Group SpatVector objects by rows — rowwise.SpatVector • tidyterra

rowwise() allows you to compute on a SpatVector a row-at-a-time. This is most useful when a vectorised function doesn't exist.

Most dplyr verbs implementation in tidyterra preserve row-wise grouping, with the exception of summarise.SpatVector(). You can explicitly ungroup with ungroup.SpatVector() or as_tibble(), or convert to a grouped SpatVector with group_by.SpatVector().

Usage

# S3 method for class 'SpatVector'
rowwise(data, ...)

Arguments

data: A SpatVector object. See Methods.
...: <tidy-select> Variables to be preserved when calling summarise.SpatVector(). This is typically a set of variables whose combination uniquely identify each row. See dplyr::rowwise().

Value

The same SpatVector object with an additional attribute.

Details

See Details on dplyr::rowwise().

Methods

Implementation of the generic dplyr::rowwise() function for SpatVector objects.

When mixing terra and dplyr syntax on a row-wise SpatVector (i.e, subsetting a SpatVector like v[1:3,1:2]) the groups attribute can be corrupted. tidyterra would try to re-generate the SpatVector. This would be triggered the next time you use a dplyr verb on your SpatVector.

Note also that some operations (as terra::spatSample()) would create a new SpatVector. In these cases, the result won't preserve the groups attribute. Use rowwise.SpatVector() to re-group.

Examples

library(terra)
library(dplyr)

v <- terra::vect(system.file("shape/nc.shp", package = "sf"))

# Select new births
nb <- v %>%
  select(starts_with("NWBIR")) %>%
  glimpse()
#> #  A SpatVector 100 x 2
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat NAD27 (EPSG:4267)
#> #  Extent (x / y) : ([84° 19' 25.87" W / 75° 27' 25.12" W] , [33° 52' 55.17" N / 36° 35' 22.74" N])
#> 
#> $ NWBIR74 <dbl> 10, 10, 208, 123, 1066, 954, 115, 254, 748, 160, 550, 1243, 93…
#> $ NWBIR79 <dbl> 19, 12, 260, 145, 1197, 1237, 139, 371, 844, 176, 597, 1369, 1…

# Compute the mean of NWBIR on each geometry
nb %>%
  rowwise() %>%
  mutate(nb_mean = mean(c(NWBIR74, NWBIR79)))
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 100, 3  (geometries, attributes)
#>  extent      : -84.32385, -75.45698, 33.88199, 36.58965  (xmin, xmax, ymin, ymax)
#>  source      : nc.shp
#>  coord. ref. : lon/lat NAD27 (EPSG:4267) 
#>  names       : NWBIR74 NWBIR79 nb_mean
#>  type        :   <num>   <num>   <num>
#>  values      :      10      19    14.5
#>                     10      12      11
#>                    208     260     234

# Additional examples
# \donttest{
# use c_across() to more easily select many variables
nb %>%
  rowwise() %>%
  mutate(m = mean(c_across(NWBIR74:NWBIR79)))
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 100, 3  (geometries, attributes)
#>  extent      : -84.32385, -75.45698, 33.88199, 36.58965  (xmin, xmax, ymin, ymax)
#>  source      : nc.shp
#>  coord. ref. : lon/lat NAD27 (EPSG:4267) 
#>  names       : NWBIR74 NWBIR79     m
#>  type        :   <num>   <num> <num>
#>  values      :      10      19  14.5
#>                     10      12    11
#>                    208     260   234

# Compute the minimum of x and y in each row

nb %>%
  rowwise() %>%
  mutate(min = min(c_across(NWBIR74:NWBIR79)))
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 100, 3  (geometries, attributes)
#>  extent      : -84.32385, -75.45698, 33.88199, 36.58965  (xmin, xmax, ymin, ymax)
#>  source      : nc.shp
#>  coord. ref. : lon/lat NAD27 (EPSG:4267) 
#>  names       : NWBIR74 NWBIR79   min
#>  type        :   <num>   <num> <num>
#>  values      :      10      19    10
#>                     10      12    10
#>                    208     260   208

# Summarising
v %>%
  rowwise() %>%
  summarise(mean_bir = mean(BIR74, BIR79)) %>%
  glimpse() %>%
  autoplot(aes(fill = mean_bir))
#> #  A SpatVector 100 x 1
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat NAD27 (EPSG:4267)
#> #  Extent (x / y) : ([84° 19' 25.87" W / 75° 27' 25.12" W] , [33° 52' 55.17" N / 36° 35' 22.74" N])
#> 
#> $ mean_bir <dbl> 1091, 487, 3188, 508, 1421, 1452, 286, 420, 968, 1612, 1035, …


# Supply a variable to be kept
v %>%
  mutate(id2 = as.integer(CNTY_ID / 100)) %>%
  rowwise(id2) %>%
  summarise(mean_bir = mean(BIR74, BIR79)) %>%
  glimpse() %>%
  autoplot(aes(fill = as.factor(id2)))
#> #  A SpatVector 100 x 2
#> #  Geometry type: Polygons
#> #  Geodetic CRS: lon/lat NAD27 (EPSG:4267)
#> #  Extent (x / y) : ([84° 19' 25.87" W / 75° 27' 25.12" W] , [33° 52' 55.17" N / 36° 35' 22.74" N])
#> 
#> Groups: id2 [2]
#> $ id2      <int> 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 1…
#> $ mean_bir <dbl> 1091, 487, 3188, 508, 1421, 1452, 286, 420, 968, 1612, 1035, …

# }

Group `SpatVector` objects by rows