upcoming code , to calculate not each constraint() separately. But for all the three groups given in the reproducible example at once.
#create a subvector of the first group and delete all the NA entries
subv1 <- c(as.numeric(as.vector(subv[1,])))
subv1 <- subv1[!is.na(subv1)]
#save subvector as charcter
subv1 <- as.character(subv1)
#creat subgraph with the nodes of group 1 from graph and add their 1st neighbors
g2 <- induced.subgraph(graph=g ,vids=unlist(neighborhood(graph=g ,order=1, nodes = subv1)))
#identify the igraph IDs of the nodes in the first group
match("1", V(g2)$name)
match("5", V(g2)$name)
match("8", V(g2)$name)
#create a contract vector and contract the vertices from largest to smallest using the output from match
convec1 <- c(1:(5-1), 3, 5:(vcount(g2)-1))
g3 <- contract.vertices(g2, convec1, vertex.attr.comb=toString)
convec2 <- c(1:(4-1), 3, 4:(vcount(g3)-1))
g4 <- contract.vertices(g3, convec2, vertex.attr.comb=toString)
#remove the selfloops and sum the weight attributes for the created graph
g5 <- simplify(g4, remove.loops = TRUE, edge.attr.comb=list(weight="sum"))
# calculate the constraint measure for the vertex 1, 5, 8
constraint(g5, nodes=3, weights=NULL)
So now I have the constraint measure for the first group. For the second and third I would have to repeat my steps again. This would be feasible, but as I stated I have 900 groups. Is there any possibility to loop this?
Please let me know if the give example is unclear as I am new to R and Stackoverflow.
This question already has answers here:
Finding local maxima and minima
(17 answers)
Closed 9 years ago.
I calculate the density function for the following data:
> dput(mydat)
c(-20, -13, 30, 4, -4, 34, 27, 19, 13.5, 15, 13, 18, 10, 12,
21, -0.769999999999996, 2.5, -7, 0, -30.6, 6.39999999999999,
-18.6, -0.199999999999989, -20.4, -19.9, 4.60000000000001, -19.4,
4.5, -9, -15, 9, -1, -14, 8, 6, -17, 5, 7)
> myden = density(mydat) # default kernel and bandwidth
which gives me this result:
I want to find the location of the two density peaks. I initially thought of using diff() on myden$y and then check for all locations where there is a sign change, using this as a condition to select the X-axis values. I tried it on a few test vectors but I was not getting the expected result and I suspect it is not that simplistic.
Is there a straightforward way to accomplish this? I want a solution that will be repeatable because I will do this as part of a random simulation study with ~ e+05 realisations, and it could happen that the number of peaks vary across the simulation.
I often use pastecs::turnpoints to find local maxima and minima.
Use which.max:
myden$x[which.max(myden$y)]
# [1] 5.91428
You can test this visually:
plot(myden, col='red')
abline(v=myden$x[which.max(myden$y)])