sf (version 0.3-4)

dplyr: Dplyr verb methods for sf objects

Description

Dplyr verb methods for sf objects

Usage

filter_.sf(.data, ..., .dots)
arrange_.sf(.data, ..., .dots)
distinct_.sf(.data, ..., .dots, .keep_all = FALSE)
group_by_.sf(.data, ..., .dots, add = FALSE)
mutate_.sf(.data, ..., .dots)
transmute_.sf(.data, ..., .dots)
select_.sf(.data, ..., .dots = NULL)
rename_.sf(.data, ..., .dots)
slice_.sf(.data, ..., .dots)
summarise_.sf(.data, ..., .dots)
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)
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

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

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 %>% summarize(mean(AREA))
nc.g %>% summarize(mean(AREA)) %>% plot(col = grey(3:6 / 7))
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()