# Raster data manipulation¶

## Introduction¶

In this chapter general aspects of the design of the `terra`

package
are discussed, notably the structure of the main classes, and what they
represent. The use of the package is illustrated in subsequent sections.
`terra`

has a large number of functions, not all of them are discussed
here, and those that are discussed are mentioned only briefly. See the
help files of the package for more information on individual functions
and `help("terra-package")`

for an index of functions by topic.

## Creating SpatRaster objects¶

A `SpatRaster`

can easily be created from scratch using the function
`rast`

. The default settings will create a global raster data
structure with a longitude/latitude coordinate reference system and 1 by
1 degree cells. You can change these settings by providing additional
arguments such as `xmin`

, `nrow`

, `ncol`

, and/or `crs`

, to the
function. You can also change these parameters after creating the
object. If you set the projection, this is only to properly define it,
not to change it. To transform a `SpatRaster`

to another coordinate
reference system (projection) you can use the function 1projectRaster1.

Here is an example of creating and changing a `SpatRaster`

object ‘r’
from scratch.

```
library(terra)
# SpatRaster with the default parameters
x <- rast()
x
## class : SpatRaster
## dimensions : 180, 360, 1 (nrow, ncol, nlyr)
## resolution : 1, 1 (x, y)
## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs
```

With some other parameters

```
x <- rast(ncol=36, nrow=18, xmin=-1000, xmax=1000, ymin=-100, ymax=900)
```

These parameters can be changed. Resolution:

```
res(x)
## [1] 55.55556 55.55556
res(x) <- 100
res(x)
## [1] 100 100
```

Change the number of columns (this affects the resolution).

```
ncol(x)
## [1] 20
ncol(x) <- 18
ncol(x)
## [1] 18
res(x)
## [1] 111.1111 100.0000
```

Set the coordinate reference system (CRS) (i.e., define the projection).

```
crs(x) <- "+proj=utm +zone=48 +datum=WGS84"
x
## class : SpatRaster
## dimensions : 10, 18, 1 (nrow, ncol, nlyr)
## resolution : 111.1111, 100 (x, y)
## extent : -1000, 1000, -100, 900 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=utm +zone=48 +datum=WGS84 +units=m +no_defs
```

The objects `x`

created in the examples above only consist of the
raster *geometry*, that is, we have defined the number of rows and
columns, and where the raster is located in geographic space, but there
are no cell-values associated with it. Setting and accessing values is
illustrated below.

First another example empty raster geometry.

```
r <- rast(ncol=10, nrow=10)
ncell(r)
## [1] 100
hasValues(r)
## [1] FALSE
```

Use the ‘values’ function.

```
values(r) <- 1:ncell(r)
```

Another example.

```
set.seed(0)
values(r) <- runif(ncell(r))
hasValues(r)
## [1] TRUE
sources(r)
## source nlyr
## 1 1
values(r)[1:10]
## [1] 0.8966972 0.2655087 0.3721239 0.5728534 0.9082078 0.2016819 0.8983897
## [8] 0.9446753 0.6607978 0.6291140
plot(r, main='Raster with 100 cells')
```

In some cases, for example when you change the number of columns or
rows, you will lose the values associated with the `SpatRaster`

if
there were any (or the link to a file if there was one). The same
applies, in most cases, if you change the resolution directly (as this
can affect the number of rows or columns). Values are not lost when
changing the extent as this change adjusts the resolution, but does not
change the number of rows or columns.

```
hasValues(r)
## [1] TRUE
res(r)
## [1] 36 18
dim(r)
## [1] 10 10 1
# extent
ext(r)
## SpatExtent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
```

Now change the maximum x coordinate of the extent (bounding box) of the SpatRaster.

```
xmax(r) <- 0
hasValues(r)
## [1] TRUE
res(r)
## [1] 18 18
dim(r)
## [1] 10 10 1
```

And the number of columns (the values disappear)

```
ncol(r) <- 6
hasValues(r)
## [1] FALSE
res(r)
## [1] 30 18
dim(r)
## [1] 10 6 1
xmax(r)
## [1] 0
```

While we can create a `SpatRaster`

from scratch, it is more common to
do so from a file. The `terra`

package can use raster files in several
formats, including GeoTiff, ESRI, ENVI, and ERDAS.

