I have a very large (shapefile) road network to read as a linear network in spatstat. So I am trying to build a basic network from reading vertices and edges as discussed in chapter 17 of book - spatial point patterns by Baddeley et al
I attach my data here
Using this code below I get an error Error: length(x0) == length(x1) is not TRUE. It is not clear to me what is x0 and x1 in order to be able to find the problem.
library(maptools)
library(spatstat)
setwd("~/documents/rwork/traced/a")
pt <- readShapePoints("collected.shp") #read vertices from a shapefile.
edgeRecords<-read.delim("edgelist.txt") #read edge connectivity list
ed<-data.frame(from=edgeRecords$from,to=edgeRecords$to)
xx<-pt#bbox[1,]#read x bounds of owin
yy<-pt#bbox[2,]#read y bounds of owin
v<-ppp(x=pt#coords[,1], y=pt#coords[,2], xx,yy) #read list of vertices
edg<-as.matrix(ed) # read node pairs as matrix
built_network<-linnet(v,edges = edg)
This results in error
Error: length(x0) == length(x1) is not TRUE
As in one of the comments above. I noticed that GIS indexing starts from 0 while R indexing starts from 1.
So to solve this problem, I just added +1 to the edge matrix. Because if you have collected your edge matrix from a GIS software it will have references to node zero in either from_node or to_node. If your edge matrix in R is em then add +1 , like so: em+1 . A sample code could be like this
edgelist <- read.delim("edgelist.txt")
em <- matrix(c(edgelist$from, edgelist$to), ncol=2) +1
net <- linnet(n,edges = em)
plot(net)
This solved the problem for me. Hope it helps someone. Or if someone has another solution, please feel free to share.
Related
first post :)
I've been transitioning my R code from sp() to sf()/stars(), and one thing I'm still trying to grasp is accounting for the area in my grids.
Here's an example code to explain what I mean.
library(stars)
library(tidyverse)
# Reading in an example tif file, from stars() vignette
tif = system.file("tif/L7_ETMs.tif", package = "stars")
x = read_stars(tif)
x
# Get areas for each grid of the x object. Returns stars object with "area" in units of [m^2]
x_area <- st_area(x)
x_area
I tried loosely adopting code from this vignette (https://github.com/r-spatial/stars/blob/master/vignettes/stars5.Rmd) to divide each value in x by it's grid area, and it's not working as expected (perhaps because my objects are stars and not sf?)
x$test1 = x$L7_ETMs.tif / x_area # Some computationally intensive calculation seems to happen, but doesn't produce the results I expect?
x$test1 = x$L7_ETMs.tif / x_area$area # Throws error, "non-conformable arrays"
What does seem to work is the following.
x %>%
mutate(test1 = L7_ETMs.tif / units::set_units(as.numeric(x_area$area), m^2))
Here are the concerns I have with this code.
I worry that as I turn the x_area$area (a matrix, areas in lat/lon) into a numeric vector, I may mess up the lat/lon matching between the grid and it's area. I did some rough testing to see if the areas match up the way I expect them to, but can't escape the worry that this could lead to errors that are difficult to catch.
It just doesn't seem clean that I start with "x_area" in the correct units, only to remove then set the units again during the computation.
Can someone suggest a "cleaner" implementation for what I'm trying to do, i.e. multiplying or dividing grids by its area while maintaining units throughout? Or convince me that the code I have is fine?
Thanks!
I do not know how to improve the stars code, but you can compare the results you get with this
tif <- system.file("tif/L7_ETMs.tif", package = "stars")
library(terra)
r <- rast(tif)
a <- cellSize(r, sum=FALSE)
x <- r / a
With planar data you could do this when it is safe to assume there is no distortion (generally not the case, but it can be the case)
y <- r / prod(res(r))
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I hope you can help me with this problem i can't find how to overcome. Sorry if I made some mistakes while writing this post, my english is a bit rusty right now.
Here is the question. I have .shp data that I want to analyze in R. The .shp can be either lines that represent lines of traps we set to catch octopuses or points located directly over those lines, representing where we had catured one.
The question i'm trying to answer is: Are octopuses statistically grouped or not?
