Sf: How to re-establish sfc-class after it has been removed?
I discovered that nested sf objects don't survive the round-trip conversion when the sfc is included in the nesting:
st_read(system.file("shape/nc.shp", package = "sf")) %>%
mutate(ROW = row_number()) %>%
nest(-ROW)
## # A tibble: 100 x 2
## ROW data
## <int> <list>
## 1 1 <sf [1 x 15]>
## 2 2 <sf [1 x 15]>
## 3 3 <sf [1 x 15]>
## 4 4 <sf [1 x 15]>
## 5 5 <sf [1 x 15]>
## 6 6 <sf [1 x 15]>
## 7 7 <sf [1 x 15]>
## 8 8 <sf [1 x 15]>
## 9 9 <sf [1 x 15]>
## 10 10 <sf [1 x 15]>
## # ... with 90 more rows
``` r
st_read(system.file("shape/nc.shp", package = "sf")) %>%
mutate(ROW = row_number()) %>%
nest(-ROW) %>%
unnest()
Warning in bind_rows_(x, .id): Vectorizing 'sfc_MULTIPOLYGON' elements may
not preserve their attributes
There were 50 or more warnings (use warnings() to see the first 50)
# A tibble: 100 x 16
ROW AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID
1 1 0.114 1.442 1825 1825 Ashe 37009 37009 5
2 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3
3 3 0.143 1.630 1828 1828 Surry 37171 37171 86
4 4 0.070 2.968 1831 1831 Currituck 37053 37053 27
5 5 0.153 2.206 1832 1832 Northampton 37131 37131 66
6 6 0.097 1.670 1833 1833 Hertford 37091 37091 46
7 7 0.062 1.547 1834 1834 Camden 37029 37029 15
8 8 0.091 1.284 1835 1835 Gates 37073 37073 37
9 9 0.118 1.421 1836 1836 Warren 37185 37185 93
10 10 0.124 1.428 1837 1837 Stokes 37169 37169 85
# ... with 90 more rows, and 7 more variables: BIR74 , SID74 ,
# NWBIR74 , BIR79 , SID79 , NWBIR79 ,
# geometry
That's unfortunate but it seems to be a result of a limitation in `nest`: it calls `bind_rows_()` which doesn't preserve the `sfc` column attribute (see the reprex below).
**Question:** Is there a way to re-establish `class = sfc` for a column that has been stripped of its attributes?
Naively I thought `unnest(nc_nested) %>% mutate(geometry = map(geometry, st_multipolygon)` would do it, but it doesn't seem to work.
<details><summary>Reprex + Session info</summary>
``` r
library(tidyverse)
library(sf)
## Linking to GEOS 3.6.1, GDAL 2.2.0, proj.4 4.9.3
nc <- st_read(system.file("shape/nc.shp", package = "sf"))
nc_nested <-
nc %>%
mutate(ROW = row_number()) %>%
nest(-ROW)
print(nc_nested)
## # A tibble: 100 x 2
## ROW data
## <int> <list>
## 1 1 <sf [1 x 15]>
## 2 2 <sf [1 x 15]>
## 3 3 <sf [1 x 15]>
## 4 4 <sf [1 x 15]>
## 5 5 <sf [1 x 15]>
## 6 6 <sf [1 x 15]>
## 7 7 <sf [1 x 15]>
## 8 8 <sf [1 x 15]>
## 9 9 <sf [1 x 15]>
## 10 10 <sf [1 x 15]>
## # ... with 90 more rows
# Attempts to unnest
unnest(nc_nested)
