从一个 data.frame 在另一个 data.frame 中找到最近的点

Question

我有两个data.tables。它们包含某些点的 x 和 y 坐标。它们如下：-

set.seed(123)

a <- data.table(x = runif(10), y = runif(10))

a
            x           y
 1: 0.9036218 0.005233401
 2: 0.1914482 0.219560561
 3: 0.6089018 0.275966545
 4: 0.2307832 0.665939467
 5: 0.5959250 0.221657568
 6: 0.2343375 0.228499024
 7: 0.2620843 0.846292513
 8: 0.8499895 0.888969644
 9: 0.5707878 0.305334950
10: 0.4771339 0.714933915

b <- data.table(x = runif(30), y = runif(30))

b

             x          y
  1: 0.86444548 0.26182116
  2: 0.54160133 0.14384453
  3: 0.84231617 0.78842707
  4: 0.26452802 0.56285334
  5: 0.31283576 0.63412792
  6: 0.85437689 0.91124597
  7: 0.71066455 0.94311426
  8: 0.28285278 0.36521536
  9: 0.28785318 0.95902709
 10: 0.86013854 0.09544158
 11: 0.64630209 0.49041844
 12: 0.54615120 0.35911319
 13: 0.15450425 0.13447951
 14: 0.49790235 0.17264283
 15: 0.08562707 0.74520138
 16: 0.93701477 0.16472997
 17: 0.62143442 0.36910724
 18: 0.30887985 0.75412718
 19: 0.64023414 0.14568618
 20: 0.81968754 0.34259618
 21: 0.77749145 0.20355856
 22: 0.55769767 0.44691877
 23: 0.39907966 0.26292129
 24: 0.07789015 0.96241503
 25: 0.13784013 0.33544759
 26: 0.99523977 0.38647609
 27: 0.49555325 0.97433126
 28: 0.34634466 0.99967023
 29: 0.08722789 0.56224314
 30: 0.27777771 0.30246561

对于a 中的每个点，我想在b 中找到离它们最近的点。即，a 中的每个点都将有一个离 b 最近的点。并且最近点的坐标应该用a连接。

我怎样才能做到这一点？

提前致谢

Answer 1

你可以试试下面的代码

> library(data.table)

> a[, c(paste0("nearest.", c("x", "y"))) := b[max.col(-abs(outer(.SD[, x + 1i * y], b[, x + 1i * y], `-`)))]][]
            x          y  nearest.x  nearest.y
 1: 0.2875775 0.95683335 0.28915974 0.89504536
 2: 0.7883051 0.45333416 0.69280341 0.44220007
 3: 0.4089769 0.67757064 0.41372433 0.71018240
 4: 0.8830174 0.57263340 0.96302423 0.66511519
 5: 0.9404673 0.10292468 0.90229905 0.09484066
 6: 0.0455565 0.89982497 0.02461368 0.81464004
 7: 0.5281055 0.24608773 0.54406602 0.12753165
 8: 0.8924190 0.04205953 0.88953932 0.04583117
 9: 0.5514350 0.32792072 0.47779597 0.44851634
10: 0.4566147 0.95450365 0.28915974 0.89504536

Answer 2

您可以使用 FNN 包中的 get.knnx 函数：

set.seed(123)
library(data.table)
a <- data.table(x = runif(10), y = runif(10))
b <- data.table(x = runif(30), y = runif(30))

library(FNN)
c<-get.knnx(b,a,k=1)

a$b.x <- b$x[c$nn.index]
a$b.y <- b$y[c$nn.index]

a

            x          y        b.x        b.y
 1: 0.2875775 0.95683335 0.28915974 0.89504536
 2: 0.7883051 0.45333416 0.69280341 0.44220007
 3: 0.4089769 0.67757064 0.41372433 0.71018240
 4: 0.8830174 0.57263340 0.96302423 0.66511519
 5: 0.9404673 0.10292468 0.90229905 0.09484066
 6: 0.0455565 0.89982497 0.02461368 0.81464004
 7: 0.5281055 0.24608773 0.54406602 0.12753165
 8: 0.8924190 0.04205953 0.88953932 0.04583117
 9: 0.5514350 0.32792072 0.47779597 0.44851634
10: 0.4566147 0.95450365 0.28915974 0.89504536

Answer 3

这是一种使用raster::pointDistance()的方法

set.seed(123)
dt1 <- data.table(x = runif(10), y = runif(10))
dt2 <- data.table(x = runif(30), y = runif(30))

dt1[, nearest_dt2 := apply(raster::pointDistance(as.matrix(dt1), 
                                                 as.matrix(dt2), 
                                                 lonlat = FALSE), 1, 
                           which.min)][]


#            x          y nearest_dt2
# 1: 0.2875775 0.95683335           9
# 2: 0.7883051 0.45333416           2
# 3: 0.4089769 0.67757064          23
# 4: 0.8830174 0.57263340          11
# 5: 0.9404673 0.10292468          12
# 6: 0.0455565 0.89982497          15
# 7: 0.5281055 0.24608773           7
# 8: 0.8924190 0.04205953           1
# 9: 0.5514350 0.32792072          16
#10: 0.4566147 0.95450365           9

Answer 4

另一种可能的解决方案，基于rdist::cdist：

library(data.table)
library(rdist)

set.seed(123)
a <- data.table(x = runif(10), y = runif(10))
b <- data.table(x = runif(30), y = runif(30))

cbind(a, b[apply(cdist(a, b), 1, which.min),])

#>             x          y          x          y
#>  1: 0.2875775 0.95683335 0.28915974 0.89504536
#>  2: 0.7883051 0.45333416 0.69280341 0.44220007
#>  3: 0.4089769 0.67757064 0.41372433 0.71018240
#>  4: 0.8830174 0.57263340 0.96302423 0.66511519
#>  5: 0.9404673 0.10292468 0.90229905 0.09484066
#>  6: 0.0455565 0.89982497 0.02461368 0.81464004
#>  7: 0.5281055 0.24608773 0.54406602 0.12753165
#>  8: 0.8924190 0.04205953 0.88953932 0.04583117
#>  9: 0.5514350 0.32792072 0.47779597 0.44851634
#> 10: 0.4566147 0.95450365 0.28915974 0.89504536