We are using igraph and R to detect communities in a network. The detection using cluster_walktrap is working great:
e <- cluster_walktrap(g)
com <-membership(e)
print(com)
write.csv2(com, file ="community.csv", sep=",")
The result is printed fine using print with the number and the community number that it belongs to but we have a problem in writing the result in the csv file and I have an error : cannot coerce class ""membership"" to a data.frame
How can I write the result of membership in a file ?
Thanks
Convert the membership object to numeric. write.csv and write.csv2 expect a data frame or matrix. The command tries to coerce the object into a data frame, which the class membership resists. Since membership really is just a vector, you can convert it a numeric. Either:
write.csv2(as.numeric(com), file ="community.csv")
Or:
com <- as.numeric(com)
write.csv2(com, file ="community.csv")
Oh, and you don't need the sep = "," argument for write.csv.
If you want to create table of vertex names/numbers and groups:
com <- cbind(V(g),e$membership) #V(g) gets the number of vertices
com <- cbind(V(g)$name,e$membership) #To get names if your vertices are labeled
I don't know if you guys resolved the problem but I did the following using R:
```
# applying the community method
com = spinglass.community(graph_builted,
weights = graph_builted$weights,
implementation = "orig",
update.rule = "config")
# creating a data frame to store the results
type = c(0)
labels = c(0)
groups = c(0)
res2 = data.frame(type, labels, groups)
labels = com$names # here you get the vertices names
groups = com$membership # here you get the communities indices
# here you save the information
res = data.frame(type = "spinGlass1", labels, groups)
res2 = rbind(res2, res)
# then you save the .csv file
write.csv(res2, "spinglass-communities.csv")
```
That resolves the problem for me.
Best regards.
Related
I have multiple files containing the data in tabulated form. I want to generate a 3-dimensional array in which the data from each file stored in the third dimension. For example, if I have 10 files then data from the first file will store in the first layer of 3D array, data from the second file in the second layer and so on.
Here is the dummy code I am using but it does not work correctly.
# reading data from the file ( I have a list of files names as fname)
dataDum <- read.table(fname[i],header = F, sep =';', skip=121, stringsAsFactors = FALSE)
# Assigning data to the array. I have already generated an empty array with the desired dimension
finaldata[, , i]=dataDum
It is not clear "why" your code is not working properly, as there is not reproducible example. As it is it should work correctly given the inputs are as expected. For example:
arr <- array(data = 0, dim = c(10,10,3));
for(i in 1:3){
mat <- matrix(rnorm(10^2), ncol = 10);
arr[,,i] <- mat
}
arr
If an error occurs it is likely due to dataDum being a data.frame. Explicitly using as.matrix(dataDum) would fix such an issue.
I am trying to create a loop where I select one file name from a list of file names, and use that one file to run read.capthist and subsequently discretize, fit, derived, and save the outputs using save. The list contains 10 files of identical rows and columns, the only difference between them are the geographical coordinates in each row.
The issue I am running into is that capt needs to be a single file (in the secr package they are 'captfile' types), but I don't know how to select a single file from this list and get my loop to recognize it as a single entity.
This is the error I get when I try and select only one file:
Error in read.capthist(female[[i]], simtraps, fmt = "XY", detector = "polygon") :
requires single 'captfile'
I am not a programmer by training, I've learned R on my own and used stack overflow a lot for solving my issues, but I haven't been able to figure this out. Here is the code I've come up with so far:
library(secr)
setwd("./")
files = list.files(pattern = "female*")
lst <- vector("list", length(files))
names(lst) <- files
for (i in 1:length(lst)) {
capt <- lst[i]
femsimCH <- read.capthist(capt, simtraps, fmt = 'XY', detector = "polygon")
femsimdiscCH <- discretize(femsimCH, spacing = 2500, outputdetector = 'proximity')
fit <- secr.fit(femsimdiscCH, buffer = 15000, detectfn = 'HEX', method = 'BFGS', trace = FALSE, CL = TRUE)
save(fit, file="C:/temp/fit.Rdata")
D.fit <- derived(fit)
save(D.fit, file="C:/temp/D.fit.Rdata")
}
simtraps is a list of coordinates.
Ideally I would also like to have my outputs have unique identifiers as well, since I am simulating data and I will have to compare all the results, I don't want each iteration to overwrite the previous data output.
I know I can use this code by bringing in each file and running this separately (this code works for non-simulation runs of a couple data sets), but as I'm hoping to run 100 simulations, this would be laborious and prone to mistakes.
Any tips would be greatly appreciated for an R novice!
