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tidyverse

Tidyverse methods for sf objects (remove .sf suffix!)

Description

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

Usage

filter.sf(.data, ..., .dots)

arrange.sf(.data, ..., .dots)

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

ungroup.sf(x, ...)

rowwise.sf(x, ...)

mutate.sf(.data, ..., .dots)

transmute.sf(.data, ..., .dots)

select.sf(.data, ...)

rename.sf(.data, ...)

slice.sf(.data, ..., .dots)

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

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

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

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

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

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

nest.sf(.data, ...)

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

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

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

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

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

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

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

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

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

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

Arguments

`.data`	data object of class sf
`...`	other arguments
`.dots`	see corresponding function in package `dplyr`
`add`	see corresponding function in dplyr
`x`	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
`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
`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
`col`	see separate
`into`	see separate
`remove`	see separate
`extra`	see separate
`.preserve`	see unnest
`y`	A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
`by`	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 by different variables on `x` and `y`, use a named vector. For example, `by = c("a" = "b")` will match `x$a` to `y$b`. To join by multiple variables, use a vector with length > 1. For example, `by = c("a", "b")` will match `x$a` to `y$a` and `x$b` to `y$b`. Use a named vector to match different variables in `x` and `y`. For example, `by = c("a" = "b", "c" = "d")` will match `x$a` to `y$b` and `x$c` to `y$d`. To perform a cross-join, generating all combinations of `x` and `y`, use `by = character()`.
`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.

Details

select keeps the geometry regardless whether it is selected or not; to deselect it, first pipe through as.data.frame to let dplyr's own select drop it.

In case one or more of the arguments (expressions) in the summarise call creates a geometry list-column, the first of these will be the (active) geometry of the returned object. If this is not the case, a geometry column is created, depending on the value of do_union.

In case do_union is FALSE, summarise will simply combine geometries using c.sfg. When polygons sharing a boundary are combined, this leads to geometries that are invalid; see for instance https://github.com/r-spatial/sf/issues/681.

distinct gives distinct records for which all attributes and geometries are distinct; st_equals is used to find out which geometries are distinct.

nest assumes that a simple feature geometry list-column was among the columns that were nested.

Value

an object of class sf

Examples

library(dplyr)
nc = st_read(system.file("shape/nc.shp", package="sf"))
nc %>% filter(AREA > .1) %>% plot()
# plot 10 smallest counties in grey:
st_geometry(nc) %>% plot()
nc %>% select(AREA) %>% arrange(AREA) %>% slice(1:10) %>% plot(add = TRUE, col = 'grey')
title("the ten counties with smallest area")
nc$area_cl = cut(nc$AREA, c(0, .1, .12, .15, .25))
nc %>% group_by(area_cl) %>% class()
nc2 <- nc %>% mutate(area10 = AREA/10)
nc %>% transmute(AREA = AREA/10, geometry = geometry) %>% class()
nc %>% transmute(AREA = AREA/10) %>% class()
nc %>% select(SID74, SID79) %>% names()
nc %>% select(SID74, SID79, geometry) %>% names()
nc %>% select(SID74, SID79) %>% class()
nc %>% select(SID74, SID79, geometry) %>% class()
nc2 <- nc %>% rename(area = AREA)
nc %>% slice(1:2)
nc$area_cl = cut(nc$AREA, c(0, .1, .12, .15, .25))
nc.g <- nc %>% group_by(area_cl)
nc.g %>% summarise(mean(AREA))
nc.g %>% summarise(mean(AREA)) %>% plot(col = grey(3:6 / 7))
nc %>% as.data.frame %>% summarise(mean(AREA))
nc[c(1:100, 1:10), ] %>% distinct() %>% nrow()
library(tidyr)
nc %>% select(SID74, SID79) %>% gather("VAR", "SID", -geometry) %>% summary()
library(tidyr)
nc$row = 1:100 # needed for spread to work
nc %>% select(SID74, SID79, geometry, row) %>%
	gather("VAR", "SID", -geometry, -row) %>%
	spread(VAR, SID) %>% head()
storms.sf = st_as_sf(storms, coords = c("long", "lat"), crs = 4326)
x <- storms.sf %>% group_by(name, year) %>% nest
trs = lapply(x$data, function(tr) st_cast(st_combine(tr), "LINESTRING")[[1]]) %>%
    st_sfc(crs = 4326)
trs.sf = st_sf(x[,1:2], trs)
plot(trs.sf["year"], axes = TRUE)

sf

Simple Features for R

v0.9-8

GPL-2 | MIT + file LICENSE

Authors

Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Roger Bivand [ctb] (<https://orcid.org/0000-0003-2392-6140>), Etienne Racine [ctb], Michael Sumner [ctb], Ian Cook [ctb], Tim Keitt [ctb], Robin Lovelace [ctb], Hadley Wickham [ctb], Jeroen Ooms [ctb] (<https://orcid.org/0000-0002-4035-0289>), Kirill Müller [ctb], Thomas Lin Pedersen [ctb], Dan Baston [ctb]

Initial release