## Warning in bind_rows_(x, .id): Vectorizing 'sfc_MULTIPOLYGON' elements may
## not preserve their attributes
## There were 50 or more warnings (use warnings() to see the first 50)
## # A tibble: 100 x 16
## ROW AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID
## <int> <dbl> <dbl> <dbl> <dbl> <fctr> <fctr> <dbl> <int>
## 1 1 0.114 1.442 1825 1825 Ashe 37009 37009 5
## 2 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3
## 3 3 0.143 1.630 1828 1828 Surry 37171 37171 86
## 4 4 0.070 2.968 1831 1831 Currituck 37053 37053 27
## 5 5 0.153 2.206 1832 1832 Northampton 37131 37131 66
## 6 6 0.097 1.670 1833 1833 Hertford 37091 37091 46
## 7 7 0.062 1.547 1834 1834 Camden 37029 37029 15
## 8 8 0.091 1.284 1835 1835 Gates 37073 37073 37
## 9 9 0.118 1.421 1836 1836 Warren 37185 37185 93
## 10 10 0.124 1.428 1837 1837 Stokes 37169 37169 85
## # ... with 90 more rows, and 7 more variables: BIR74 <dbl>, SID74 <dbl>,
## # NWBIR74 <dbl>, BIR79 <dbl>, SID79 <dbl>, NWBIR79 <dbl>,
## # geometry <list>
unnest(nc_nested) %>%
mutate(geometry = map(geometry, st_multipolygon)) %>%
st_sf()
## Warning in bind_rows_(x, .id): Vectorizing 'sfc_MULTIPOLYGON' elements may
## not preserve their attributes
## There were 50 or more warnings (use warnings() to see the first 50)
## Error in st_sf(.): no simple features geometry column present
devtools::session_info()
## Session info -------------------------------------------------------------
## setting value
## version R version 3.4.0 (2017-04-21)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.1252
## tz America/Los_Angeles
## date 2017-12-05
## Packages -----------------------------------------------------------------
## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.2)
## backports 1.1.0 2017-05-22 CRAN (R 3.4.0)
## base * 3.4.0 2017-04-21 local
## bindr 0.1 2016-11-13 CRAN (R 3.4.2)
## bindrcpp * 0.2 2017-06-17 CRAN (R 3.4.2)
## broom 0.4.2 2017-02-13 CRAN (R 3.4.0)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.4.2)
## class 7.3-14 2015-08-30 CRAN (R 3.4.0)
## classInt 0.1-24 2017-04-16 CRAN (R 3.4.2)
## cli 1.0.0 2017-11-05 CRAN (R 3.4.2)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.2)
## compiler 3.4.0 2017-04-21 local
## crayon 1.3.4 2017-10-30 Github (r-lib/crayon@b5221ab)
## datasets * 3.4.0 2017-04-21 local
## DBI 0.7 2017-06-18 CRAN (R 3.4.2)
## devtools 1.13.2 2017-06-02 CRAN (R 3.4.0)
## digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
## dplyr * 0.7.4 2017-09-28 CRAN (R 3.4.2)
## e1071 1.6-8 2017-02-02 CRAN (R 3.4.2)
## evaluate 0.10 2016-10-11 CRAN (R 3.4.0)
## forcats * 0.2.0 2017-01-23 CRAN (R 3.4.2)
## foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
## ggplot2 * 2.2.1.9000 2017-12-02 Github (tidyverse/ggplot2@7b5c185)
## glue 1.2.0.9000 2017-12-05 Github (tidyverse/glue@69bc72c)
## graphics * 3.4.0 2017-04-21 local
## grDevices * 3.4.0 2017-04-21 local
## grid 3.4.0 2017-04-21 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.2)
## haven 1.1.0 2017-07-09 CRAN (R 3.4.2)
## hms 0.3 2016-11-22 CRAN (R 3.4.2)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.0)
## httr 1.3.1 2017-08-20 CRAN (R 3.4.2)
## jsonlite 1.5 2017-06-01 CRAN (R 3.4.0)
## knitr 1.16 2017-05-18 CRAN (R 3.4.0)
## lattice 0.20-35 2017-03-25 CRAN (R 3.4.0)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.2)
## lubridate 1.7.1 2017-11-03 CRAN (R 3.4.2)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.0)
## methods * 3.4.0 2017-04-21 local
## mnormt 1.5-5 2016-10-15 CRAN (R 3.4.1)
## modelr 0.1.1 2017-07-24 CRAN (R 3.4.2)
## munsell 0.4.3 2016-02-13 CRAN (R 3.4.2)
## nlme 3.1-131 2017-02-06 CRAN (R 3.4.0)
## parallel 3.4.0 2017-04-21 local
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.2)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.2)
## psych 1.7.8 2017-09-09 CRAN (R 3.4.2)
## purrr * 0.2.4.9000 2017-12-05 Github (tidyverse/purrr@62b135a)
## R6 2.2.2 2017-06-17 CRAN (R 3.4.0)
## Rcpp 0.12.14 2017-11-23 CRAN (R 3.4.2)
## readr * 1.1.1 2017-05-16 CRAN (R 3.4.2)
## readxl 1.0.0 2017-04-18 CRAN (R 3.4.2)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.4.2)
## rlang 0.1.4 2017-11-05 CRAN (R 3.4.2)
## rmarkdown 1.8 2017-11-17 CRAN (R 3.4.2)
## rprojroot 1.2 2017-01-16 CRAN (R 3.4.0)
## rvest 0.3.2 2016-06-17 CRAN (R 3.4.2)
## scales 0.5.0.9000 2017-12-02 Github (hadley/scales@d767915)
## sf * 0.5-6 2017-12-04 Github (r-spatial/sf@315890c)
## stats * 3.4.0 2017-04-21 local
## stringi 1.1.6 2017-11-17 CRAN (R 3.4.2)
## stringr * 1.2.0 2017-02-18 CRAN (R 3.4.0)
## tibble * 1.3.4 2017-08-22 CRAN (R 3.4.2)
## tidyr * 0.7.2.9000 2017-12-05 Github (tidyverse/tidyr@efd9ea5)
## tidyselect 0.2.3 2017-11-06 CRAN (R 3.4.3)
## tidyverse * 1.2.1 2017-11-14 CRAN (R 3.4.2)
## tools 3.4.0 2017-04-21 local
## udunits2 0.13 2016-11-17 CRAN (R 3.4.1)
## units 0.4-6 2017-08-27 CRAN (R 3.4.2)
## utils * 3.4.0 2017-04-21 local
## withr 2.1.0.9000 2017-12-02 Github (jimhester/withr@fe81c00)
## xml2 1.1.1 2017-01-24 CRAN (R 3.4.2)
## yaml 2.1.14 2016-11-12 CRAN (R 3.4.0)
tiernanmartin
All 3 comments
I'm still unclear about how to re-establish the sfc class after the attributes are dropped, but I have figured out a way to use cbind to perform the nesting round-trip (not pretty):
library(tidyverse)
library(sf)
## Linking to GEOS 3.6.1, GDAL 2.2.0, proj.4 4.9.3
nc_nested <-
st_read(system.file("shape/nc.shp", package = "sf")) %>%
mutate(ROW = row_number()) %>%
{cbind(
data = st_set_geometry(.,NULL) %>% as_tibble %>% nest(-ROW) %>% select(data),
geom = st_geometry(.))
