I've decided to process my Landsat data in R instead or ArcGIS - due to my missing knowledge of python and and because of (assumed) high computation capacities of R. I want to :

1. import r1 raster to R,
2. import shp1 convert raster r1 to shp r.to.poly (dissolve = TRUE)
3. intersect converter raster r.to.poly with my polygon shp1
4. calculate area of every created polygon of intersected shp

Thus:

# read shp
shp <-readOGR(dsn = "C://...",
    layer = "m")

#read raster
r1<-raster("r1.tif")

# convert raster to polygon, dissolved neighboring same values
r.to.poly<-rasterToPolygons(r1, dissolve = T)

# define the same projection 
proj4string(shp) <- proj4string(r.to.poly)  

# use intersection from raster package
int.r <-raster::intersect(r.to.poly,shp)  

# calculate area per polygon
int.r$area <-gArea(int.r, byid = T) 
  
# export shapefile
writeOGR(int.r, dsn = "C:/...",
          layer = "...", driver="ESRI Shapefile", overwrite = TRUE)

So far, so good, but I takes about an hour to run the single conversion  ! moreover, when I tried FOR loop, my R on Windows crashed twice... It runs on mac, for the moment. Where the problem could be and how can I increase computation speed? Am I running out of R memory? The raster size on my disk is only 779 580 byte, size of shp is 1 729 532 bytes, thus are small. Also, make the same task in ArcGIS takes only couple seconds.

I've found some related discussion here: Increasing speed of crop, mask, & extract raster by many polygons in R? but as I have only about 10 rasters to process I don't want to start with parallel processing...

I've decided to process my Landsat data in R instead of ArcGIS - due to my missing knowledge of python and because of (assumed) high computation capacities of R. I want to :

1. import r1 raster to R,
2. import shp1 convert raster r1 to shp r.to.poly (dissolve = TRUE)
3. intersect converter raster r.to.poly with my polygon shp1
4. calculate area of every created polygon of intersected shp

Thus:

# read shp
shp <-readOGR(dsn = "C://...",
    layer = "m")

#read raster
r1<-raster("r1.tif")

# convert raster to polygon, dissolved neighboring same values
r.to.poly<-rasterToPolygons(r1, dissolve = T)

# define the same projection 
proj4string(shp) <- proj4string(r.to.poly)  

# use intersection from raster package
int.r <-raster::intersect(r.to.poly,shp)  

# calculate area per polygon
int.r$area <-gArea(int.r, byid = T) 
  
# export shapefile
writeOGR(int.r, dsn = "C:/...",
          layer = "...", driver="ESRI Shapefile", overwrite = TRUE)

So far, so good, but it takes about an hour to run the single conversion! moreover, when I tried FOR loop, my R on Windows crashed twice... It runs on mac, for the moment. Where the problem could be and how can I increase computation speed? Am I running out of R memory? The raster size on my disk is only 779 580 byte, size of shp is 1 729 532 bytes, thus are small. Also, make the same task in ArcGIS takes only couple seconds.

I've found some related discussion here: Increasing speed of crop, mask, & extract raster by many polygons in R? but as I have only about 10 rasters to process I don't want to start with parallel processing...

I've decided to process my Landsat data in R instead or ArcGIS - due to my missing knowledge of python and and because of (assumed) high computation capacities of R. I want to :

1. import r1 raster to R,
2. import shp1 convert raster r1 to shp r.to.poly (dissolve = TRUE)
3. intersect converter raster r.to.poly with my polygon shp1
4. calculate area of every created polygon of intersected shp

Thus:

# read shp
shp <-readOGR(dsn = "C://...",
    layer = "m")

#read raster
r1<-raster("r1.tif")

# convert raster to polygon, dissolved neighboring same values
r.to.poly<-rasterToPolygons(r1, dissolve = T)

# define the same projection 
proj4string(shp) <- proj4string(r.to.poly)  

# use intersection from raster package
int.r <-raster::intersect(r.to.poly,shp)  

# calculate area per polygon
int.r$area <-gArea(int.r, byid = T) 
  
# export shapefile
writeOGR(int.r, dsn = "C:/...",
          layer = "...", driver="ESRI Shapefile", overwrite = TRUE)

So far, so good, but I takes about an hour to run the single conversion ! moreover, when I tried FOR loop, my R on Windows crashed twice... It runs on mac, for the moment. Where the problem could be and how can I increase computation speed? Am I running out of R memory? The raster size on my disk is only 779 580 byte, size of shp is 1 729 532 bytes, thus are small. Also, make the same task in ArcGIS takes only couple seconds.

I've found some related discussion here: Increasing speed of crop, mask, & extract raster by many polygons in R? but as I have only about 10 rasters to process I don't want to start with parallel processing...

I've decided to process my Landsat data in R instead or ArcGIS - due to my missing knowledge of python and and because of (assumed) high computation capacities of R. I want to :

1. import r1 raster to R,
2. import shp1 convert raster r1 to shp r.to.poly (dissolve = TRUE)
3. intersect converter raster r.to.poly with my polygon shp1
4. calculate area of every created polygon of intersected shp

Thus:

# read shp
shp <-readOGR(dsn = "C://...",
    layer = "m")

#read raster
r1<-raster("r1.tif")

# convert raster to polygon, dissolved neighboring same values
r.to.poly<-rasterToPolygons(r1, dissolve = T)

# define the same projection 
proj4string(shp) <- proj4string(r.to.poly)  

# use intersection from raster package
int.r <-raster::intersect(r.to.poly,shp)  

# calculate area per polygon
int.r$area <-gArea(int.r, byid = T) 
  
# export shapefile
writeOGR(int.r, dsn = "C:/...",
          layer = "...", driver="ESRI Shapefile", overwrite = TRUE)

So far, so good, but I takes about an hour to run the single conversion ! moreover, when I tried FOR loop, my R on Windows crashed twice... It runs on mac, for the moment. Where the problem could be and how can I increase computation speed? Am I running out of R memory? The raster size on my disk is only 779 580 byte, size of shp is 1 729 532 bytes, thus are small. Also, make the same task in ArcGIS takes only couple seconds.

I've found some related discussion here: Increasing speed of crop, mask, & extract raster by many polygons in R? but as I have only about 10 rasters to process I don't want to start with parallel processing...