Merge SpatRasters, or merge a SpatVector with a data.frame

Merge multiple SpatRasters to create a new SpatRaster with a larger spatial extent. The SpatRasters should all have the same coordinate reference system. They should normally also have the same spatial origin and resolution, but automatic resampling can be done depending on the algorithm used (see argument algo). In areas where the SpatRasters overlap, the values of the SpatRaster that is first in the sequence of arguments (or in the SpatRasterCollection) will be retained (unless first=FALSE).

There is also a method for merging SpatVector with a data.frame; that is, to join the data.frame to the attribute table of the SpatVector.

See classify to merge a SpatRaster with a data.frame.

Usage

# S4 method for class 'SpatRaster,SpatRaster'
merge(x, y, ..., first=TRUE, na.rm=TRUE, algo=1, resample=FALSE, method="", 
      filename="", overwrite=FALSE, wopt=list())

# S4 method for class 'SpatRasterCollection,missing'
merge(x, first=TRUE, na.rm=TRUE, algo=1, resample=FALSE, method="", filename="", ...)

# S4 method for class 'SpatVector,data.frame'
merge(x, y, ...)

Arguments

x

SpatRaster, SpatRasterCollection, or SpatVector

y

missing if x is a SpatRasterCollection. SpatRaster if x is a SpatRaster. data.frame if x is a SpatVector

...

if x is a SpatRaster: additional objects of the same class as x. If x is a SpatRasterCollection: options for writing files as in writeRaster. If x is a SpatVector, the same arguments as in merge

first

logical. If TRUE, in areas where rasters overlap, the first value is used. Otherwise the last value is used

na.rm

logical. If TRUE missing values are are ignored. This is only used for algo 1; the other two always ignore missing values

algo

integer. You can use 1, 2 or 3 to pick a merge algorithm. algo 1 is generally faster than algo 2, but it may have poorer file compression. Algo 3 creates a virtual raster (see vrt). This is very quick and can be a good approach if the merge raster is used as input to a next step in the analysis. It allows any amount of misalignment and resamples without giving a warning. Otherwise its speed is similar to that of algo 2

resample

logical. If TRUE input rasters are resampled if they do not align (same origin and resolution) with the first raster

method

character. The resampling method used (only if resample is TRUE and algo is 1 or 2). One of "nearest", "bilinear", "cubic", "cubicspline", "lanczos", "average", "mode" as in resample. If the value is "", "nearest" is used for categorical rasters and "bilinear" for other rasters

filename

character. Output filename

overwrite

logical. If TRUE, filename is overwritten

wopt

list with named options for writing files as in writeRaster

Value

SpatRaster or SpatVector

See also

Combining tiles with vrt may be more efficient than using merge.

See mosaic for averaging or blending the values of overlapping regions.

See classify to merge a SpatRaster and a data.frame and union to combine SpatExtent objects.

Examples

x <- rast(xmin=-110, xmax=-80, ymin=40, ymax=70, res=1, vals=1)
y <- rast(xmin=-85, xmax=-55, ymax=60, ymin=30, res=1, vals=2)
z <- rast(xmin=-60, xmax=-30, ymax=50, ymin=20, res=1, vals=3)

m1 <- merge(x, y, z)
m2 <- merge(z, y, x)
m3 <- merge(y, x, z)
# panel(c(m1, m2, m3))

# if you have many SpatRasters, it may be convenient
# to make a SpatRasterCollection
# s <- sprc(list(x, y, z))
s <- sprc(x, y, z)

sm1 <- merge(s, algo=1, first=FALSE)
sm2 <- merge(s, algo=2, first=FALSE)
#sm3 <- merge(s, algo=3, first=FALSE)

## SpatVector with data.frame
f <- system.file("ex/lux.shp", package="terra")
p <- vect(f)
dfr <- data.frame(District=p$NAME_1, Canton=p$NAME_2, Value=round(runif(length(p), 100, 1000)))
dfr <- dfr[1:5, ]
pm <- merge(p, dfr, all.x=TRUE, by.x=c('NAME_1', 'NAME_2'), by.y=c('District', 'Canton'))
pm
#> class       : SpatVector
#> geometry    : polygons
#> dimensions  : 12, 7  (geometries, attributes)
#> extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> names       :   NAME_1   NAME_2  ID_1  ID_2  AREA   POP Value
#> type        :    <chr>    <chr> <num> <num> <num> <num> <num>
#> values      : Diekirch Clervaux     1     1   312 18081   613
#>               Diekirch Diekirch     1     2   218 32543   531
#>               Diekirch  Redange     1     3   259 18664   377
#>               ...
values(pm)
#>          NAME_1           NAME_2 ID_1 ID_2 AREA    POP Value
#> 1      Diekirch         Clervaux    1    1  312  18081   613
#> 2      Diekirch         Diekirch    1    2  218  32543   531
#> 3      Diekirch          Redange    1    3  259  18664   377
#> 4      Diekirch          Vianden    1    4   76   5163   897
#> 5      Diekirch            Wiltz    1    5  263  16735   588
#> 6  Grevenmacher       Echternach    2    6  188  18899    NA
#> 7  Grevenmacher           Remich    2    7  129  22366    NA
#> 8  Grevenmacher     Grevenmacher    2   12  210  29828    NA
#> 9    Luxembourg         Capellen    3    8  185  48187    NA
#> 10   Luxembourg Esch-sur-Alzette    3    9  251 176820    NA
#> 11   Luxembourg       Luxembourg    3   10  237 182607    NA
#> 12   Luxembourg           Mersch    3   11  233  32112    NA