A notable feature of the `terra`

package is that it can work with
raster datasets that are stored on disk and are too large to be loaded
into memory (RAM). The package can work with large files because the
objects it creates from these files only contain information about the
structure of the data, such as the number of rows and columns, the
spatial extent, and the filename, but it does not attempt to read all
the cell values in memory. In computations with these objects, data is
processed in chunks. If no output filename is specified to a function,
and the output raster is too large to keep in memory, the results are
written to a temporary file.

For this example, we first we get the name of an example file installed
with the package. Do **not** use this `system.file`

construction of
your own files (just type the file name; don’t forget the forward
slashes).

```
filename <- system.file("ex/test.tif", package="terra")
filename
## [1] "C:/soft/R/R-4.0.3/library/terra/ex/test.tif"
```

```
r <- rast(filename)
sources(r)
## source nlyr
## 1 C:/soft/R/R-4.0.3/library/terra/ex/test.tif 1
hasValues(r)
## [1] TRUE
plot(r, main='SpatRaster from file')
```

Multi-layer objects can be created in memory or from files.

Create three identical SpatRaster objects

```
r1 <- r2 <- r3 <- rast(nrow=10, ncol=10)
# Assign random cell values
values(r1) <- runif(ncell(r1))
values(r2) <- runif(ncell(r2))
values(r3) <- runif(ncell(r3))
```

Combine three SpatRasters:

```
s <- c(r1, r2, r3)
s
## class : SpatRaster
## dimensions : 10, 10, 3 (nrow, ncol, nlyr)
## resolution : 36, 18 (x, y)
## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs
## sources : memory
## memory
## memory
## names : lyr.1, lyr.1, lyr.1
## min values : 0.01307758, 0.02778712, 0.06380247
## max values : 0.9926841, 0.9815635, 0.9960774
nlyr(s)
## [1] 3
```

You can also create a multilayer object from a file.

```
filename <- system.file("ex/logo.tif", package="terra")
filename
## [1] "C:/soft/R/R-4.0.3/library/terra/ex/logo.tif"
b <- rast(filename)
b
## class : SpatRaster
## dimensions : 77, 101, 3 (nrow, ncol, nlyr)
## resolution : 1, 1 (x, y)
## extent : 0, 101, 0, 77 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
## source : logo.tif
## names : red, green, blue
## min values : 0, 0, 0
## max values : 255, 255, 255
nlyr(b)
## [1] 3
```

Extract a single layer (the second one on this case)

```
r <- b[[2]]
```

## Raster algebra¶

Many generic functions that allow for simple and elegant raster algebra
have been implemented for `Raster`

objects, including the normal
algebraic operators such as `+`

, `-`

, `*`

, `/`

, logical
operators such as `>`

, `>=`

, `<`

, `==`

, `!`

and functions like
`abs`

, `round`

, `ceiling`

, `floor`

, `trunc`

, `sqrt`

,
`log`

, `log10`

, `exp`

, `cos`

, `sin`

, `atan`

, `tan`

,
`max`

, `min`

, `range`

, `prod`

, `sum`

, `any`

, `all`

. In
these functions you can mix `raster`

objects with numbers, as long as
the first argument is a `raster`

object.

Create an empty SpatRaster and assign values to cells.

```
r <- rast(ncol=10, nrow=10)
values(r) <- 1:ncell(r)
```

Now some raster algebra.

```
s <- r + 10
s <- sqrt(s)
s <- s * r + 5
values(r) <- runif(ncell(r))
r <- round(r)
r <- r == 1
```

You can also use replacement functions.

```
#Not yet implemented
s[r] <- -0.5
s[!r] <- 5
s[s == 5] <- 15
```

If you use multiple `SpatRaster`

objects (in functions where this is
relevant, such as range), these must have the same resolution and
origin. The origin of a `Raster`

object is the point closest to (0, 0)
that you could get if you moved from a corner of a `SpatRaster`

toward
that point in steps of the `x`

and `y`

resolution. Normally these
objects would also have the same extent, but if they do not, the
returned object covers the spatial intersection of the objects used.

