sf (version 0.5-5)

dplyr: Dplyr verb methods for sf objects

Description

Dplyr verb methods for sf objects. Geometries are sticky, use as.data.frame to let dplyr's own methods drop them.

Usage

filter.sf(.data, ..., .dots)
arrange.sf(.data, ..., .dots)
distinct.sf(.data, ..., .dots, .keep_all = FALSE)
group_by.sf(.data, ..., .dots, add = FALSE)
ungroup.sf(x, ...)
mutate.sf(.data, ..., .dots)
transmute.sf(.data, ..., .dots)
select.sf(.data, ...)
rename.sf(.data, ..., .dots)
slice.sf(.data, ..., .dots)
summarise.sf(.data, ..., .dots, do_union = TRUE)
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, ..., .key = "data")
separate.sf(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE,
  convert = FALSE, extra = "warn", fill = "warn", ...)
unite.sf(data, col, ..., sep = "_", remove = TRUE)
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, ...)
anti_join.sf(x, y, by = NULL, copy = FALSE, ...)

Arguments

.data

data object of class sf

...

other arguments

.dots

see corresponding function in package dplyr

.keep_all

see corresponding function in dplyr

add

see corresponding function in dplyr

x

see left_join

do_union

logical; should geometries be unioned by using st_union, or simply be combined using st_combine? Using st_union resolves internal boundaries, but in case of unioning points may also change the order of the points.

data

see original function docs

key

see original function docs

value

see original function docs

na.rm

see original function docs

convert

see original function docs

factor_key

see original function docs

fill

see original function docs

drop

see original function docs

sep

see original function docs

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

.key

see nest

col

see separate

into

see separate

remove

see separate

extra

see separate

y

see left_join

by

see left_join

copy

see left_join

suffix

see left_join

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.

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

Examples

# NOT RUN {
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[c(1:100, 1:10), ] %>% distinct() %>% nrow()
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))
library(tidyr)
nc %>% select(SID74, SID79, geometry) %>% 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)
# }