The simplest description of what I am trying to do is that I have a column in a data.frame like 1,2,3,..., n, 1,2,3,...n,.... and I want group the first 1...n as 1 the second 1...n as 2 and so on.
The full context is; I am using the R spcosa package to do equal area stratification composite sampling on parcels of land. I start with a shape file from a GIS that contains a number of polygons (land parcels). The end result I want is a GIS file with each of the strata and sample locations in a GIS file format with each stratum and sample location labeled by land parcel, stratum and sample id. So far I can do all this except one bit which is identifying the stratum that the samples belongs too and including it in the sample label. The sample label needs to look like "parcel#-strata#-composite# (where # is the number). In practice I don't need this actual label but as separate attributes in GIS file.
The basic work flow is a follows
For each individual polygon using spcosa::stratify I divide it into a number of equal area strata like
strata.CSEA <- stratify(poly[i,], nStrata = n, nTry = 1, equalArea = TRUE, nGridCells = x)
Note spcosa::stratify generates a CompactStratificationEqualArea object. I cocerce this to a SpatialPixelData then use rasterToPolygon to be able to output it as a GIS file.
I then generate the sample locations as follows:
samples.SPRC <- spsample(strata.CSEA, n = n, type = "composite")
spcosa::spsample creates a SamplingPatternRandomComposite object. I coerce this to a SpatialPointsDataFrame
samples.SPDF <- as(samples.SPRC, "SpatialPointsDataFrame")
and add two columns to the #data slot
samples.SPDF#data$Strata <- "this is the bit I can't do yet"
samples.SPDF#data$CEA <- poly[i,]$name
I can then write samples.SPDF as a GIS file ( ie writeOGE) with all the wanted attributes.
As above the part I can't sort out is how the sample ids relate to the strata ids. The sample points are a vector like 1,2,3...n, 1,2,3...n,.... How do I extract which sample goes with which strata? As actual strata number are arbitrary, I can just group ( as per my simple question above) but ideally I would like to use the numbering of the actual strata so everything lines up.
To give any contributors access to a hands on example I copy below the code from the spcosa documentation slightly modified to generate the correct objects.
# Note: the example below requires the 'rgdal'-package You may consider the 'maptools'-package as an alternative
if (require(rgdal)) {
# read a vector representation of the `Farmsum' field
shpFarmsum <- readOGR(
dsn = system.file("maps", package = "spcosa"),
layer = "farmsum"
)
# stratify `Farmsum' into 50 strata
# NB: increase argument 'nTry' to get better results
set.seed(314)
myStratification <- stratify(shpFarmsum, nStrata = 50, nTry = 1, equalArea = TRUE)
# sample two sampling units per stratum
mySamplingPattern <- spsample(myStratification, n = 2 type = "composite")
# plot the resulting sampling pattern on
# top of the stratification
plot(myStratification, mySamplingPattern)
}
Maybe order() function can help you
n <- 10
dat <- data.frame(col1 = rep(1:n, 2), col2 = rnorm(2*n))
head(dat)
dat[order(dat$col1), ]
I did not get where the "ID" (1,2,3...n) is to be found; so let's assume you have your SpatialPolygonsDataFrame called shpFarmsum with a attribute data column "ID". You can access this column via shpFarmsum$ID. Therefore, if you want to create individual subsets for each ID this is one way to go:
for (i in unique(shpFarmsum$ID)) {
tempSubset shpFarmsum[shpFarmsum$ID == i,]
writeOGR(tempSubset, ".", paste0("subset_", i), driver = "ESRI Shapefile")
}
I added the line writeOGR(... so all subsets are written to your working direktory. However, you can change this line or add further analysis into the for-loop.
How it works
unique(shpFarmsum$ID) extracts all occuring IDs (compareable to your 1,2,3...n).
In each repetition of the for loop, another value of this IDs will be used to create a subset of the whole SpatialPolygonsDataFrame, which you can use for further analysis.
I am new to the world of spatial analysis using R. Using this link I have downloaded OSM data in .osm.pbf format. Then I used osm2pgsql tool to get data in PostgreSQL (PostGIS extension). Now I have several tables in my database and I want to access the polygons table in R and then perform spatial analysis on the polygon vector data. I have been searching around allot but am not able to import required data in R. I found this tutorial quite similar to what I am looking for but its in Python. I want to access polygon data from PostGIS using R.
Therefore, essentially I would like to know interaction of R with PostGIS. Can anybody recommend me any book on this topic? Since I couldn't find a blog or tutorial so far that works for me on my Windows 10 64-bit machine.