When you use multiple multi-layer objects with different numbers or layers, the ‘shorter’ objects are ‘recycled’. For example, if you multiply a 4-layer object (a1, a2, a3, a4) with a 2-layer object (b1, b2), the result is a four-layer object (a1b1, a2b2, a3b1, a3b2).

```
r <- rast(ncol=5, nrow=5)
values(r) <- 1
s <- c(r, r+1)
q <- c(r, r+2, r+4, r+6)
x <- r + s + q
x
## class : SpatRaster
## dimensions : 5, 5, 4 (nrow, ncol, nlyr)
## resolution : 72, 36 (x, y)
## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## names : lyr1, lyr2, lyr3, lyr4
## min values : 3, 6, 7, 10
## max values : 3, 6, 7, 10
```

Summary functions (min, max, mean, prod, sum, Median, cv, range, any,
all) always return a `SpatRaster`

object. Perhaps this is not obvious
when using functions like `min`

, `sum`

or `mean`

.

```
a <- mean(r,s,10)
b <- sum(r,s)
st <- c(r, s, a, b)
sst <- sum(st)
sst
## class : SpatRaster
## dimensions : 5, 5, 1 (nrow, ncol, nlyr)
## resolution : 72, 36 (x, y)
## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## name : sum
## min value : 14.5
## max value : 14.5
```

Use `global`

if you want a single number summarizing the cell values
of each layer.

```
global(st, 'sum')
## sum
## lyr.1 25.0
## lyr.1.1 25.0
## lyr.1.2 50.0
## lyr.1.3 137.5
## lyr1 50.0
## lyr2 75.0
global(sst, 'sum')
## sum
## sum 362.5
```

## ‘High-level’ functions¶

Several ‘high level’ functions have been implemented for `SpatRaster`

objects. ‘High level’ functions refer to functions that you would
normally find in a computer program that supports the analysis of raster
data. Here we briefly discuss some of these functions. All these
functions work for raster datasets that cannot be loaded into memory.
See the help files for more detailed descriptions of each function.

The high-level functions have some arguments in common. The first
argument is typically a `SpatRaster`

‘x’ or ‘object’. It is followed
by one or more arguments specific to the function (either additional
`SpatRaster`

objects or other arguments), followed by a filename=””
and “…” arguments.

The default filename is an empty character “”. If you do not specify a
filename, the default action for the function is to return a `raster`

object that only exists in memory. However, if the function deems that
the `raster`

object to be created would be too large to hold memory it
is written to a temporary file instead.

The “…” argument allows for setting additional arguments that are relevant when writing values to a file: the file format, datatype (e.g. integer or real values), and a to indicate whether existing files should be overwritten.

### Modifying a SpatRaster object¶

There are several functions that deal with modifying the spatial extent
of `SpatRaster`

objects. The `crop`

function lets you take a
geographic subset of a larger `raster`

object. You can crop a
`SpatRaster`

by providing an extent object or another spatial object
from which an extent can be extracted (objects from classes deriving
from Raster and from Spatial in the sp package). An easy way to get an
extent object is to plot a `SpatRaster`

and then use `drawExtent`

to
visually determine the new extent (bounding box) to provide to the crop
function.

`trim`

crops a `SpatRaster`

by removing the outer rows and columns
that only contain `NA`

values. In contrast, `extend`

adds new rows
and/or columns with `NA`

values. The purpose of this could be to
create a new `SpatRaster`

with the same Extent of another, larger,
`SpatRaster`

such that they can be used together in other functions.

The `merge`

function lets you merge 2 or more `Raster`

objects into
a single new object. The input objects must have the same resolution and
origin (such that their cells neatly fit into a single larger raster).
If this is not the case you can first adjust one of the `Raster`

objects with use `(dis)aggregate`

or `resample`

.

`aggregate`

and `disaggregate`

allow for changing the resolution
(cell size) of a `SpatRaster`

object. In the case of `aggregate`

,
you need to specify a function determining what to do with the grouped
cell values `mean`

. It is possible to specify different
(dis)aggregation factors in the x and y direction. `aggregate`

and
`disaggregate`

are the best functions when adjusting cells size only,
with an integer step (e.g. each side 2 times smaller or larger), but in
some cases that is not possible.

For example, you may need nearly the same cell size, while shifting the
cell centers. In those cases, the `resample`

function can be used. It
can do either nearest neighbor assignments (for categorical data) or
bilinear interpolation (for numerical data). Simple linear shifts of a
Raster object can be accomplished with the `shift`

function or with
the `extent`

function.

