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'tidyverse' methods for sftime objects

Source: R/join.R, R/tidyverse.R
tidyverse.Rd

'tidyverse' methods for sftime objects. Geometries are sticky, use as.data.frame to let dplyr's own methods drop them. Use these methods without the .sftime suffix and after loading the 'tidyverse' package with the generic (or after loading package 'tidyverse').

Usage

inner_join.sftime(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

left_join.sftime(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

right_join.sftime(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

full_join.sftime(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

semi_join.sftime(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

anti_join.sftime(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

filter.sftime(.data, ..., .dots)

arrange.sftime(.data, ..., .dots)

group_by.sftime(.data, ..., add = FALSE)

ungroup.sftime(.data, ...)

rowwise.sftime(.data, ...)

mutate.sftime(.data, ..., .dots)

transmute.sftime(.data, ..., .dots)

select.sftime(.data, ...)

rename.sftime(.data, ...)

slice.sftime(.data, ..., .dots)

summarise.sftime(.data, ..., .dots, do_union = TRUE, is_coverage = FALSE)

summarize.sftime(.data, ..., .dots, do_union = TRUE, is_coverage = FALSE)

distinct.sftime(.data, ..., .keep_all = FALSE)

gather.sftime(
  data,
  key,
  value,
  ...,
  na.rm = FALSE,
  convert = FALSE,
  factor_key = FALSE
)

pivot_longer.sftime(
  data,
  cols,
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL,
  ...
)

spread.sftime(
  data,
  key,
  value,
  fill = NA,
  convert = FALSE,
  drop = TRUE,
  sep = NULL
)

sample_n.sftime(
  tbl,
  size,
  replace = FALSE,
  weight = NULL,
  .env = parent.frame()
)

sample_frac.sftime(
  tbl,
  size = 1,
  replace = FALSE,
  weight = NULL,
  .env = parent.frame()
)

nest.sftime(.data, ...)

unnest.sftime(data, ..., .preserve = NULL)

separate.sftime(
  data,
  col,
  into,
  sep = "[^[:alnum:]]+",
  remove = TRUE,
  convert = FALSE,
  extra = "warn",
  fill = "warn",
  ...
)

unite.sftime(data, col, ..., sep = "_", remove = TRUE)

separate_rows.sftime(data, ..., sep = "[^[:alnum:]]+", convert = FALSE)

drop_na.sftime(data, ...)

Arguments

x

An object of class sftime.

y

See dplyr::`mutate-joins`.

by

A join specification created with join_by(), or a character vector of variables to join by.

If NULL, the default, *_join() will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly.

To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b.

To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c).

join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins.

For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b").

To perform a cross-join, generating all combinations of x and y, see cross_join().

copy

If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.

suffix

If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.

...

other arguments

.data

An object of class stime.

.dots

see corresponding function in package dplyr

add

see corresponding function in dplyr

do_union

logical; in case summary does not create a geometry column, should geometries be created by unioning using st_union, or simply by combining using st_combine? Using st_union resolves internal boundaries, but in case of unioning points, this will likely change the order of the points; see Details.

is_coverage

logical; if do_union is TRUE, use an optimized algorithm for features that form a polygonal coverage (have no overlaps)

.keep_all

see corresponding function in dplyr

data

see original function docs

key

see original function docs

value

see original function docs

na.rm

see original function docs

convert

see separate_rows

factor_key

see original function docs

cols

see original function docs

names_to

see original function docs

names_prefix

see original function docs

names_sep

see original function docs

names_pattern

see original function docs

names_ptypes

see original function docs

names_transform

see original function docs

names_repair

see original function docs

values_to

see original function docs

values_drop_na

see original function docs

values_ptypes

see original function docs

values_transform

see original function docs

fill

see original function docs

drop

see original function docs

sep

see separate_rows

tbl

see original function docs

size

see original function docs

replace

see original function docs

weight

see original function docs

.env

see original function docs

.preserve

see unnest

col

see separate

into

see separate

remove

see separate

extra

see separate

Value

  • For _join methods: An object of class sftime representing the joining result of x and y. See mutate-joins.

  • For filter: See filter.

  • For arrange: See arrange.

  • For group_by and ungroup: A grouped sftime object. See arrange.

  • For rowwise: An sftime object. See rowwise.

  • For mutate and transmute: See mutate.

  • For select: See select. If the active time column is not explicitly selected, a sf object is returned.

  • For rename: See rename.

  • For slice: See slice.

  • For summarize and summarise: See summarise.

  • For distinct: See distinct.

  • For gather: See gather.

Examples

g1 <- st_sfc(st_point(1:2), st_point(c(5, 8)), st_point(c(2, 9)))
x1 <- st_sftime(a = 1:3, geometry = g1, time = Sys.time())

g2 <- st_sfc(st_point(c(4, 6)), st_point(c(4, 6)), st_point(c(4, 6)))
x2 <- st_sftime(a = 2:4, geometry = g2, time = Sys.time())

library(dplyr)

## inner_join
inner_join(x1, as.data.frame(x2), by = "a") # note: the active time column is
#> Spatiotemporal feature collection with 2 features and 2 fields
#> Active geometry column: geometry.x
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 2 ymin: 8 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 2 2024-09-13 09:15:21 POINT (5 8) POINT (4 6) 2024-09-13 09:15:21
#> 2 3 2024-09-13 09:15:21 POINT (2 9) POINT (4 6) 2024-09-13 09:15:21
# time.x and the active geometry column geometry.x

inner_join(x2, as.data.frame(x1), by = "a")
#> Spatiotemporal feature collection with 2 features and 2 fields
#> Active geometry column: geometry.x
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4 ymin: 6 xmax: 4 ymax: 6
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.396825 to 2024-09-13 09:15:21.396825.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 2 2024-09-13 09:15:21 POINT (4 6) POINT (5 8) 2024-09-13 09:15:21
#> 2 3 2024-09-13 09:15:21 POINT (4 6) POINT (2 9) 2024-09-13 09:15:21

## left_join
left_join(x1, as.data.frame(x2), by = "a")
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Active geometry column: geometry.x
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 1                <NA> POINT (1 2) POINT EMPTY 2024-09-13 09:15:21
#> 2 2 2024-09-13 09:15:21 POINT (5 8) POINT (4 6) 2024-09-13 09:15:21
#> 3 3 2024-09-13 09:15:21 POINT (2 9) POINT (4 6) 2024-09-13 09:15:21

left_join(x2, as.data.frame(x1), by = "a")
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Active geometry column: geometry.x
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4 ymin: 6 xmax: 4 ymax: 6
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.396825 to 2024-09-13 09:15:21.396825.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 2 2024-09-13 09:15:21 POINT (4 6) POINT (5 8) 2024-09-13 09:15:21
#> 2 3 2024-09-13 09:15:21 POINT (4 6) POINT (2 9) 2024-09-13 09:15:21
#> 3 4                <NA> POINT (4 6) POINT EMPTY 2024-09-13 09:15:21

## right_join
right_join(x1, as.data.frame(x2), by = "a")
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Active geometry column: geometry.x (with 1 geometry empty)
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 2 ymin: 8 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 2 2024-09-13 09:15:21 POINT (5 8) POINT (4 6) 2024-09-13 09:15:21
#> 2 3 2024-09-13 09:15:21 POINT (2 9) POINT (4 6) 2024-09-13 09:15:21
#> 3 4 2024-09-13 09:15:21 POINT EMPTY POINT (4 6)                <NA>

right_join(x2, as.data.frame(x1), by = "a")
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Active geometry column: geometry.x (with 1 geometry empty)
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4 ymin: 6 xmax: 4 ymax: 6
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.396825 to 2024-09-13 09:15:21.396825.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 2 2024-09-13 09:15:21 POINT (4 6) POINT (5 8) 2024-09-13 09:15:21
#> 2 3 2024-09-13 09:15:21 POINT (4 6) POINT (2 9) 2024-09-13 09:15:21
#> 3 1 2024-09-13 09:15:21 POINT EMPTY POINT (1 2)                <NA>

## full_join
full_join(x1, as.data.frame(x2), by = "a")
#> Spatiotemporal feature collection with 4 features and 2 fields
#> Active geometry column: geometry.x (with 1 geometry empty)
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 1                <NA> POINT (1 2) POINT EMPTY 2024-09-13 09:15:21
#> 2 2 2024-09-13 09:15:21 POINT (5 8) POINT (4 6) 2024-09-13 09:15:21
#> 3 3 2024-09-13 09:15:21 POINT (2 9) POINT (4 6) 2024-09-13 09:15:21
#> 4 4 2024-09-13 09:15:21 POINT EMPTY POINT (4 6)                <NA>

full_join(x2, as.data.frame(x1), by = "a")
#> Spatiotemporal feature collection with 4 features and 2 fields
#> Active geometry column: geometry.x (with 1 geometry empty)
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4 ymin: 6 xmax: 4 ymax: 6
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.396825 to 2024-09-13 09:15:21.396825.
#>   a              time.y  geometry.x  geometry.y              time.x
#> 1 2 2024-09-13 09:15:21 POINT (4 6) POINT (5 8) 2024-09-13 09:15:21
#> 2 3 2024-09-13 09:15:21 POINT (4 6) POINT (2 9) 2024-09-13 09:15:21
#> 3 4                <NA> POINT (4 6) POINT EMPTY 2024-09-13 09:15:21
#> 4 1 2024-09-13 09:15:21 POINT EMPTY POINT (1 2)                <NA>

## semi_join
semi_join(x1, as.data.frame(x2), by = "a")
#> Spatiotemporal feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 2 ymin: 8 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a    geometry                time
#> 1 2 POINT (5 8) 2024-09-13 09:15:21
#> 2 3 POINT (2 9) 2024-09-13 09:15:21

semi_join(x2, as.data.frame(x1), by = "a")
#> Spatiotemporal feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4 ymin: 6 xmax: 4 ymax: 6
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.396825 to 2024-09-13 09:15:21.396825.
#>   a    geometry                time
#> 1 2 POINT (4 6) 2024-09-13 09:15:21
#> 2 3 POINT (4 6) 2024-09-13 09:15:21

## anti_join
anti_join(x1, as.data.frame(x2), by = "a")
#> Spatiotemporal feature collection with 1 feature and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 1 ymax: 2
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Representing 2024-09-13 09:15:21.393653.
#>   a    geometry                time
#> 1 1 POINT (1 2) 2024-09-13 09:15:21

anti_join(x2, as.data.frame(x1), by = "a")
#> Spatiotemporal feature collection with 1 feature and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4 ymin: 6 xmax: 4 ymax: 6
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Representing 2024-09-13 09:15:21.396825.
#>   a    geometry                time
#> 1 4 POINT (4 6) 2024-09-13 09:15:21

## filter
filter(x1, a <= 2)
#> Spatiotemporal feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 8
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a    geometry                time
#> 1 1 POINT (1 2) 2024-09-13 09:15:21
#> 2 2 POINT (5 8) 2024-09-13 09:15:21

## arrange
arrange(x1, dplyr::desc(a))
#> Spatiotemporal feature collection with 3 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time    geometry
#> 1 3 2024-09-13 09:15:21 POINT (2 9)
#> 2 2 2024-09-13 09:15:21 POINT (5 8)
#> 3 1 2024-09-13 09:15:21 POINT (1 2)

## group_by
group_by(x1, time)
#> Spatiotemporal feature collection with 3 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#> # A tibble: 3 × 3
#> # Groups:   time [1]
#>       a geometry time               
#> * <int>  <POINT> <dttm>             
#> 1     1    (1 2) 2024-09-13 09:15:21
#> 2     2    (5 8) 2024-09-13 09:15:21
#> 3     3    (2 9) 2024-09-13 09:15:21

## ungroup
ungroup(group_by(x1, time))
#> Spatiotemporal feature collection with 3 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#> # A tibble: 3 × 3
#>       a geometry time               
#> * <int>  <POINT> <dttm>             
#> 1     1    (1 2) 2024-09-13 09:15:21
#> 2     2    (5 8) 2024-09-13 09:15:21
#> 3     3    (2 9) 2024-09-13 09:15:21

## rowwise
x1 %>%
  mutate(a1 = 5:7) %>%
  rowwise() %>%
  mutate(a2 = mean(a, a1))
#> Spatiotemporal feature collection with 3 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#> # A tibble: 3 × 5
#> # Rowwise: 
#>       a geometry time                   a1    a2
#> * <int>  <POINT> <dttm>              <int> <int>
#> 1     1    (1 2) 2024-09-13 09:15:21     5     1
#> 2     2    (5 8) 2024-09-13 09:15:21     6     2
#> 3     3    (2 9) 2024-09-13 09:15:21     7     3

## mutate
x1 %>%
  mutate(a1 = 5:7)
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a    geometry                time a1
#> 1 1 POINT (1 2) 2024-09-13 09:15:21  5
#> 2 2 POINT (5 8) 2024-09-13 09:15:21  6
#> 3 3 POINT (2 9) 2024-09-13 09:15:21  7

## transmute
x1 %>%
  transmute(a1 = 5:7)
#> Simple feature collection with 3 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#>   a1    geometry
#> 1  5 POINT (1 2)
#> 2  6 POINT (5 8)
#> 3  7 POINT (2 9)

## select
x1 %>%
  select(-time) %>%
  select(geometry)
#> Simple feature collection with 3 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#>      geometry
#> 1 POINT (1 2)
#> 2 POINT (5 8)
#> 3 POINT (2 9)

## rename
x1 %>%
  rename(a1 = a)
#> Spatiotemporal feature collection with 3 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a1                time    geometry
#> 1  1 2024-09-13 09:15:21 POINT (1 2)
#> 2  2 2024-09-13 09:15:21 POINT (5 8)
#> 3  3 2024-09-13 09:15:21 POINT (2 9)

## slice
x1 %>%
  slice(1:2)
#> Spatiotemporal feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 8
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time    geometry
#> 1 1 2024-09-13 09:15:21 POINT (1 2)
#> 2 2 2024-09-13 09:15:21 POINT (5 8)

## summarise
x1 %>%
  summarise(time = mean(time))
#> Spatiotemporal feature collection with 1 feature and 0 fields
#> Geometry type: MULTIPOINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Representing 2024-09-13 09:15:21.393653.
#>                  time                       geometry
#> 1 2024-09-13 09:15:21 MULTIPOINT ((1 2), (2 9), (...
  
x1 %>%
  summarize(time = mean(time))
#> Spatiotemporal feature collection with 1 feature and 0 fields
#> Geometry type: MULTIPOINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Representing 2024-09-13 09:15:21.393653.
#>                  time                       geometry
#> 1 2024-09-13 09:15:21 MULTIPOINT ((1 2), (2 9), (...

## distinct
x1 %>%
  distinct(geometry)
#> Simple feature collection with 3 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#>      geometry
#> 1 POINT (1 2)
#> 2 POINT (5 8)
#> 3 POINT (2 9)

## gather
library(tidyr)
x1 %>%
  mutate(a1 = 5:7) %>%
  gather(key = "variable", value = "value", a, a1)
#> Spatiotemporal feature collection with 6 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>                  time key value    geometry
#> 1 2024-09-13 09:15:21   a     1 POINT (1 2)
#> 2 2024-09-13 09:15:21   a     2 POINT (5 8)
#> 3 2024-09-13 09:15:21   a     3 POINT (2 9)
#> 4 2024-09-13 09:15:21  a1     5 POINT (1 2)
#> 5 2024-09-13 09:15:21  a1     6 POINT (5 8)
#> 6 2024-09-13 09:15:21  a1     7 POINT (2 9)

## pivot_longer
x1 %>%
  mutate(a1 = 5:7) %>%
  pivot_longer(cols = c("a", "a1"), names_to = "variable", values_to = "value")
#> Spatiotemporal feature collection with 6 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#> # A tibble: 6 × 4
#>   geometry time                variable value
#> *  <POINT> <dttm>              <chr>    <int>
#> 1    (1 2) 2024-09-13 09:15:21 a            1
#> 2    (1 2) 2024-09-13 09:15:21 a1           5
#> 3    (5 8) 2024-09-13 09:15:21 a            2
#> 4    (5 8) 2024-09-13 09:15:21 a1           6
#> 5    (2 9) 2024-09-13 09:15:21 a            3
#> 6    (2 9) 2024-09-13 09:15:21 a1           7

## spread
x1 %>%
  mutate(a1 = 5:7) %>%
  gather(key = "variable", value = "value", a, a1) %>%
  spread(key = "variable", value = "value")
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>                  time a a1    geometry
#> 1 2024-09-13 09:15:21 1  5 POINT (1 2)
#> 2 2024-09-13 09:15:21 2  6 POINT (5 8)
#> 3 2024-09-13 09:15:21 3  7 POINT (2 9)

## sample_n
set.seed(234)
x1 %>%
  sample_n(size = 10, replace = TRUE)
#> Spatiotemporal feature collection with 10 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>    a                time    geometry
#> 1  1 2024-09-13 09:15:21 POINT (1 2)
#> 2  3 2024-09-13 09:15:21 POINT (2 9)
#> 3  2 2024-09-13 09:15:21 POINT (5 8)
#> 4  2 2024-09-13 09:15:21 POINT (5 8)
#> 5  2 2024-09-13 09:15:21 POINT (5 8)
#> 6  2 2024-09-13 09:15:21 POINT (5 8)
#> 7  1 2024-09-13 09:15:21 POINT (1 2)
#> 8  1 2024-09-13 09:15:21 POINT (1 2)
#> 9  3 2024-09-13 09:15:21 POINT (2 9)
#> 10 2 2024-09-13 09:15:21 POINT (5 8)

