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

Source: R/tidyverse.R, R/join.R

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

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.
.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.

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/RtmpCdQsky/temp_libpath64f92385e079/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
#> CRS:            4267
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
#> CRS:            4267
#>    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
#> CRS:            4267
#> # 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$row = 1:100 # needed for spread to work
nc %>% select(SID74, SID79, geometry, row) %>%
        gather("VAR", "SID", -geometry, -row) %>%
        spread(VAR, SID) %>% head()
#> Simple feature collection with 6 features and 3 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -81.74107 ymin: 36.07282 xmax: -75.77316 ymax: 36.58965
#> CRS:            4267
#>   row SID74 SID79                       geometry
#> 1   1     1     0 MULTIPOLYGON (((-81.47276 3...
#> 2   2     0     3 MULTIPOLYGON (((-81.23989 3...
#> 3   3     5     6 MULTIPOLYGON (((-80.45634 3...
#> 4   4     1     2 MULTIPOLYGON (((-76.00897 3...
#> 5   5     9     3 MULTIPOLYGON (((-77.21767 3...
#> 6   6     7     5 MULTIPOLYGON (((-76.74506 3...
storms.sf = st_as_sf(storms, coords = c("long", "lat"), crs = 4326)
x <- storms.sf %>% group_by(name, year) %>% nest
trs = lapply(x$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)