With the `warp`

function you can transform values of `SpatRaster`

object to a new object with a different coordinate reference system.

Here are some simple examples.

Aggregate and disaggregate.

```
r <- rast()
values(r) <- 1:ncell(r)
ra <- aggregate(r, 20)
rd <- disaggregate(ra, 20)
```

Crop and merge example.

```
r1 <- crop(r, ext(-50,0,0,30))
r2 <- crop(r, ext(-10,50,-20, 10))
m <- merge(r1, r2, filename='test.tif', overwrite=TRUE)
plot(m)
```

`flip`

lets you flip the data (reverse order) in horizontal or
vertical direction – typically to correct for a ‘communication problem’
between different R packages or a misinterpreted file. `rotate`

lets
you rotate longitude/latitude rasters that have longitudes from 0 to 360
degrees (often used by climatologists) to the standard -180 to 180
degrees system. With `t`

you can rotate a `SpatRaster`

object 90
degrees.

### Overlay¶

`app`

allows you to do a computation for a single `SpatRaster`

object by providing a function. For exaple, `sum`

The `lapp`

(layer-apply) function can be used as an alternative to the
raster algebra discussed above.

### Reclassify¶

You can use `classify`

to replace ranges of values with single values,
or to substitute (replace) single values with other values.

```
r <- rast(ncol=3, nrow=2)
values(r) <- 1:ncell(r)
values(r)
## lyr.1
## [1,] 1
## [2,] 2
## [3,] 3
## [4,] 4
## [5,] 5
## [6,] 6
```

Set all values above 4 to `NA`

```
s <- app(r, fun=function(x){ x[x < 4] <- NA; return(x)} )
as.matrix(s)
## lyr.1
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] 4
## [5,] 5
## [6,] 6
```

Divide the first raster with two times the square root of the second raster and add five.

```
rs <- c(r, s)
w <- lapp(rs, fun=function(x, y){ x / (2 * sqrt(y)) + 5 } )
as.matrix(w)
## lyr1
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] 6.000000
## [5,] 6.118034
## [6,] 6.224745
```

Remove from `r`

all values that are `NA`

in `w`

.

```
u <- mask(r, w)
as.matrix(u)
## lyr.1
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] 4
## [5,] 5
## [6,] 6
```

Identify the cell values in `u`

that are the same as in `s`

.

```
v <- u==s
as.matrix(v)
## lyr.1
## [1,] NaN
## [2,] NaN
## [3,] NaN
## [4,] 1
## [5,] 1
## [6,] 1
```

Replace `NA`

values in `w`

with values of `r`

.

```
cvr <- cover(w, r)
as.matrix(w)
## lyr1
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] 6.000000
## [5,] 6.118034
## [6,] 6.224745
```

Change value between 0 and 2 to 1, etc.

```
x <- classify(w, rbind(c(0,2,1), c(2,5,2), c(4,10,3)))
as.matrix(x)
## lyr1
## [1,] NaN
## [2,] NaN
## [3,] NaN
## [4,] 3
## [5,] 3
## [6,] 3
```

Substitute 2 with 40 and 3 with 50.

```
y <- classify(x, cbind(id=c(2,3), v=c(40,50)))
as.matrix(y)
## lyr1
## [1,] NaN
## [2,] NaN
## [3,] NaN
## [4,] 50
## [5,] 50
## [6,] 50
```

### Focal functions¶

The `focal`

function currently only work for (single layer) SpatRaster
objects. They make a computation using values in a neighborhood of cells
around a focal cell, and putting the result in the focal cell of the
output SpatRaster. The neighborhood is a user-defined matrix of weights
and could approximate any shape by giving some cells zero weight. It is
possible to only computes new values for cells that are `NA`

in the
input SpatRaster.