## sample_frac
x1 %>%
  sample_frac(size = 10, replace = TRUE) %>%
  sample_frac(size = 0.1, replace = FALSE)
#> Spatiotemporal feature collection with 3 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 2 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time    geometry
#> 1 1 2024-09-13 09:15:21 POINT (1 2)
#> 2 1 2024-09-13 09:15:21 POINT (1 2)
#> 3 3 2024-09-13 09:15:21 POINT (2 9)

## nest
x1 %>%
  nest(a1 = -time)
#>                  time                        a1
#> 1 2024-09-13 09:15:21 1, 2, 3, 1, 2, 5, 8, 2, 9

## unnest
x1 %>%
  mutate(a1 = list(1, c(1, 2), 5)) %>%
  unnest(a1)
#> Spatiotemporal feature collection with 4 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#> # A tibble: 4 × 4
#>       a geometry time                   a1
#> * <int>  <POINT> <dttm>              <dbl>
#> 1     1    (1 2) 2024-09-13 09:15:21     1
#> 2     2    (5 8) 2024-09-13 09:15:21     1
#> 3     2    (5 8) 2024-09-13 09:15:21     2
#> 4     3    (2 9) 2024-09-13 09:15:21     5

## separate
x1 %>%
  mutate(x = c(NA, "a.b", "a.d")) %>%
  separate(x, c("A", "B"))
#> Spatiotemporal feature collection with 3 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time    A    B    geometry
#> 1 1 2024-09-13 09:15:21 <NA> <NA> POINT (1 2)
#> 2 2 2024-09-13 09:15:21    a    b POINT (5 8)
#> 3 3 2024-09-13 09:15:21    a    d POINT (2 9)

## unite
x1 %>%
  mutate(x = c(NA, "a.b", "a.d")) %>%
  separate(x, c("A", "B")) %>%
  unite(x, c("A", "B"))
#> Spatiotemporal feature collection with 3 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time   col    geometry
#> 1 1 2024-09-13 09:15:21 NA_NA POINT (1 2)
#> 2 2 2024-09-13 09:15:21   a_b POINT (5 8)
#> 3 3 2024-09-13 09:15:21   a_d POINT (2 9)
  
## separate_rows
x1 %>%
  mutate(z = c("1", "2,3,4", "5,6")) %>%
  separate_rows(z, convert = TRUE)
#> Spatiotemporal feature collection with 6 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#> # A tibble: 6 × 4
#>       a geometry time                    z
#> * <int>  <POINT> <dttm>              <int>
#> 1     1    (1 2) 2024-09-13 09:15:21     1
#> 2     2    (5 8) 2024-09-13 09:15:21     2
#> 3     2    (5 8) 2024-09-13 09:15:21     3
#> 4     2    (5 8) 2024-09-13 09:15:21     4
#> 5     3    (2 9) 2024-09-13 09:15:21     5
#> 6     3    (2 9) 2024-09-13 09:15:21     6

## drop_na
x1 %>%
  mutate(z = c(1, 2, NA)) %>%
  drop_na(z)
#> Spatiotemporal feature collection with 2 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 5 ymax: 8
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time z    geometry
#> 1 1 2024-09-13 09:15:21 1 POINT (1 2)
#> 2 2 2024-09-13 09:15:21 2 POINT (5 8)
  
x1 %>%
  mutate(z = c(1, NA, NA)) %>%
  drop_na(z)   
#> Spatiotemporal feature collection with 1 feature and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1 ymin: 2 xmax: 1 ymax: 2
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Representing 2024-09-13 09:15:21.393653.
#>   a                time z    geometry
#> 1 1 2024-09-13 09:15:21 1 POINT (1 2)

x1 %>%
  mutate(time = replace(time, 1, NA)) %>%
  drop_na(time)
#> Spatiotemporal feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 2 ymin: 8 xmax: 5 ymax: 9
#> CRS:           NA
#> Time column with classes: 'POSIXct', 'POSIXt'.
#> Ranging from 2024-09-13 09:15:21.393653 to 2024-09-13 09:15:21.393653.
#>   a                time    geometry
#> 1 2 2024-09-13 09:15:21 POINT (5 8)
#> 2 3 2024-09-13 09:15:21 POINT (2 9)