After a bit of investigation it seems to me that i need to use R and its linearK function to answer that question, using the libraries Maptools, SpatStat and Sp.
Here is the code i'm using in RStudio:
Loading the libraries
library(spatstat)
library(maptools)
library(sp)
Creating a linnet object with the track
t1<- as.linnet(readShapeSpatial("./20170518/t1.shp"))
I get the following warning but it seems to work
Warning messages:
1: use rgdal::readOGR or sf::st_read
2: use rgdal::readOGR or sf::st_read
Plotting it to be sure everything is ok
plot(t1)
Creating a ppp object with the points
p1<- as.ppp(readShapeSpatial("./20170518/p1.shp"))
I get the same warning here, but the real problems start when I try to plot it:
> plot(p1)
Error in if (!is.vector(xrange) || length(xrange) != 2 || xrange[2L] < :
missing value where TRUE/FALSE needed
In addition: Warning messages:
1: Interpretation of arguments maxsize and markscale has changed (in spatstat version 1.37-0 and later). Size of a circle is now measured by its diameter.
2: In plot.ppp(x, ..., multiplot = FALSE, do.plot = FALSE) :
All mark values are NA; plotting locations only.
3: In plot.ppp(x, ..., multiplot = FALSE, do.plot = FALSE) :
All mark values are NA; plotting locations only.
4: In plot.ppp(x, ..., multiplot = FALSE, do.plot = FALSE) :
All mark values are NA; plotting locations only.
5: In plot.ppp(x, ..., multiplot = FALSE, do.plot = FALSE) :
All mark values are NA; plotting locations only.
6: In plot.ppp(x, ..., multiplot = FALSE, do.plot = FALSE) :
All mark values are NA; plotting locations only.
7: In plot.ppp(x, ..., multiplot = FALSE, do.plot = FALSE) :
All mark values are NA; plotting locations only.
Now what is left is to join the objects in a lpp object and to analyze it with the linearK function
> pt1 <- lpp(p1,t1)
> linearK(pt1)
Function value object (class ‘fv’)
for the function r -> K[L](r)
......................................
Math.label Description
r r distance argument r
est {hat(K)[L]}(r) estimated K[L](r)
......................................
Default plot formula: .~r
where “.” stands for ‘est’
Recommended range of argument r: [0, 815.64]
Available range of argument r: [0, 815.64]
This is my situation right now. What i dont know is why the plot function is not working with my ppp object and how to understant the return of the linearK function. Help(linearK) didn't provide any clue. Since i have a lot of tracks, each with its set of points, my desired outcome would be some kind of summary like x tracks analized, a grouped, b dispersed and c unkown.
Thank you for your time, i'll greatly appreciate if you can help me solve this problem.
Edit: Here is a link to a zip file containing al the shp files of one day, both tracks and points, and a txt file with my code. https://drive.google.com/open?id=0B0uvwT-2l4A5ODJpOTdCekIxWUU
First two pieces of general advice: (1) each time you create a complicated object, print it at the terminal, to see if it is what you expected. (2) When you get an error, immediately type traceback() and copy the output. This will reveal exactly where the error is detected.
A ppp object must include a specification of the study region (window). In your code, the object p1 is created by converting data of class SpatialPointsDataFrame, which do not include a specification of the study region, converted via the function as.ppp.SpatialPointsDataFrame, into an object of class ppp in which the window is guessed by taking the bounding box of the coordinates. Unfortunately, in your example, there is only one data point in p1, so the default bounding box is a rectangle of width 0 and height 0. [This would have been revealed by printing p1.] Such objects can usually be handled by spatstat, but this particular object triggers a bug in the function plot.solist which expects windows to have non-zero size. I will fix the bug, but...
In your case, I suggest you do
Window(p1) <- Window(t1)
immediately after creating p1. This will ensure that p1 has the window that you probably intended.
If all else fails, read the spatstat vignette on shapefiles...
I have managed to find a solution. As Adrian Baddeley noticed there was a problem with the owin object. That problem seems to be bypassed (not really solved) if I create the ppp object in a manual way instead of converting my set of points.
I have also changed the readShapeFile function for the rgdal::readOGR, since the first once was deprecated, and that was the reason of the warnings I was getting.
This is the R script i'm using right now, commented to clarify:
#first install spatstat, maptools y sp
#load them
library(spatstat)
library(maptools)
library(sp)
#create an array of folders, will add more when everything works fine
folders=c("20170518")
for(f in folders){
#read all shp from that folder, both points and tracks
pointfiles <- list.files(paste("./",f,"/points", sep=""), pattern="*.shp$")
trackfiles <- list.files(paste("./",f,"/tracks", sep=""), pattern="*.shp$")
#for each point and track couple
for(i in 1:length(pointfiles)){
#create a linnet object with the track
t<- as.linnet(rgdal::readOGR(paste("./",f,"/tracks/",trackfiles[i], sep="")))
#plot(t)
#create a ppp object for each set of points
pre_p<-rgdal::readOGR(paste("./",f,"/points/",pointfiles[i], sep=""))
#plot(p)
#obtain the coordinates the current set of points
c<-coordinates(pre_p)
#create vector of x coords
xc=c()
#create vector of y coords
yc=c()
#not a very good way to fill my vectors but it works for my study area
for(v in c){
if(v>4000000){yc<-c(yc,v)}
else {if(v<4000000 && v>700000){xc<-c(xc,v)}}
}
print(xc)
print(yc)
#create a ppp object using the vectors of x and y coords, and a window object
#extracted from my set of points
p=ppp(xc,yc,Window(as.ppp(pre_p)))
#join them into an lpp object
pt <- lpp(p,t)
#plot(pt)
#analize it with the linearK function, nsim=9 for testing purposes
#envelope.lpp is the method for analyzing linear point patterns
assign(paste("results",f,i,sep="_"),envelope.lpp(pt, nsim=9, fun=linearK))
}#end for each points & track set
}#end for each day of study
So as you can see this script is testing for CSR each couple of points and track for each day, working fine right now. Unfortunately I have not managed to create a report or reportlike with the results yet (or even to fully understand them), I'll keep working on that. Of course I can use any advice you have, since this is my first try with R and many newie mistakes will happen.
The script and the shp files with the updated folder structure can be found here(113 KB size)
So my problem is quite difficult to describe so I hope I can make my question as clear as possible.
I use the rLiDAR package to load a .las file into R and afterwards convert it into a SpatialPointsDataFrame using the sp package.
So my SpatialPointsDataFrame is quite dense.
Now I want to define a buffer of 0.5 meters and loop (iterate) with him (the buffer) through the points, choosing always the point with the highest Z value within the buffer, as the next point to jump to.This should be repeated until there isn't any point within the buffer with an higher Z value as the current. All values (or perhaps the X and Y values) of this "found" point should then be written into a list/dataframe and the process should be repeated until all such highest points are found.
Thats the code I got so far:
>library(rLiDAR)
>library(sp)
>rLAS<-readLAS("Test.las",short=FALSE)
>PointCloud<- data.frame(rLAS)
>coordinates(PointCloud) <- c("X", "Y")
Well I googled extensively but I could not find any clues how to proceed further...
I dont even know which packages could be of help, I guess perhaps spatstat as my question would probably go into the spatial point pattern analysis.
Does anyone have some ideas how to archive something like that in R? Or is something like that not possible? (Do I perhaps have to skip to python to make something like this work?)
Help would gladly be appreciated.
If you want to get the set of points which are the local maxima within a 0.5m radius circle around each point, this should work. The gist of it is:
Convert the LAS points to a SpatialPointsDataFrame
Create a buffered polygon set with overlapping polygons
Loop through all buffered polygons and find the desired element within the buffer -- in your case, it's the one with the maximum height.
