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 class 'SpatVector'
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))
# S3 method for class 'SpatVector'
ungroup(x, ...)Arguments
- .data, x
A
SpatVectorobject. 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 separatemutate()step before thegroup_by(). Computations are not allowed innest_by(). Inungroup(), 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 calledadd, and conflicts with our naming conventions.- .drop
Drop groups formed by factor levels that don't appear in the data? The default is
TRUEexcept when.datahas been previously grouped with.drop = FALSE. Seegroup_by_drop_default()for details.
Details
See Details on dplyr::group_by().
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.
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
#> class : SpatVector
#> geometry : polygons
#> dimensions : 12, 6 (geometries, attributes)
#> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)
#> source : lux.shp
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> names : ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> type : <num> <chr> <num> <chr> <num> <num>
#> values : 1 Diekirch 1 Clervaux 312 1.808e+04
#> 1 Diekirch 2 Diekirch 218 3.254e+04
#> 1 Diekirch 3 Redange 259 1.866e+04
# But add metadata for grouping: See the coercion to tibble
# Not grouped
p_tbl <- as_tibble(p)
class(p_tbl)
#> [1] "tbl_df" "tbl" "data.frame"
head(p_tbl, 3)
#> # A tibble: 3 × 6
#> ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 1 Diekirch 1 Clervaux 312 18081
#> 2 1 Diekirch 2 Diekirch 218 32543
#> 3 1 Diekirch 3 Redange 259 18664
# Grouped
by_name1_tbl <- as_tibble(by_name1)
class(by_name1_tbl)
#> [1] "grouped_df" "tbl_df" "tbl" "data.frame"
head(by_name1_tbl, 3)
#> # A tibble: 3 × 6
#> # Groups: NAME_1 [1]
#> ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 1 Diekirch 1 Clervaux 312 18081
#> 2 1 Diekirch 2 Diekirch 218 32543
#> 3 1 Diekirch 3 Redange 259 18664
# It changes how it acts with the other dplyr verbs:
by_name1 %>% summarise(
pop = mean(POP),
area = sum(AREA)
)
#> class : SpatVector
#> geometry : polygons
#> dimensions : 3, 3 (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 pop area
#> type : <chr> <num> <num>
#> values : Diekirch 1.824e+04 1128
#> Grevenmacher 2.37e+04 527
#> Luxembourg 1.099e+05 906
# Each call to summarise() removes a layer of grouping
by_name2_name1 <- p %>% group_by(NAME_2, NAME_1)
by_name2_name1
#> class : SpatVector
#> geometry : polygons
#> dimensions : 12, 6 (geometries, attributes)
#> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)
#> source : lux.shp
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> names : ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> type : <num> <chr> <num> <chr> <num> <num>
#> values : 1 Diekirch 1 Clervaux 312 1.808e+04
#> 1 Diekirch 2 Diekirch 218 3.254e+04
#> 1 Diekirch 3 Redange 259 1.866e+04
group_data(by_name2_name1)
#> # A tibble: 12 × 3
#> NAME_2 NAME_1 .rows
#> <chr> <chr> <list<int>>
#> 1 Capellen Luxembourg [1]
#> 2 Clervaux Diekirch [1]
#> 3 Diekirch Diekirch [1]
#> 4 Echternach Grevenmacher [1]
#> 5 Esch-sur-Alzette Luxembourg [1]
#> 6 Grevenmacher Grevenmacher [1]
#> 7 Luxembourg Luxembourg [1]
#> 8 Mersch Luxembourg [1]
#> 9 Redange Diekirch [1]
#> 10 Remich Grevenmacher [1]
#> 11 Vianden Diekirch [1]
#> 12 Wiltz Diekirch [1]
by_name2 <- by_name2_name1 %>% summarise(n = dplyr::n())
by_name2
#> class : SpatVector
#> geometry : polygons
#> dimensions : 12, 3 (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_2 NAME_1 n
#> type : <chr> <chr> <int>
#> values : Capellen Luxembourg 1
#> Clervaux Diekirch 1
#> Diekirch Diekirch 1
group_data(by_name2)
#> # A tibble: 12 × 2
#> NAME_2 .rows
#> <chr> <list<int>>
#> 1 Capellen [1]
#> 2 Clervaux [1]
#> 3 Diekirch [1]
#> 4 Echternach [1]
#> 5 Esch-sur-Alzette [1]
#> 6 Grevenmacher [1]
#> 7 Luxembourg [1]
#> 8 Mersch [1]
#> 9 Redange [1]
#> 10 Remich [1]
#> 11 Vianden [1]
#> 12 Wiltz [1]
# To removing grouping, use ungroup
by_name2 %>%
ungroup() %>%
summarise(n = sum(n))
#> class : SpatVector
#> geometry : polygons
#> dimensions : 1, 1 (geometries, attributes)
#> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> names : n
#> type : <int>
#> values : 12
# By default, group_by() overrides existing grouping
by_name2_name1 %>%
group_by(ID_1, ID_2) %>%
group_vars()
#> [1] "ID_1" "ID_2"
# Use add = TRUE to instead append
by_name2_name1 %>%
group_by(ID_1, ID_2, .add = TRUE) %>%
group_vars()
#> [1] "NAME_2" "NAME_1" "ID_1" "ID_2"
# 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)
#> class : SpatVector
#> geometry : polygons
#> dimensions : 12, 7 (geometries, attributes)
#> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax)
#> source : lux.shp
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> names : ID_COMB ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> type : <num> <num> <chr> <num> <chr> <num> <num>
#> values : 100 1 Diekirch 1 Clervaux 312 1.808e+04
#> 50 1 Diekirch 2 Diekirch 218 3.254e+04
#> 33.33 1 Diekirch 3 Redange 259 1.866e+04
# }