Edit 2023-06: use terra::vrt() or terra::mosaic()
Since gdalUtils seems unmaintained for a while now, you can use a similar approach as in my original answer just using the more up-to-date terra package which is the successor to the raster package:
library(terra)
vrt(
x = list.files(path = "folder/to/images", pattern = "*.tif$", full.names = TRUE),
filename = "dem.vrt"
)
# afterwards read it as if it was a normal raster:
dem <- rast("dem.vrt")
Another possibility is to use terra::mosaic(). This gives you more control over the aggregation function in overlapping pixels. For that you first create a raster collection using sprc() from a list of rast objects and then mosaic to combine them into one raster.
library(terra)
# vector of file names
fls <- list.files("your/folder", ".tif$", full.names = TRUE)
# list of rast objects
r_lst <- lapply(fls, rast)
# create spatial raster collection
coll <- sprc(r_list)
# combine all rasters
mosaic(coll, function = "mean")
Original answer using gdalUtils
You can make use of the powerful GDAL functions. From my experience these are much faster than pure R code.
My approach would be with library(gdalUtils):
First, build a virtual raster file (vrt):
library(gdalUtils)
setwd(...)
gdalbuildvrt(gdalfile = "*.tif", # uses all tiffs in the current folder
output.vrt = "dem.vrt")
Then, copy the virtual raster to a actual physical file:
gdal_translate(src_dataset = "dem.vrt",
dst_dataset = "dem.tif",
output_Raster = TRUE # returns the raster as Raster*Object
# if TRUE, you should consider to assign
# the whole function to an object like dem <- gddal_tr..
options = c("BIGTIFF=YES", "COMPRESSION=LZW"))
Another pure (and probably slower) raster package solution would be:
f <- list.files(path = "your/path", pattern = ".tif$", full.names = TRUE)
rl <- lapply(f, raster)
do.call(merge, c(rl, tolerance = 1))
you have to adjust the tolerance since the raster files will probably not have the same origin.