Skip to contents

Subset cells/rows/columns/geometries using their positions

Source: R/slice-Spat.R
slice.Spat.Rd

slice() methods lets you index cells/rows/columns/geometries by their (integer) locations. It allows you to select, remove, and duplicate those dimensions of a Spat* object.

If you want to slice your SpatRaster by geographic coordinates use filter.SpatRaster() method.

It is accompanied by a number of helpers for common use cases:

  • slice_head() and slice_tail() select the first or last cells/geometries.

  • slice_sample() randomly selects cells/geometries.

  • slice_rows() and slice_cols() allow to subset entire rows or columns, of a SpatRaster.

  • slice_colrows() subsets regions of the SpatRaster by row and column position of a SpatRaster.

You can get a skeleton of your SpatRaster with the cell, column and row index with as_coordinates().

See Methods for details.

Usage

# S3 method for class 'SpatRaster'
slice(.data, ..., .preserve = FALSE, .keep_extent = FALSE)

# S3 method for class 'SpatVector'
slice(.data, ..., .preserve = FALSE)

# S3 method for class 'SpatRaster'
slice_head(.data, ..., n, prop, .keep_extent = FALSE)

# S3 method for class 'SpatVector'
slice_head(.data, ..., n, prop)

# S3 method for class 'SpatRaster'
slice_tail(.data, ..., n, prop, .keep_extent = FALSE)

# S3 method for class 'SpatVector'
slice_tail(.data, ..., n, prop)

# S3 method for class 'SpatRaster'
slice_min(
  .data,
  order_by,
  ...,
  n,
  prop,
  with_ties = TRUE,
  .keep_extent = FALSE,
  na.rm = TRUE
)

# S3 method for class 'SpatVector'
slice_min(.data, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

# S3 method for class 'SpatRaster'
slice_max(
  .data,
  order_by,
  ...,
  n,
  prop,
  with_ties = TRUE,
  .keep_extent = FALSE,
  na.rm = TRUE
)

# S3 method for class 'SpatVector'
slice_max(.data, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

# S3 method for class 'SpatRaster'
slice_sample(
  .data,
  ...,
  n,
  prop,
  weight_by = NULL,
  replace = FALSE,
  .keep_extent = FALSE
)

# S3 method for class 'SpatVector'
slice_sample(.data, ..., n, prop, weight_by = NULL, replace = FALSE)

slice_rows(.data, ...)

# S3 method for class 'SpatRaster'
slice_rows(.data, ..., .keep_extent = FALSE)

slice_cols(.data, ...)

# S3 method for class 'SpatRaster'
slice_cols(.data, ..., .keep_extent = FALSE)

slice_colrows(.data, ...)

# S3 method for class 'SpatRaster'
slice_colrows(.data, ..., cols, rows, .keep_extent = FALSE, inverse = FALSE)

Arguments

.data

A SpatRaster created with terra::rast() or a SpatVector created with terra::vect().

...

<data-masking> Integer row values. Provide either positive values to keep, or negative values to drop.

The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored. See Methods.

.preserve

Ignored for Spat* objects.

.keep_extent

Should the extent of the resulting SpatRaster be kept? See also terra::trim(), terra::extend().

n, prop

Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows.

A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.

order_by

<data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

na.rm

Logical, should cells that present a value of NA removed when computing slice_min()/slice_max()?. The default is TRUE.

na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in arrange()), so they will only be included if there are insufficient non-missing values to reach n/prop.

weight_by

<data-masking> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

replace

Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.

cols, rows

Integer col/row values of the SpatRaster

inverse

If TRUE, .data is inverse-masked to the given selection. See terra::mask().

Value

A Spat* object of the same class than .data. See Methods.

terra equivalent

terra::subset(), terra::spatSample()

Methods

Implementation of the generic dplyr::slice() function.

SpatRaster

The result is a SpatRaster with the crs and resolution of the input and where cell values of the selected cells/columns/rows are preserved.

Use .keep_extent = TRUE to preserve the extent of .data on the output. The non-selected cells would present a value of NA.

SpatVector

The result is a SpatVector where the attributes of the selected geometries are preserved. If .data is a grouped SpatVector, the operation will be performed on each group, so that (e.g.) slice_head(df, n = 5) will select the first five rows in each group.

See also

dplyr::slice(), terra::spatSample().

You can get a skeleton of your SpatRaster with the cell, column and row index with as_coordinates().

If you want to slice by geographic coordinates use filter.SpatRaster().

