How to group repeating sequences of numbers using R - r

Question

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)
}

Answer 1

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), ]

Answer 2

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.