Installing fasterRaster

You probably already have fasterRaster installed on your computer, but if not, you can install the latest release version from CRAN using:

install.packages("fasterRaster")

and the latest development version using:

remotes::install_github("adamlilith/fasterRaster", dependencies = TRUE)

(You may need to install the remotes package first.)

Installing GRASS GIS

fasterRaster uses GRASS to do its operations. You will need to install GRASS using the “stand-alone” installer, available through the GRASS GIS. Be sure to use the “stand-alone” installer, not the “OSGeo4W” installer!

Optional: A few functions in fasterRaster require GRASS “addon” modules, which do not come bundled with GRASS. You do not need to install these addons if you do not use functions that call them. A list of functions that require addons can be seen in the “addons” vignette (in R, use vignette("addons", package = "fasterRaster")). This vignette also explains how to install addons.

Starting a fasterRaster session

I recommend attaching the data.table, terra, and sf packages before attaching fasterRaster package to avoid function conflicts. The data.table package is not required, but you most surely will use at least one of the other two.

library(terra)
#> terra 1.7.83
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(data.table)
#> 
#> Attaching package: 'data.table'
#> The following object is masked from 'package:terra':
#> 
#>     shift
library(fasterRaster)
#> fasterRaster 8.4.0.3
#> To avoid conflicts between functions, please attach the `terra`, `sf`,
#> and `data.table` packages before attaching `fasterRaster` using, for
#> example, `library(terra)`.
#> For guides and table of contents, see `?fasterRaster`.
#> 
#> Attaching package: 'fasterRaster'
#> The following object is masked from 'package:data.table':
#> 
#>     %notin%
#> The following object is masked from 'package:sf':
#> 
#>     st_coordinates
#> The following object is masked from 'package:stats':
#> 
#>     kernel
#> The following object is masked from 'package:graphics':
#> 
#>     grid
#> The following objects are masked from 'package:base':
#> 
#>     rbind, xor

To begin, you need to tell fasterRaster the full file path of the folder where GRASS is installed on your system. Where this is well depend on your operating system and the version of GRASS installed. Three examples below show you what this might look like, but you may need to change the file path to match your case:

grassDir <- "C:/Program Files/GRASS GIS 8.3" # Windows
grassDir <- "/Applications/GRASS-8.3.app/Contents/Resources" # Mac OS
grassDir <- "/usr/local/grass" # Linux

To tell fasterRaster where GRASS is installed, use the faster() function:

faster(grassDir = grassDir)

You can also use the faster() function to set options that affect how fasterRaster functions run. This includes setting the amount of maximum memory and number of computer cores allocated to operations.

Importantly, some fasterRaster functions require addons (GRASS modules that are optional and are not included with teh GRASS download). To see how to set up addons so functions that use them can operate, see `vignette(“addons”, package = “fasterRaster”).

Importing spatial objects into fasterRaster GRasters and GVectors

In fasterRaster, rasters are called GRasters and vectors are called GVectors. The easiest (but not always fastest) way to start using a GRaster or GVector is to convert it from one already in R. In the example below, we use a raster that comes with the fasterRaster package. The raster represents elevation of a portion of eastern Madagascar. We first load the SpatRaster using fastData(), a helper function for loading example data objects that come with the fasterRaster package.

madElev <- fastData("madElev") # example SpatRaster
madElev

class       : SpatRaster 
dimensions  : 1024, 626, 1  (nrow, ncol, nlyr)
resolution  : 59.85157, 59.85157  (x, y)
extent      : 731581.6, 769048.6, 1024437, 1085725  (xmin, xmax, ymin, ymax)
coord. ref. : Tananarive (Paris) / Laborde Grid 
source      : madElev.tif 
name        : madElev 
min value   :       1 
max value   :     570

Now, we do the conversion to a GRaster and a GVector using fast(). This function can create a GRaster or GVector from a SpatRaster or a file representing a raster.

elev <- fast(madElev)
elev

class       : GRaster
topology    : 2D 
dimensions  : 1024, 626, NA, 1 (nrow, ncol, ndepth, nlyr)
resolution  : 59.85157, 59.85157, NA (x, y, z)
extent      : 731581.552, 769048.635, 1024437.272, 1085725.279 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid 
name(s)     : madElev 
datatype    : integer 
min. value  :       1 
max. value  :     570

Converting rasters and vectors that are already in R to GRasters usually takes more time than loading them directly from disk. To load from disk, simply replace the first argument in fast() with a string representing the folder path and file name of the raster you want to load into the session. For example, you can do:

rastFile <- system.file("extdata", "madElev.tif", package = "fasterRaster")
elev2 <- fast(rastFile)

Now, let’s create a GVector. The fast() function can take a SpatVector from the terra package, an sf object from the sf package, or a string representing the file path and file name of a vector file (e.g., a GeoPackage file or a shapefile).

madRivers <- fastData("madRivers") # sf vector
madRivers

Simple feature collection with 11 features and 5 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 731627.1 ymin: 1024541 xmax: 762990.1 ymax: 1085580
Projected CRS: Tananarive (Paris) / Laborde Grid
First 10 features:
       F_CODE_DES          HYC_DESCRI      NAM ISO     NAME_0                       geometry
1180 River/Stream Perennial/Permanent MANANARA MDG Madagascar LINESTRING (739818.2 108005...
1185 River/Stream Perennial/Permanent MANANARA MDG Madagascar LINESTRING (739818.2 108005...
1197 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (747857.8 108558...
1216 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (739818.2 108005...
1248 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (762990.1 105737...
1256 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (742334.2 106858...
1257 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (731803.7 105391...
1264 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (755911.6 104957...
1300 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (731871 1044531,...
1312 River/Stream Perennial/Permanent      UNK MDG Madagascar LINESTRING (750186.1 103441...

rivers <- fast(madRivers)
rivers

class       : GVector
geometry    : 2D lines 
dimensions  : 11, 11, 5 (geometries, sub-geometries, columns)
extent      : 731627.0998, 762990.1321, 1024541.23477, 1085580.45359 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid 
names       :   F_CODE_DES      HYC_DESCRI      NAM   ISO     NAME_0 
type        :        <chr>           <chr>    <chr> <chr>      <chr> 
values      : River/Stream Perennial/Perm~ MANANARA   MDG Madagascar 
              River/Stream Perennial/Perm~ MANANARA   MDG Madagascar 
              River/Stream Perennial/Perm~      UNK   MDG Madagascar 
              (and 8 more rows) 

Operations on GRasters and GVectors

You can do operations on GRasters and GVectors as if they were SpatRasters, SpatVectors, and sf objects. For example, you plot them as if the were any other spatial object:

plot(elev)
plot(rivers, col = 'lightblue', add = TRUE)

Elevation and rivers

You can use mathematical operators and functions:

elev_feet <- elev * 3.28084
elev_feet

class       : GRaster
topology    : 2D 
dimensions  : 1024, 626, NA, 1 (nrow, ncol, ndepth, nlyr)
resolution  : 59.85157, 59.85157, NA (x, y, z)
extent      : 731581.552, 769048.635, 1024437.272, 1085725.279 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid 
name(s)     :    layer 
datatype    :   double 
min. value  :   3.2808 
max. value  : 1870.056

log10_elev <- log10(elev)
log10_elev

class       : GRaster
topology    : 2D 
dimensions  : 1024, 626, NA, 1 (nrow, ncol, ndepth, nlyr)
resolution  : 59.85157, 59.85157, NA (x, y, z)
extent      : 731581.552, 769048.635, 1024437.272, 1085725.279 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid 
name(s)     :              log 
datatype    :           double 
min. value  :                0 
max. value  : 2.75587485567249

You can also use the many fasterRaster functions. In general, these functions have the same names as their terra counterparts and often the same arguments. Note that even many terra and fasterRaster functions have the same name, they do not necessarily produce the exact same output. Much care has been taken to ensure they do, but sometimes there are multiple ways to do the same task, so choices made by the authors of terra and GRASS can lead to differences.

The following code 1) creates a raster where cell values reflect the distance between them and the nearest river; b) makes a buffer around the rivers; then c) plots the output:

dist <- distance(elev, rivers)
dist

class       : GRaster
topology    : 2D 
dimensions  : 1024, 626, NA, 1 (nrow, ncol, ndepth, nlyr)
resolution  : 59.85157, 59.85157, NA (x, y, z)
extent      : 731581.552, 769048.635, 1024437.272, 1085725.279 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid 
name(s)     :         distance 
datatype    :           double 
min. value  :                0 
max. value  : 21310.9411762729 

river_buff <- buffer(rivers, 2000)
river_buff

class       : GVector
geometry    : 2D polygons 
dimensions  : 1, 5, 0 (geometries, sub-geometries, columns)
extent      : 729629.19151, 764989.97343, 1022544.92079, 1087580.24979 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid 

plot(dist)
plot(rivers, col = 'lightblue', add = TRUE)
plot(river_buff, border = 'white', add = TRUE)

Distance between each cell and nearest major river

And that’s how you get started! Now that you have a raster and a vector in your fasterRaster “location”, you can start doing manipulations and analyses using any of the fasterRaster functions! To see an annotated list of these functions, use ?fasterRaster.

Converting and saving GRasters and GVectors

You can convert a GRaster to a SpatRaster raster using rast():

terra_elev <- rast(elev)

To convert a GVector to the terra package’s SpatVector format or to an sf vector, use vect() or st_as_sf():

terra_rivers <- vect(rivers)
sf_rivers <- st_as_sf(rivers)

Finally, you can use writeRaster() and writeVector() to save GRasters and GVectors directly to disk. This will always be faster than using rast(), vect(), or st_as_sf() then saving the result from those functions.

elev_temp_file <- tempfile(fileext = ".tif") # save as GeoTIFF
writeRaster(elev, elev_temp_file)

vect_temp_file <- tempfile(fileext = ".shp") # save as shapefile
writeVector(rivers, vect_temp_file)

Known issues

• Comparability between terra and fasterRaster: As much as possible, fasterRaster functions were written to recreate the output that functions in terra produce. However, owing to implementation choices made by the respective developers of terra and GRASS, outputs are not always the same.

• fasterRaster can crash when the temporary folder is cleaned: Some operating systems have automated procedures that clean out the system’s temporary folders when they get too large. This can remove files GRASS is using and fasterRaster is pointing to, rendering them broken. In Windows, this setting can be changed by going to Settings, then Storage, then Storage Sense. Turn off the setting “Keep Windows running smoothly by automatically cleaning up temporary system and app files”.

• Disk space fills up: As counter to the previous issue, prolonged use of fasterRaster by the same R process can create a lot of temporary files in the GRASS cache that fills your hard drive. fasterRaster does its best to remove these files when they are not needed. However, temporary files can still accumulate. For example, the operation new_raster <- 2 * old_raster^3 creates a raster file with the ^3 operation, which is then multiplied by 2 to get the desired output. The raster from the ^3 operation is still left in the disk cache, even though it does not have a “name” in R. Judicious use of the mow() function can remove these temporary files.

~ FINIS ~