Question: I've been trying to find a time-efficient way to merge multiple raster images in R. These are adjacent ASTER scenes from the southern Kilimanjaro region, and my target is to put them together to obtain one large image.

This is what I got so far (object 'ast14dmo' representing a list of RasterLayer objects):

```
# Loop through single ASTER scenes
for (i in seq(ast14dmo.sd)) {
  if (i == 1) {
    # Merge current with subsequent scene
    ast14dmo.sd.mrg <- merge(ast14dmo.sd[[i]], ast14dmo.sd[[i+1]], tolerance = 1)
  } else if (i > 1 && i < length(ast14dmo.sd)) {
    tmp.mrg <- merge(ast14dmo.sd[[i]], ast14dmo.sd[[i+1]], tolerance = 1)
    ast14dmo.sd.mrg <- merge(ast14dmo.sd.mrg, tmp.mrg, tolerance = 1)
  } else {
    # Save merged image
    writeRaster(ast14dmo.sd.mrg, paste(path.mrg, "/AST14DMO_sd_", z, "m_mrg", sep = ""), format = "GTiff", overwrite = TRUE)
  }
}

```

As you surely guess, the code works. However, merging takes quite long considering that each single raster object is some 70 mb large. I also tried Reduce and do.call, but that failed since I couldn't pass the argument 'tolerance' which circumvents the different origins of the raster files.

Anybody got an idea of how to speed things up?

Answer: 
SAGA GIS mosaicking tool (<http://www.saga-gis.org/saga_tool_doc/7.3.0/grid_tools_3.html>) gives you maximum flexibility for merging numeric layers, and it runs in parallel by default! You only have to translate all rasters/images to SAGA .sgrd format first, then run the command line saga\_cmd.
I was faced with this same problem and I used 

```
#Read desired files into R
data_name1<-'file_name1.tif' 

r1=raster(data_name1)

data_name2<-'file_name2.tif'

r2=raster(data_name2)

#Merge files
new_data <- raster::merge(r1, r2)

```

Although it did not produce a new merged raster file, it stored in the data environment and produced a merged map when plotted.