### Distance¶

There are a number of distance related functions. `distance`

computes
the shortest distance to cells that are not `NA`

. `pointDistance`

computes the shortest distance to any point in a set of points.
`gridDistance`

computes the distance when following grid cells that
can be traversed (e.g. excluding water bodies). `direction`

computes
the direction toward (or from) the nearest cell that is not `NA`

.
`adjacency`

determines which cells are adjacent to other cells. See
the `gdistance`

package for more advanced distance calculations (cost
distance, resistance distance)

### Spatial configuration¶

`patches`

identifies groups of cells that are connected.
`boundaries`

identifies edges, that is, transitions between cell
values. `area`

computes the size of each grid cell (for unprojected
rasters), this may be useful to, e.g. compute the area covered by a
certain class on a longitude/latitude raster.

```
r <- rast(nrow=45, ncol=90)
values(r) <- round(runif(ncell(r))*3)
a <- area(r, sum=FALSE)
zonal(a, r, "sum")
## zone lyr.1
## 1 0 9.391452e+13
## 2 1 1.694339e+14
## 3 2 1.586069e+14
## 4 3 8.811029e+13
```

### Predictions¶

The `terra`

package has two functions to make model predictions to
(potentially very large) rasters. `predict`

takes a multilayer raster
and a fitted model as arguments. Fitted models can be of various
classes, including glm, gam, and RandomForest. The function
`interpolate`

is similar but is for models that use coordinates as
predictor variables, for example in Kriging and spline interpolation.

### Vector to raster conversion¶

The terra package supports point, line, and polygon to raster conversion
with the `rasterize`

function. For vector type data (points, lines,
polygons), SpatVector objects are used; but points can also be
represented by a two-column matrix (x and y).

Point to raster conversion is often done with the purpose to analyze the
point data. For example to count the number of distinct species
(represented by point observations) that occur in each raster cell.
`rasterize`

takes a `SpatRaster`

object to set the spatial extent
and resolution, and a function to determine how to summarize the points
(or an attribute of each point) by cell.

Polygon to raster conversion is typically done to create a
`SpatRaster`

that can act as a mask, i.e. to set to `NA`

a set of
cells of a `SpatRaster`

object, or to summarize values on a raster by
zone. For example a country polygon is transferred to a raster that is
then used to set all the cells outside that country to `NA`

; whereas
polygons representing administrative regions such as states can be
transferred to a raster to summarize raster values by region.

It is also possible to convert the values of a `SpatRaster`

to points
or polygons, using `as.points`

and `as.polygons`

. Both functions
only return values for cells that are not `NA`

. Unlike
`rasterToPolygons`

, `rasterToPoints`

is reasonably efficient and
allows you to provide a function to subset the output before it is
produced (which can be necessary for very large rasters as the point
object is created in memory).

## Summarizing functions¶

When used with a `SpatRaster`

object as first argument, normal summary
statistics functions such as min, max and mean return a SpatRaster. You
can use cellStats if, instead, you want to obtain a summary for all
cells of a single `SpatRaster`

object. You can use `freq`

to make a
frequency table, or to count the number of cells with a specified value.
Use `zonal`

to summarize a `SpatRaster`

object using zones (areas
with the same integer number) defined in a `SpatRaster`

and
`crosstab`

to cross-tabulate two `SpatRaster`

objects.

```
r <- rast(ncol=36, nrow=18)
values(r) <- runif(ncell(r))
global(r, mean)
## mean
## lyr.1 0.5179682
```

Zonal stats

```
s <- r
values(s) <- round(runif(ncell(r)) * 5)
zonal(r, s, 'mean')
## zone lyr.1
## 1 0 0.5144431
## 2 1 0.5480089
## 3 2 0.5249257
## 4 3 0.5194031
## 5 4 0.4853966
## 6 5 0.5218401
```

Count cells

```
freq(s)
## layer value count
## [1,] 1 0 54
## [2,] 1 1 102
## [3,] 1 2 139
## [4,] 1 3 148
## [5,] 1 4 133
## [6,] 1 5 72
freq(s, value=3)
## layer value count
## [1,] 1 3 148
```

Cross-tabulate

```
#ctb <- crosstab(r*3, s)
#head(ctb)
```

## Helper functions¶

The cell number is an important concept in the terra package. Raster
data can be thought of as a matrix, but in a `SpatRaster`

it is more
commonly treated as a vector. Cells are numbered from the upper left
cell to the upper right cell and then continuing on the left side of the
next row, and so on until the last cell at the lower-right side of the
raster. There are several helper functions to determine the column or
row number from a cell and vice versa, and to determine the cell number
for x, y coordinates and vice versa.

```
library(terra)
r <- rast(ncol=36, nrow=18)
ncol(r)
## [1] 36
nrow(r)
## [1] 18
ncell(r)
## [1] 648
rowFromCell(r, 100)
## [1] 3
colFromCell(r, 100)
## [1] 28
cellFromRowCol(r,5,5)
## [1] 149
xyFromCell(r, 100)
## x y
## [1,] 95 65
cellFromXY(r, cbind(0,0))
## [1] 343
colFromX(r, 0)
## [1] 19
rowFromY(r, 0)
## [1] 10
```

## Accessing cell values¶

Cell values can be accessed with several methods. Use `getValues`

to
get all values or a single row; and `getValuesBlock`

to read a block
(rectangle) of cell values.

```
r <- rast(system.file("ex/test.tif", package="terra"))
#v <- values(r, 50)
#v[35:39]
#getValuesBlock(r, 50, 1, 35, 5)
```

You can also read values using cell numbers or coordinates (xy) using
the `extract`

method.

```
cells <- cellFromRowCol(r, 50, 35:39)
cells
## [1] 3955 3956 3957 3958 3959
r[cells]
## test
## 1 743
## 2 706
## 3 646
## 4 686
## 5 758
xy <- xyFromCell(r, cells)
xy
## x y
## [1,] 179780 332020
## [2,] 179820 332020
## [3,] 179860 332020
## [4,] 179900 332020
## [5,] 179940 332020
extract(r, xy)
## test
## 1 743
## 2 706
## 3 646
## 4 686
## 5 758
```

You can also extract values using `SpatVector`

objects. The default
approach for extracting raster values with polygons is that a polygon
has to cover the center of a cell, for the cell to be included. However,
you can use argument “weights=TRUE” in which case you get, apart from
the cell values, the percentage of each cell that is covered by the
polygon, so that you can apply, e.g., a “50% area covered” threshold, or
compute an area-weighted average.

In the case of lines, any cell that is crossed by a line is included. For lines and points, a cell that is only ‘touched’ is included when it is below or to the right (or both) of the line segment/point (except for the bottom row and right-most column).

In addition, you can use standard *R* indexing to access values, or to
replace values (assign new values to cells) in a `SpatRaster`

object.
If you replace a value in a `SpatRaster`

object based on a file, the
connection to that file is lost (because it now is different from that
file). Setting raster values for very large files will be very slow with
this approach as each time a new (temporary) file, with all the values,
is written to disk. If you want to overwrite values in an existing file,
you can use `update`

(with caution!)

```
r[cells]
## test
## 1 743
## 2 706
## 3 646
## 4 686
## 5 758
r[1:4]
## test
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
sources(r)
## source nlyr
## 1 C:/soft/R/R-4.0.3/library/terra/ex/test.tif 1
#r[2:3] <- 10
r[1:4]
## test
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
sources(r)
## source nlyr
## 1 C:/soft/R/R-4.0.3/library/terra/ex/test.tif 1
```

Note that in the above examples values are retrieved using cell numbers.
That is, a raster is represented as a (one-dimensional) vector. Values
can also be inspected using a (two-dimensional) matrix notation. As for
*R* matrices, the first index represents the row number, the second the
column number.

```
r[1]
## test
## 1 NaN
r[2,2]
## test
## 1 NaN
r[1, 1:5]
## test
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
## 5 NaN
r[1:5, 2]
## test
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
## 5 NaN
r[1:3,1:3]
## test
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
## 5 NaN
## 6 NaN
## 7 NaN
## 8 NaN
## 9 NaN
# keep the matrix structure
r[1:3,1:3, drop=FALSE]
## test
## 1 NaN
## 2 NaN
## 3 NaN
## 4 NaN
## 5 NaN
## 6 NaN
## 7 NaN
## 8 NaN
## 9 NaN
```

Accessing values through this type of indexing should be avoided inside
functions as it is less efficient than accessing values via functions
like `getValues`

.

## Coercion to other classes¶

You can convert `SpatRaster`

objects to `Raster*`

objects defined in
the `raster`

package.

```
r <- rast(ncol=36, nrow=18)
values(r) <- runif(ncell(r))
library(raster)
x <- raster(r)
```