Other single table verbs: arrange.SpatVector(), filter.Spat, mutate.Spat, rename.Spat, select.Spat, summarise.SpatVector()

Other dplyr verbs that operate on rows: arrange.SpatVector(), distinct.SpatVector(), filter.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, summarise.SpatVector()

Examples



library(terra)

f <- system.file("extdata/cyl_temp.tif", package = "tidyterra")
r <- rast(f)

# Slice first 100 cells
r %>%
  slice(1:100) %>%
  plot()


# Rows
r %>%
  slice_rows(1:30) %>%
  plot()


# Cols
r %>%
  slice_cols(-(20:50)) %>%
  plot()


# Spatial sample
r %>%
  slice_sample(prop = 0.2) %>%
  plot()



# Slice regions
r %>%
  slice_colrows(
    cols = c(20:40, 60:80),
    rows = -c(1:20, 30:50)
  ) %>%
  plot()


# Group wise operation with SpatVectors--------------------------------------
v <- terra::vect(system.file("ex/lux.shp", package = "terra"))

# \donttest{
glimpse(v) %>% autoplot(aes(fill = NAME_1))
#> #  A SpatVector 12 x 6
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y) : ([5° 44' 38.9" E / 6° 31' 41.71" E] , [49° 26' 52.11" N / 50° 10' 53.84" N])
#> 
#> $ ID_1   <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3
#> $ NAME_1 <chr> "Diekirch", "Diekirch", "Diekirch", "Diekirch", "Diekirch", "Gr…
#> $ ID_2   <dbl> 1, 2, 3, 4, 5, 6, 7, 12, 8, 9, 10, 11
#> $ NAME_2 <chr> "Clervaux", "Diekirch", "Redange", "Vianden", "Wiltz", "Echtern…
#> $ AREA   <dbl> 312, 218, 259, 76, 263, 188, 129, 210, 185, 251, 237, 233
#> $ POP    <int> 18081, 32543, 18664, 5163, 16735, 18899, 22366, 29828, 48187, 1…


gv <- v %>% group_by(NAME_1)
# All slice helpers operate per group, silently truncating to the group size
gv %>%
  slice_head(n = 1) %>%
  glimpse() %>%
  autoplot(aes(fill = NAME_1))
#> #  A SpatVector 3 x 6
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y) : ([5° 49' 34.44" E / 6° 31' 41.71" E] , [49° 32' 55.33" N / 50° 10' 53.84" N])
#> 
#> Groups: NAME_1 [3]
#> $ ID_1   <dbl> 1, 2, 3
#> $ NAME_1 <chr> "Diekirch", "Grevenmacher", "Luxembourg"
#> $ ID_2   <dbl> 1, 6, 8
#> $ NAME_2 <chr> "Clervaux", "Echternach", "Capellen"
#> $ AREA   <dbl> 312, 188, 185
#> $ POP    <int> 18081, 18899, 48187

gv %>%
  slice_tail(n = 1) %>%
  glimpse() %>%
  autoplot(aes(fill = NAME_1))
#> #  A SpatVector 3 x 6
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y) : ([5° 44' 38.9" E / 6° 30' 59.35" E] , [49° 35' 13.15" N / 50° 2' 10.76" N])
#> 
#> Groups: NAME_1 [3]
#> $ ID_1   <dbl> 1, 2, 3
#> $ NAME_1 <chr> "Diekirch", "Grevenmacher", "Luxembourg"
#> $ ID_2   <dbl> 5, 12, 11
#> $ NAME_2 <chr> "Wiltz", "Grevenmacher", "Mersch"
#> $ AREA   <dbl> 263, 210, 233
#> $ POP    <int> 16735, 29828, 32112

gv %>%
  slice_min(AREA, n = 1) %>%
  glimpse() %>%
  autoplot(aes(fill = NAME_1))
#> #  A SpatVector 3 x 6
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y) : ([5° 51' 7.49" E / 6° 22' 54.12" E] , [49° 27' 53.95" N / 49° 59' 2.98" N])
#> 
#> Groups: NAME_1 [3]
#> $ ID_1   <dbl> 1, 2, 3
#> $ NAME_1 <chr> "Diekirch", "Grevenmacher", "Luxembourg"
#> $ ID_2   <dbl> 4, 7, 8
#> $ NAME_2 <chr> "Vianden", "Remich", "Capellen"
#> $ AREA   <dbl> 76, 129, 185
#> $ POP    <int> 5163, 22366, 48187

gv %>%
  slice_max(AREA, n = 1) %>%
  glimpse() %>%
  autoplot(aes(fill = NAME_1))
#> #  A SpatVector 3 x 6
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> #  Extent (x / y) : ([5° 48' 37.74" E / 6° 30' 59.35" E] , [49° 26' 52.11" N / 50° 10' 53.84" N])
#> 
#> Groups: NAME_1 [3]
#> $ ID_1   <dbl> 1, 2, 3
#> $ NAME_1 <chr> "Diekirch", "Grevenmacher", "Luxembourg"
#> $ ID_2   <dbl> 1, 12, 9
#> $ NAME_2 <chr> "Clervaux", "Grevenmacher", "Esch-sur-Alzette"
#> $ AREA   <dbl> 312, 210, 251
#> $ POP    <int> 18081, 29828, 176820

# }