Code below:
library(rLiDAR)
library(sp)
library(rgeos)
rLAS <- readLAS("Test.las",short=FALSE)
PointCloud <- data.frame(rLAS)
coordinates(PointCloud) <- c("X", "Y")
Finish creating the SpatialPointsDataFrame from the LAS source. I'm assuming the field with the point height is PointCloud$value
pointCloudSpdf <- SpatialPointsDataFrame(data=PointCloud,xy)
Use rgeos library for intersection. It's important to have byid=TRUE or the polygons will get merged where they intersect
bufferedPoints <- gBuffer(pointCloudSpdf,width=0.5,byid=TRUE)
# Save our local maxima state (this will be updated)
localMaxes <- rep(FALSE,nrow(PointCloud))
i=0
for (buff in 1:nrow(bufferedPoint#data)){
i <- i+1
bufPolygons <- bufferedPoints#polygons[[i]]
bufSpPolygons <- SpatialPolygons(list(bufPolygons))
bufSpPolygonDf <-patialPolygonsDataFrame(bufSpPolygons,bufferedPoints#data[i,])
ptsInBuffer <- which(!is.na(over(pointCloudSpdf,spPolygonDf)))
# I'm assuming `value` is the field name containing the point height
localMax <- order(pointCloudSpdf#data$value[ptsInBuffer],decreasing=TRUE)[1]
localMaxes[localMax] <- TRUE
}
localMaxPointCloudDf <- pointCloudSpdf#data[localMaxes,]
Now localMaxPointCloudDf should contain the data from the original points if they are a local maximum. Just a warning -- this isn't going to be super fast if you have a lot of points. If that ends up being a concern you may be smarter about pre-filtering your points using a smaller grid and extract from the raster package.
That would look something like this:
Make the cell size small enough so that each 0.5m buffer will intersect at least 4 raster cells -- err on smaller since we are comparing circles to squares.
library(raster)
numRows <- extent(pointCloudSpdf)#ymax-extent(pointCloudSpdf)#ymin/0.2
numCols <- extent(pointCloudSpdf)#xmax-extent(pointCloudSpdf)#xmin/0.2
emptyRaster <- raster(nrow=numRows,ncol=numCols)
rasterize will create a grid with the maximum value of the given field within a cell. Because of the square/circle mismatch this is only a starting point to filter out obvious non-maxima. After this we will have a raster in which all the local maxima are represented by cells. However, we won't know which cells are maxima in the 0.5m radius and we don't know which point in the original feature layer they came from.
r <- rasterize(pointCloudSpdf,emptyRaster,"value",fun="max")
extract will give us raster values (i.e., the highest value for each cell) that each point intersects. Recall from above that all the local maxima will be in this set, although some values will not be 0.5m radius local maxima.
rasterMaxes <- extract(r,pointCloudSpdf)
To match up the original points with the raster maxes, just subtract the raster value at each point from that point's value. If the value is 0, then the values are the same and we have a point with a potential maximum. Note that at this point we are only merging the points back to the raster -- we will have to throw some of these out because they are "under" a 0.5m radius with a higher local max even though they are the max in their 0.2m x 0.2m cell.
potentialMaxima <- which(pointCloudSpdf#data$value-rasterMaxes==0)
Next, just subset the original SpatialPointsDataFrame and we'll do the more exhaustive and accurate iteration over this subset of points since we should have thrown out a bunch of points which could not have been maxima.
potentialMaximaCoords <- coordinates(pointCloudSpdf#coords[potentialMaxima,])
# using the data.frame() constructor because my example has only one column
potentialMaximaDf <- data.frame(pointCloudSpdf#data[potentialMaxima,])
potentialMaximaSpdf <-SpatialPointsDataFrame(potentialMaximaCoords,potentialMaximaDf)
The rest of the algorithm is the same but we are buffering the smaller dataset and iterating over it:
bufferedPoints <- gBuffer(potentialMaximaSpdf, width=0.5, byid=TRUE)
# Save our local maxima state (this will be updated)
localMaxes <- rep(FALSE, nrow(PointCloud))
i=0
for (buff in 1:nrow(bufferedPoint#data)){
i <- i+1
bufPolygons <- bufferedPoints#polygons[[i]]
bufSpPolygons <- SpatialPolygons(list(bufPolygons))
bufSpPolygonDf <-patialPolygonsDataFrame(bufSpPolygons,bufferedPoints#data[i,])
ptsInBuffer <- which(!is.na(over(pointCloudSpdf, spPolygonDf)))
localMax <- order(pointCloudSpdf#data$value[ptsInBuffer], decreasing=TRUE)[1]
localMaxes[localMax] <- TRUE
}
localMaxPointCloudDf <- pointCloudSpdf#data[localMaxes,]
I am new to R/igraph. I would like to remove N nodes randomly from a graph. However, I could not find the right way to do that. I have generated the Erdos-Renyi graph with the help of the igraph package with 400 vertices.
igraph provides the deletion of the vertices, but not in the random way.
