# crop
Crop data cube extent by space and/or time
```{r include=FALSE}
library(gdalcubes)
```
## Description
Create a proxy data cube, which crops a data cube by a spatial and/or temporal extent.
## Usage
```r
crop(cube, extent = NULL, iextent = NULL, snap = "near")
```
## Arguments
| Argument | Description |
|:------------|:----------------------------------|
| cube | source data cube |
| extent | list with numeric items left, right, top, bottom, and character items t0 and t1, or a subset thereof, see examples |
| iextent | list with length-two integer items named x, y, and t, defining the lower and upper boundaries as integer coordinates, see examples |
| snap | one of 'near', 'in', or 'out'; ignored if using `iextent` |
## Details
The new extent can be specified by spatial coordinates and datetime values (using the `extent` argument), or as zero-based integer indexes (using the `iextent` argument).
In the former case, `extent` expects a list with numeric items left, right, top, bottom, t0, and t1, or a subset thereof. In the latter case,
`iextent` is expected as a list with length-two integer vectors x, y, and t as items, defining the lower and upper cell indexes per dimension.
Notice that it is possible to crop only selected boundaries (e.g., only the right boundary) as missing boundaries in the `extent` or NA / NULL values in the `iextent` arguments are considered as "no change".
It is, however, not possible to mix arguments `extent` and `iextent`.
If `extent` is given, the `snap` argument can be used to define what happens if the new boundary falls within a data cube cell.
## Note
This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.
## Examples
```{r}
# create image collection from example Landsat data only
# if not already done in other examples
if (!file.exists(file.path(tempdir(), "L8.db"))) {
L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
".TIF", recursive = TRUE, full.names = TRUE)
create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
}
L8.col = image_collection(file.path(tempdir(), "L8.db"))
v = cube_view(extent=list(left=388941.2, right=766552.4,
bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
srs="EPSG:32618", nx = 497, ny=526, dt="P3M", aggregation = "median")
L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
L8.rgb = select_bands(L8.cube, c("B02", "B03", "B04"))
# crop by integer indexes
L8.cropped = crop(L8.rgb, iextent = list(x=c(0,400), y=c(0,400), t=c(1,1)))
# crop by spatiotemporal coordinates
L8.cropped = crop(L8.rgb, extent = list(left=388941.2, right=766552.4,
bottom=4345299, top=4744931, t0="2018-01", t1="2018-06"), snap = "in")
L8.cropped
L8.cropped = crop(L8.rgb, extent = list(left=388941.2, right=766552.4,
bottom=4345299, top=4744931, t0="2018-01", t1="2018-06"), snap = "near")
L8.cropped
plot(L8.cropped, rgb = 3:1, zlim=c(5000,10000))
```
