Extract values from raster and append to existing dataframe - r

Question

I am trying to extract values from a rasterstack and append those to an existing dataframe. The values are a collection of metrics (PatchStat from r package SDMtools) which I am able to extract into list format, but I am stuck trying to bind the values to my existing dataframe.
Input data:
library(sp)
library(sf)
library(raster)
library(dplyr)
library(SDMTools)
mydata <- read.table(header=TRUE, text = "
animal X Y ord.year
1 pb_20414 157978.9 2323819 2009168
2 pb_20414 156476.3 2325586 2009168
3 pb_06817 188512.0 2299679 2006263
4 pb_06817 207270.9 2287248 2006264")
# add rasters
s <- stack(system.file("external/rlogo.grd", package="raster"))
names(s) <- c('masie_ice_r00_v01_2009168_4km', 'masie_ice_r00_v01_2006263_4km', 'masie_ice_r00_v01_2006264_4km')
# Create sp object
projection <-CRS('+proj=stere +lat_0=90 +lat_ts=60 +lon_0=-80 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +units=m + datum=WGS84 +no_defs +towgs84=0,0,0') # matches MASIE raster
coords <- cbind(mydata$X, mydata$Y)
mydata.sp <- SpatialPointsDataFrame(coords = coords, data = mydata, proj4string = projection)
# Create sf object
mydata.sf <- st_as_sf(mydata)
mydata.buf30 <- st_buffer(mydata.sf, 30000)
My goal is to match each GPS point (X,Y) with the correct GeoTIFF by date (mydata$ord.year), crop the raster to a (spatially explicit) 30 km buffer, run PatchStat in program SDMtools for R, and append the results to the original dataframe. The catch is that PatchStat results are provided in a dataframe, so I am having trouble matching those results to my existing dataframe.
Here is an example of results provided when I run PatchStat:
patchID n.cell n.core.cell n.edges.perimeter n.edges.internal area core.area perimeter
2 3 73 13 86 206 73 13 86
perim.area.ratio shape.index frac.dim.index core.area.index
2 1.178082 2.388889 1.430175 0.1780822
Here is what I have been able to do so far:
# separate date component of TIF name to correspond to mydata$ord.year
stack <- list()
date<-vector()
for (i in 1:length(rasterlist)) {
stack[[i]]<-raster(rasterlist[i])
tt<-unlist(strsplit(names(stack[[i]]), "[_]"))
date[i]<-tt[which(nchar(tt)==max(nchar(tt)))]
}
st <- stack(stack) # Create rasterstack object
# crop raster to buffer
mydata.sp <- as(mydata.sf, 'Spatial') # back to sp object
# pull raster data from GeoTIFF that corresponds to ordinal date
pat <- list()
for (i in 1:nrow(mydata.sp)) {
st2<-st[[which(date==mydata.sp$ord.year[i])]]
GeoCrop <- raster::crop(st2, mydata.sp[i,])
GeoCrop_mask <- raster::mask(GeoCrop, mydata.sp[i,])
pat[[i]] <- PatchStat(GeoCrop_mask)}
Additionally, I have eliminated one of the two land cover types so that each element in the list has only one row:
pat2 <- lapply(pat, `[`, -1,) # remove first row in each list element so only one row remains (using program plyr for R)
Now, I would like to match these rows to my original dataframe, so that pat2[[1]] is appended to mydata.sp[1,] like this (assuming a,b, and c are columns of metadata within my original SpatialPointsDataFrame). I would like all the columns of data from PatchStat added but to save time and space, I only included the first three here:
a b c PatchID n.cell n.core.cell
1 2 3 3 73 13
Note: If possible, I would love for this whole process to be included in the for loop to minimize room for error and also processing time.
Thanks so much!

Answer 1

Thanks for your effort to provide example data. But it is still incomplete (it refers to files that we do not have. You could to this
library(raster)
library(SDMTools)
s <- stack(system.file("external/rlogo.grd", package="raster"))
s <- round(s / 50) # to have fewer patches
names(s) <- c('masie_ice_r00_v01_2009168_4km', 'masie_ice_r00_v01_2006263_4km', 'masie_ice_r00_v01_2006264_4km')
df <- data.frame(ord.year=c("2009168", "2009168", "2006263", "2006264"))
pts <- SpatialPoints(cbind(c(20,40,60,80), c(20,40,60,20)))
crs(pts) <- crs(s)
pts <- SpatialPointsDataFrame(pts, df)
Make a buffer
b <- buffer(pts, 15, dissolve=FALSE)
Get matching names
nms <- names(s)
nms <- gsub('masie_ice_r00_v01_', '', nms)
nms <- gsub('_4km', '', nms)
Loop to match names, and put results in a list
p <- list()
for (i in 1:length(b)) {
j <- which(b$ord.year[i] == nms)
r <- s[[j]]
z <- crop(r, b[i,])
z <- mask(z, b[i,])
p[[i]] <- PatchStat(z)
}
Note that each element of p has a data.frame with multiple rows and columns.
p[[1]]
#patchID n.cell n.core.cell n.edges.perimeter n.edges.internal area core.area perimeter perim.area.ratio shape.index frac.dim.index core.area.index
#1 1 53 5 68 144 53 5 68 1.2830189 2.266667 1.427207 0.09433962
#2 2 123 8 182 310 123 8 182 1.4796748 3.956522 1.586686 0.06504065
#3 3 149 31 190 406 149 31 190 1.2751678 3.800000 1.543074 0.20805369
#4 4 54 2 114 102 54 2 114 2.1111111 3.800000 1.679578 0.03703704
#5 5 337 206 146 1202 337 206 146 0.4332344 1.972973 1.236172 0.61127596
If you only want the first rows
pp <- t(sapply(p, function(i) i[1,]))
Combining this with the orginal data.frame is now trivial
dfpp <- cbind(df, pp)

Answer 2

Well I did this very ugly thing and got what I wanted. But I don't like it. If anyone has a better idea I'd love to hear it!
# Change objects to df
pat2 <- lapply(pat, `[`, -1,) # remove first row in each list element
library(plyr) # ldply command
pat3 <- ldply (pat2, data.frame)
pat4 <- bind_cols(pb, pat3)