} %>%
as_tibble() %>%
st_as_sf()
nc_nested
## Simple feature collection with 100 features and 1 field
## 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: 100 x 2
## data geometry
## <list> <simple_feature>
## 1 <tibble [1 x 14]> <MULTIPOLYGON...>
## 2 <tibble [1 x 14]> <MULTIPOLYGON...>
## 3 <tibble [1 x 14]> <MULTIPOLYGON...>
## 4 <tibble [1 x 14]> <MULTIPOLYGON...>
## 5 <tibble [1 x 14]> <MULTIPOLYGON...>
## 6 <tibble [1 x 14]> <MULTIPOLYGON...>
## 7 <tibble [1 x 14]> <MULTIPOLYGON...>
## 8 <tibble [1 x 14]> <MULTIPOLYGON...>
## 9 <tibble [1 x 14]> <MULTIPOLYGON...>
## 10 <tibble [1 x 14]> <MULTIPOLYGON...>
## # ... with 90 more rows
#Unnest
unnest(nc_nested)
## 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
## # A tibble: 100 x 15
## AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74
## <dbl> <dbl> <dbl> <dbl> <fctr> <fctr> <dbl> <int> <dbl>
## 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091
## 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487
## 3 0.143 1.630 1828 1828 Surry 37171 37171 86 3188
## 4 0.070 2.968 1831 1831 Currituck 37053 37053 27 508
## 5 0.153 2.206 1832 1832 Northampton 37131 37131 66 1421
## 6 0.097 1.670 1833 1833 Hertford 37091 37091 46 1452
## 7 0.062 1.547 1834 1834 Camden 37029 37029 15 286
## 8 0.091 1.284 1835 1835 Gates 37073 37073 37 420
## 9 0.118 1.421 1836 1836 Warren 37185 37185 93 968
## 10 0.124 1.428 1837 1837 Stokes 37169 37169 85 1612
## # ... with 90 more rows, and 6 more variables: SID74 <dbl>, NWBIR74 <dbl>,
## # BIR79 <dbl>, SID79 <dbl>, NWBIR79 <dbl>, geometry <simple_feature>
Session info
devtools::session_info()
## Session info -------------------------------------------------------------
## setting value
## version R version 3.4.0 (2017-04-21)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.1252
## tz America/Los_Angeles
## date 2017-12-05
## Packages -----------------------------------------------------------------
## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.2)
## backports 1.1.0 2017-05-22 CRAN (R 3.4.0)
## base * 3.4.0 2017-04-21 local
## bindr 0.1 2016-11-13 CRAN (R 3.4.2)
## bindrcpp * 0.2 2017-06-17 CRAN (R 3.4.2)
## broom 0.4.2 2017-02-13 CRAN (R 3.4.0)
## cellranger 1.1.0 2016-07-27 CRAN (R 3.4.2)
## class 7.3-14 2015-08-30 CRAN (R 3.4.0)
## classInt 0.1-24 2017-04-16 CRAN (R 3.4.2)
## cli 1.0.0 2017-11-05 CRAN (R 3.4.2)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.2)
## compiler 3.4.0 2017-04-21 local
## crayon 1.3.4 2017-10-30 Github (r-lib/crayon@b5221ab)
## datasets * 3.4.0 2017-04-21 local
## DBI 0.7 2017-06-18 CRAN (R 3.4.2)
## devtools 1.13.2 2017-06-02 CRAN (R 3.4.0)
## digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
## dplyr * 0.7.4 2017-09-28 CRAN (R 3.4.2)
## e1071 1.6-8 2017-02-02 CRAN (R 3.4.2)
## evaluate 0.10 2016-10-11 CRAN (R 3.4.0)
## forcats * 0.2.0 2017-01-23 CRAN (R 3.4.2)
## foreign 0.8-67 2016-09-13 CRAN (R 3.4.0)
## ggplot2 * 2.2.1.9000 2017-12-02 Github (tidyverse/ggplot2@7b5c185)
## glue 1.2.0.9000 2017-12-05 Github (tidyverse/glue@69bc72c)
