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: http://gis.stackexchange.com/questions/130522/r-increase-speed-of-crop-mask-extract-raster-by-many-polygons but as I have only about 10 rasters to process I don't want to start with parallel processing...