tidyterra (version 0.7.2)

group-by.SpatVector: Group a SpatVector by one or more variables

Description

Most data operations are done on groups defined by variables. group_by.SpatVector() adds new attributes to an existing SpatVector indicating the corresponding groups. See Methods.

Usage

# S3 method for SpatVector
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))
# S3 method for SpatVector
ungroup(x, ...)

Value

A SpatVector object with an additional attribute.

Arguments

.data, x

A SpatVector object. See Methods.

...

In group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separate mutate() step before the group_by(). Computations are not allowed in nest_by(). In ungroup(), variables to remove from the grouping.

.add

When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE.

This argument was previously called add, but that prevented creating a new grouping variable called add, and conflicts with our naming conventions.

.drop

Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when .data has been previously grouped with .drop = FALSE. See group_by_drop_default() for details.

Methods

Implementation of the generic dplyr::group_by() family functions for SpatVector objects.

When mixing terra and dplyr syntax on a grouped SpatVector (i.e, subsetting a SpatVector like v[1:3,1:2]) the groups attribute can be corrupted. tidyterra would try to re-group 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 group_by() to re-group.

Details

See Details on dplyr::group_by().

See Also

dplyr::group_by(), dplyr::ungroup()

Other dplyr verbs that operate on group of rows: count.SpatVector(), rowwise.SpatVector(), summarise.SpatVector()

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

Examples

# \donttest{

library(terra)
f <- system.file("ex/lux.shp", package = "terra")
p <- vect(f)


by_name1 <- p %>% group_by(NAME_1)

# grouping doesn't change how the SpatVector looks
by_name1

# But add metadata for grouping: See the coercion to tibble

# Not grouped
p_tbl <- as_tibble(p)
class(p_tbl)
head(p_tbl, 3)

# Grouped
by_name1_tbl <- as_tibble(by_name1)
class(by_name1_tbl)
head(by_name1_tbl, 3)


# It changes how it acts with the other dplyr verbs:
by_name1 %>% summarise(
  pop = mean(POP),
  area = sum(AREA)
)

# Each call to summarise() removes a layer of grouping
by_name2_name1 <- p %>% group_by(NAME_2, NAME_1)

by_name2_name1
group_data(by_name2_name1)

by_name2 <- by_name2_name1 %>% summarise(n = dplyr::n())
by_name2
group_data(by_name2)

# To removing grouping, use ungroup
by_name2 %>%
  ungroup() %>%
  summarise(n = sum(n))

# By default, group_by() overrides existing grouping
by_name2_name1 %>%
  group_by(ID_1, ID_2) %>%
  group_vars()


# Use add = TRUE to instead append
by_name2_name1 %>%
  group_by(ID_1, ID_2, .add = TRUE) %>%
  group_vars()

# You can group by expressions: this is a short-hand
# for a mutate() followed by a group_by()
p %>%
  group_by(ID_COMB = ID_1 * 100 / ID_2) %>%
  relocate(ID_COMB, .before = 1)
# }