## graphics * 3.4.0 2017-04-21 local
## grDevices * 3.4.0 2017-04-21 local
## grid 3.4.0 2017-04-21 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.2)
## haven 1.1.0 2017-07-09 CRAN (R 3.4.2)
## hms 0.3 2016-11-22 CRAN (R 3.4.2)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.0)
## httr 1.3.1 2017-08-20 CRAN (R 3.4.2)
## jsonlite 1.5 2017-06-01 CRAN (R 3.4.0)
## knitr 1.16 2017-05-18 CRAN (R 3.4.0)
## lattice 0.20-35 2017-03-25 CRAN (R 3.4.0)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.2)
## lubridate 1.7.1 2017-11-03 CRAN (R 3.4.2)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.0)
## methods * 3.4.0 2017-04-21 local
## mnormt 1.5-5 2016-10-15 CRAN (R 3.4.1)
## modelr 0.1.1 2017-07-24 CRAN (R 3.4.2)
## munsell 0.4.3 2016-02-13 CRAN (R 3.4.2)
## nlme 3.1-131 2017-02-06 CRAN (R 3.4.0)
## parallel 3.4.0 2017-04-21 local
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.2)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.2)
## psych 1.7.8 2017-09-09 CRAN (R 3.4.2)
## purrr * 0.2.4.9000 2017-12-05 Github (tidyverse/purrr@62b135a)
## R6 2.2.2 2017-06-17 CRAN (R 3.4.0)
## Rcpp 0.12.14 2017-11-23 CRAN (R 3.4.2)
## readr * 1.1.1 2017-05-16 CRAN (R 3.4.2)
## readxl 1.0.0 2017-04-18 CRAN (R 3.4.2)
## reshape2 1.4.2 2016-10-22 CRAN (R 3.4.2)
## rlang 0.1.4 2017-11-05 CRAN (R 3.4.2)
## rmarkdown 1.8 2017-11-17 CRAN (R 3.4.2)
## rprojroot 1.2 2017-01-16 CRAN (R 3.4.0)
## rvest 0.3.2 2016-06-17 CRAN (R 3.4.2)
## scales 0.5.0.9000 2017-12-02 Github (hadley/scales@d767915)
## sf * 0.5-6 2017-12-04 Github (r-spatial/sf@315890c)
## stats * 3.4.0 2017-04-21 local
## stringi 1.1.6 2017-11-17 CRAN (R 3.4.2)
## stringr * 1.2.0 2017-02-18 CRAN (R 3.4.0)
## tibble * 1.3.4 2017-08-22 CRAN (R 3.4.2)
## tidyr * 0.7.2.9000 2017-12-05 Github (tidyverse/tidyr@efd9ea5)
## tidyselect 0.2.3 2017-11-06 CRAN (R 3.4.3)
## tidyverse * 1.2.1 2017-11-14 CRAN (R 3.4.2)
## tools 3.4.0 2017-04-21 local
## udunits2 0.13 2016-11-17 CRAN (R 3.4.1)
## units 0.4-6 2017-08-27 CRAN (R 3.4.2)
## utils * 3.4.0 2017-04-21 local
## withr 2.1.0.9000 2017-12-02 Github (jimhester/withr@fe81c00)
## xml2 1.1.1 2017-01-24 CRAN (R 3.4.2)
## yaml 2.1.14 2016-11-12 CRAN (R 3.4.0)
Does
x <- read_sf(system.file("shape/nc.shp", package = "sf")) %>%
mutate(ROW = row_number()) %>%
nest(-ROW)
unnest(x) %>% mutate(geometry = st_sfc(geometry)) %>% st_sf
help?
edzer
on 6 Dec 2017
👍2
That's helpful and _pretty close_ to the steps I'd expect.
I want to be able to access the sfc within the nested tibble object - just like I would any other column. Your suggestion pointed me in the right direction - this works the way I expected it to:
st_read(system.file("shape/nc.shp", package = "sf")) %>%
mutate(ROW = row_number()) %>%
nest(-ROW) %>%
mutate(NAME = map_chr(data, "NAME"),
geometry = map(data, "geometry") %>% purrr::flatten %>% st_sfc) %>%
st_sf
flatten() removes a single layer of hierarchy from data$geometry and st_sfc() re-establishes the class.
tiernanmartin
on 6 Dec 2017
👍1
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