sf (version 0.4-2)

dplyr: Dplyr verb methods for sf objects

Description

Dplyr verb methods for sf objects

Usage

filter_.sf(.data, ..., .dots)
filter.sf(.data, ...)
arrange_.sf(.data, ..., .dots)
arrange.sf(.data, ...)
distinct_.sf(.data, ..., .dots, .keep_all = FALSE)
distinct.sf(.data, ..., .dots, .keep_all = FALSE)
group_by_.sf(.data, ..., .dots, add = FALSE)
group_by.sf(.data, ..., .dots, add = FALSE)
mutate_.sf(.data, ..., .dots)
mutate.sf(.data, ..., .dots)
transmute_.sf(.data, ..., .dots)
transmute.sf(.data, ..., .dots)
select_.sf(.data, ..., .dots = NULL)
select.sf(.data, ...)
rename_.sf(.data, ..., .dots)
rename.sf(.data, ...)
slice_.sf(.data, ..., .dots)
slice.sf(.data, ...)
summarise.sf(.data, ..., .dots, do_union = TRUE)
summarise_.sf(.data, ..., .dots, do_union = TRUE)
gather_.sf(data, key_col, value_col, gather_cols, na.rm = FALSE,
  convert = FALSE, factor_key = FALSE)
spread_.sf(data, key_col, value_col, 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_col, nest_cols)
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

do_union

logical; should geometries be unioned, using st_union, or simply be combined using st_combine?

data

see original function docs

key_col

see original function docs

value_col

see original function docs

gather_cols

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

nest_cols

see nest

x

see left_join

y

see left_join

by

see left_join

copy

see left_join

suffix

see left_join

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[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()