For example: delete.vertices(graph, v).
I referred to this documentation.
I also searched the web and previous questions on Stack Overflow, but could not get the right answer.
Can anyone please tell or refer me to documentation on how to remove the N (lets say N = 100) random nodes?
Basically you just need to generate a vector of random numbers ranging from 1 to 400:
random.deletes <- runif(n=100, min=1, max=400)
And then apply it:
my.new.graph <- delete.vertices(graph, random.deletes)
Of course, both can be done at once but you'd lose track of the deleted nodes:
my.new.graph <- delete.vertices(graph, runif(n=100, min=1, max=400))
I have some experience with R as a statistics platform, but am inexperienced in image based maths. I have a series of photographs (tiff format, px/µm is known) with holes and irregular curves. I'd like to measure the shortest distance between a hole and the closest curve for that particular hole. I'd like to do this for each hole in a photograph. The holes are not regular either, so maybe I'd need to tell the program what are holes and what are curves (ImageJ has a point and segmented line functions).
Any ideas how to do this? Which package should I use in R? Would you recommend another program for this kind of task?
EDIT: Doing this is now possible using sclero package. The package is currently available on GitHub and the procedure is described in detail in the tutorial. Just to illustrate, I use an example from the tutorial:
library(devtools)
install_github("MikkoVihtakari/sclero", dependencies = TRUE)
library(sclero)
path <- file.path(system.file("extdata", package = "sclero"), "shellspots.zip")
dat <- read.ijdata(path, scale = 0.7812, unit = "um")
shell <- convert.ijdata(dat)
aligned <- spot.dist(shell)
plot(aligned)
It is also possible to add sample spot sizes using the functions provided by the sclero package. Please see Section 2.5 in the tutorial.
There's a tool for edge detection written for Image J that might help you first find the holes and the lines, and clarify them. You find it at
http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:edge_detection:start
Playing around with the settings for the tresholding and the hysteresis can help in order to get the lines and holes found. It's difficult to tell whether this has much chance of working without seeing your actual photographs, but a colleague of mine had good results using this tool on FRAP images. I programmed a ImageJ tool that can calculate recoveries in FRAP analysis based on those images. You might get some ideas for yourself when looking at the code (see: http://imagejdocu.tudor.lu/doku.php?id=plugin:analysis:frap_normalization:start )
The only way I know you can work with images, is by using EBImage that's contained in the bioconductor system. The package Rimage is orphaned, so is no longer maintained.
To find the shortest distance: once you have the coordinates of the lines and holes, you can go for the shotgun approach : calculate the distances between all points and the line, and then take the minimum. An illustration about that in R :
x <- -100:100
x2 <- seq(-70,-50,length.out=length(x)/4)
a.line <- list(x = x,
y = 4*x + 5)
a.hole <- list(
x = c(x2,rev(x2)),
y = c(200 + sqrt(100-(x2+60)^2),
rev(200 - sqrt(100-(x2+60)^2)))
)
plot(a.line,type='l')
lines(a.hole,col='red')
calc.distance <- function(line,hole){
mline <- matrix(unlist(line),ncol=2)
mhole <- matrix(unlist(hole),ncol=2)
id1 <- rep(1:nrow(mline),nrow(mhole))
id2 <- rep(1:nrow(mhole), each=nrow(mline))
min(
sqrt(
(mline[id1,1]-mhole[id2,1])^2 +
(mline[id1,2]-mhole[id2,2])^2
)
)
}
Then :
> calc.distance(a.line,a.hole)
[1] 95.51649
Which you can check mathematically by deriving the equations from the circle and the line. This goes fast enough if you don't have millions of points describing thousands of lines and holes.