Thanks for your time and looking forward for the suggestions.
I have still not found a way to get required data form PostGIS using rgdal package available in R. Probably it is because of my OS issues. (I am not exactly sure as I am not an expert). But I have found an alternative to rgdal and it has done exactly what I wanted it to do. The code is as following:
library(RPostgreSQL)
library(rgeos)
library(sp)
# Load data from the PostGIS server
conn = dbConnect(
dbDriver("PostgreSQL"), dbname="dbNAME", host="localhost", port=5432,
user="username", password="pw"
)
strSQL = "SELECT osm_id, name, area, highway, railway, place, ST_AsText(way) AS wkt_geometry FROM table"
df = dbGetQuery(conn, strSQL)
#Geomtery column as R list
geo_col = df$wkt_geometry
polygon_list = suppressWarnings(lapply(geo_col, function(x){
x <- gsub("POLYGON\\(\\(", "", x)
x <- gsub("\\)", "", x)
x <- strsplit(x, ",")[[1]]
#Now each polygon has been parsed by removing POLYGON(( from the start and )) from the end
#Now for each POLYGON its xValues and yValues are to be extracted to for Polygon object
xy <- strsplit(x, " ")
v_xy = suppressWarnings(sapply(xy, function(p){
xValue = p[1]
yValue = p[2]
vec = c(xValue, yValue)
}))
#Now we have all x values in first column of v_xy and all y values in second column of v_xy
#Let us make the Polygon object now
p_xvalues = as.numeric(v_xy[1, ])
p_yvalues = as.numeric(v_xy[2, ])
p_object <- Polygon(cbind(p_xvalues, p_yvalues))
}))
#Now we have all of the polygons in polygon object format
#Let us join it with main data frame, i.e. df
df$object_polygon <- polygon_list
#View(df)
#Now Let us form SpatialPolygons() object out of it
Ps_list = list()
for (i in seq(nrow(df))) {
Ps_list[[i]] <- Polygons(polygon_list[i], ID=df[i,][1])
}
SPs = SpatialPolygons(Ps_list)
#Now FINALY its the time to form SpatialPolygonsDataFrame
row.names(df) = df$osm_id
SPDF = SpatialPolygonsDataFrame(Sr = SPs, data = df[, 1:6], match.ID = TRUE)
Therefore, essentially I had to write a parser to get the required data which readOGR() does it one line.
I dont know R, but very familiar with postgis. Postgis is just SQL functions, if you can use Select you can access postgis,
So if you want access polygon just create new function in postgres.
But if you want display polygons you need check with R what function are available, i usually use Openlayer javascript api to show my results in the webpage.
I am organizing weather data into netCDF files in R. Everything goes fine until I try to populate the netcdf variables with data, because it is asking me to specify only one dimension for two-dimensional variables.
library(ncdf)
These are the dimension tags for the variables. Each variable uses the Threshold dimension and one of the other two dimensions.
th <- dim.def.ncdf("Threshold", "level", c(5,6,7,8,9,10,50,75,100))
rt <- dim.def.ncdf("RainMinimum", "cm", c(5, 10, 25))
wt <- dim.def.ncdf("WindMinimum", "m/s", c(18, 30, 50))
The variables are created in a loop, and there are a lot of them, so for the sake of easy understanding, in my example I'll only populate the list of variables with one variable.
vars <- list()
v1 <- var.def.ncdf("ARMM_rain", "percent", list(th, rt), -1, prec="double")
vars[[length(vars)+1]] <- v1
ncdata <- create.ncdf("composite.nc", vars)
I use another loop to extract data from different data files into a 9x3 data frame named subframe while iterating through the variables of the netcdf file with varindex. For the sake of reproducing, I'll give a quick initialization for these values.
varindex <- 1
subframe <- data.frame(matrix(nrow=9, ncol=3, rep(.01, 27)))
The desired outcome from there is to populate each ncdf variable with the contents of subframe. The code to do so is:
for(x in 1:9) {
for(y in 1:3) {
value <- ifelse(is.na(subframe[x,y]), -1, subframe[x,y])
put.var.ncdf(ncdata, varindex, value, start=c(x,y), count=1)
}
}
The error message is:
Error in put.var.ncdf(ncdata, varindex, value, start = c(x, y), count = 1) :
'start' should specify 1 dims but actually specifies 2
tl;dr: I have defined two-dimensional variables using ncdf in R, I am trying to write data to them, but I am getting an error message because R believes they are single-dimensional variables instead.
Anyone know how to fix this error?