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).

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

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

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

ungroup.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)

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

tbls to join

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.

.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

tbls to join

by

a character vector of variables to join by. If NULL, the default, *_join() will do a natural join, using all variables with common names across the two tables. A message lists the variables so that you can check they're right (to suppress the message, simply explicitly list the variables that you want to join).

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.

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.

Value

an object of class sf

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.

Examples

library(dplyr) nc = st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source `/tmp/RtmpnRHNNJ/temp_libpath1381650468c/sf/shape/nc.shp' using driver `ESRI Shapefile' #> Simple feature collection with 100 features and 14 fields #> geometry type: MULTIPOLYGON #> dimension: XY #> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> epsg (SRID): 4267 #> proj4string: +proj=longlat +datum=NAD27 +no_defs
nc %>% filter(AREA > .1) %>% plot()
#> Warning: plotting the first 10 out of 14 attributes; use max.plot = 14 to plot all
# 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()
#> [1] "sf" "grouped_df" "tbl_df" "tbl" "data.frame"
nc2 <- nc %>% mutate(area10 = AREA/10) nc %>% transmute(AREA = AREA/10, geometry = geometry) %>% class()
#> [1] "sf" "data.frame"
nc %>% transmute(AREA = AREA/10) %>% class()
#> [1] "sf" "data.frame"
nc %>% select(SID74, SID79) %>% names()
#> [1] "SID74" "SID79" "geometry"
nc %>% select(SID74, SID79, geometry) %>% names()
#> [1] "SID74" "SID79" "geometry"
nc %>% select(SID74, SID79) %>% class()
#> [1] "sf" "data.frame"
nc %>% select(SID74, SID79, geometry) %>% class()
#> [1] "sf" "data.frame"
nc2 <- nc %>% rename(area = AREA) nc %>% slice(1:2)
#> Simple feature collection with 2 features and 15 fields #> geometry type: MULTIPOLYGON #> dimension: XY #> bbox: xmin: -81.74107 ymin: 36.23436 xmax: -80.90344 ymax: 36.58965 #> epsg (SRID): 4267 #> proj4string: +proj=longlat +datum=NAD27 +no_defs #> AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74 #> 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091 1 #> 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487 0 #> NWBIR74 BIR79 SID79 NWBIR79 area_cl geometry #> 1 10 1364 0 19 (0.1,0.12] MULTIPOLYGON (((-81.47276 3... #> 2 10 542 3 12 (0,0.1] MULTIPOLYGON (((-81.23989 3...
nc$area_cl = cut(nc$AREA, c(0, .1, .12, .15, .25)) nc.g <- nc %>% group_by(area_cl) nc.g %>% summarise(mean(AREA))
#> Simple feature collection with 4 features and 2 fields #> geometry type: MULTIPOLYGON #> dimension: XY #> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> epsg (SRID): 4267 #> proj4string: +proj=longlat +datum=NAD27 +no_defs #> # A tibble: 4 x 3 #> area_cl `mean(AREA)` geometry #> * <fct> <dbl> <MULTIPOLYGON [°]> #> 1 (0,0.1] 0.0760 (((-77.96073 34.18924, -77.96587 34.24229, -77.97528 … #> 2 (0.1,0.12] 0.112 (((-84.29104 35.21054, -84.22594 35.2616, -84.17973 3… #> 3 (0.12,0.1… 0.134 (((-76.54427 34.58783, -76.55515 34.61066, -76.53775 … #> 4 (0.15,0.2… 0.190 (((-76.64705 34.90633, -76.62562 34.89065, -76.75021 …
nc.g %>% summarise(mean(AREA)) %>% plot(col = grey(3:6 / 7))
nc %>% as.data.frame %>% summarise(mean(AREA))
#> mean(AREA) #> 1 0.12626
nc[c(1:100, 1:10), ] %>% distinct() %>% nrow()
#> [1] 100
library(tidyr) nc %>% select(SID74, SID79) %>% gather("VAR", "SID", -geometry) %>% summary()
#> VAR SID geometry #> Length:200 Min. : 0.000 MULTIPOLYGON :200 #> Class :character 1st Qu.: 2.000 epsg:4267 : 0 #> Mode :character Median : 5.000 +proj=long...: 0 #> Mean : 7.515 #> 3rd Qu.: 9.000 #> Max. :57.000
library(tidyr) nc$row = 1:100 # needed for spread to work nc %>% select(SID74, SID79, geometry, row) %>% gather("VAR", "SID", -geometry, -row) %>% spread(VAR, SID) %>% head()
#> Simple feature collection with 6 features and 3 fields #> geometry type: MULTIPOLYGON #> dimension: XY #> bbox: xmin: -81.74107 ymin: 36.07282 xmax: -75.77316 ymax: 36.58965 #> epsg (SRID): 4267 #> proj4string: +proj=longlat +datum=NAD27 +no_defs #> row SID74 SID79 geometry #> 1 1 1 0 MULTIPOLYGON (((-81.47276 3... #> 2 2 0 3 MULTIPOLYGON (((-81.23989 3... #> 3 3 5 6 MULTIPOLYGON (((-80.45634 3... #> 4 4 1 2 MULTIPOLYGON (((-76.00897 3... #> 5 5 9 3 MULTIPOLYGON (((-77.21767 3... #> 6 6 7 5 MULTIPOLYGON (((-76.74506 3...
storms.sf = st_as_sf(storms, coords = c("long", "lat"), crs = 4326) x <- storms.sf %>% group_by(name, year) %>% nest trs = lapply(x$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)