Question

我正在尝试做的任务非常简单，使用R中的sp包，但我正在尝试学习sf因此我的问题。我正试图在R中创建一个点的形状。我有很多点，所以它必须是高效的。我在sp和sf都成功了，但是sf方法很慢。作为sf的新手，我有一种感觉，我不是最有效的方式。

我做了3个不同的功能，它们做同样的事情：

1）100％sp

f_rgdal <- function(dat) {
  coordinates(dat) <- ~x+y
}

2）100％sf（可能不好......）

f_sf <- function(dat) {
  dat <- st_sfc(
    lapply(
      apply(dat[,c("x", "y")], 1, list), function(xx) st_point(xx[[1]])
      )
    )
}

3）两者兼而有之：

f_rgdal_sp <- function(dat) {
  coordinates(dat) <- ~x+y
  dat <- as(dat, "sf")
}

如果我对它们进行基准测试，你会发现函数2和3都比函数1慢：

set.seed(1234)
dd <- data.frame(x = runif(nb_pt, 0, 100),
                 y = runif(nb_pt, 0,50),
                 f1 = rnorm(nb_pt))

library(sp)
library(sf)
library(rbenchmark)
benchmark(f_rgdal(dd), f_sf(dd), f_rgdal_sp(dd), columns = c("test", "elapsed"))
            test elapsed
1    f_rgdal(dd)    0.22
3 f_rgdal_sp(dd)    4.82
2       f_sf(dd)    4.08

他们是加快sf加速的方法吗？最后，我想使用比st_write更快的writeOGR，因此留在sp并不理想。

Answer 1

更“紧凑”的替代方案可以是：

library(sf)
set.seed(1234)
nb_pt <- 10000
dd <- data.frame(x = runif(nb_pt, 0, 100),
                 y = runif(nb_pt, 0,50),
                 f1 = rnorm(nb_pt))

sf <- sf::st_as_sf(dd, coords = c("x","y"))
sf

#> Simple feature collection with 10000 features and 1 field
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 0.03418126 ymin: 0.02131674 xmax: 99.95938 ymax: 49.99873
#> epsg (SRID):    NA
#> proj4string:    NA
#> First 10 features:
#>             f1                       geometry
#> 1  -1.81689753 POINT (11.3703411305323 10....
#> 2   0.62716684 POINT (62.2299404814839 24....
#> 3   0.51809210 POINT (60.9274732880294 31....
#> 4   0.14092183 POINT (62.3379441676661 47....
#> 5   1.45727195 POINT (86.0915383556858 8.9...
#> 6  -0.49359652 POINT (64.0310605289415 14....
#> 7  -2.12224406 POINT (0.94957563560456 19....
#> 8  -0.13356660 POINT (23.2550506014377 3.8...
#> 9  -0.42760035 POINT (66.6083758231252 14....
#> 10  0.08779481 POINT (51.4251141343266 23....

它具有保留数据属性的优势，对于较大的数据集看起来更快：

library(microbenchmark)

microbenchmark::microbenchmark(
  st_cast = st_cast(st_sfc(st_multipoint(as.matrix(dd[,1:2]))), "POINT"),
  st_asf = sf::st_as_sf(dd, coords = c("x","y"))
)
#> Unit: milliseconds
#>     expr      min       lq     mean   median       uq      max neval
#>  st_cast 208.6751 256.8995 294.2232 284.2213 316.1777 454.6856   100
#>   st_asf 157.1974 176.6357 207.9863 200.1610 226.1047 323.5700   100

Answer 2

library(sf)
library(microbenchmark)

set.seed(1234)

nb_pt <- 100

dd <- data.frame(x = runif(nb_pt, 0, 100),
                 y = runif(nb_pt, 0,50),
                 f1 = rnorm(nb_pt))

print(st_cast(st_sfc(st_multipoint(as.matrix(dd[,1:2]))), "POINT"))
## Geometry set for 100 features 
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 0.9495756 ymin: 1.110341 xmax: 99.21504 ymax: 49.93704
## epsg (SRID):    NA
## proj4string:    NA
## First 5 geometries:
## POINT (11.3703411305323 1.77283635130152)
## POINT (62.2299404814839 28.253805602435)
## POINT (60.9274732880294 14.0128888073377)
## POINT (62.3379441676661 10.2098158211447)
## POINT (86.0915383556858 6.68694493360817)

microbenchmark(
  sf=st_cast(st_sfc(st_multipoint(as.matrix(dd[,1:2]))), "POINT")
)
## Unit: milliseconds
##  expr      min       lq     mean median       uq      max neval
##    sf 1.834133 1.960914 2.608143 2.0314 2.280842 39.04158   100

使用sf以快速方式创建SpatialPointsDataFrame

2 个答案: