如何在r中按最接近的距离合并两个数据集? [英] How to merge two Dataset by closest distance in r?
问题描述
我有两个包含值和坐标的数据集A和B
I have two dataset A and B that contains values and coordonates
A:
╔═══╦════════════╦═════════════╦═════════════╗
║ ║ name ║ x ║ y ║
╠═══╬════════════╬═════════════╬═════════════╣
║ 1 ║ city ║ 50.3 ║ 4.2 ║
║ 2 ║ farm ║ 14.8 ║ 8.6 ║
║ 3 ║ lake ║ 18.7 ║ 9.8 ║
║ 3 ║ Mountain ║ 44 ║ 9.8 ║
╚═══╩════════════╩═════════════╩═════════════╝
B:
╔═══╦════════════╦═════════════╦═════════════╗
║ ║ Temp ║ x ║ y ║
╠═══╬════════════╬═════════════╬═════════════╣
║ 1 ║ 18 ║ 50.7 ║ 6.2 ║
║ 2 ║ 17,3 ║ 20 ║ 11 ║
║ 3 ║ 15 ║ 15 ║ 9 ║
╚═══╩════════════╩═════════════╩═════════════╝
我想要这个,C:
╔═══╦════════════╦═════════════╦═════════════╗
║ ║ Name ║ Temp ║ Distance ║
╠═══╬════════════╬═════════════╬═════════════╣
║ 1 ║ city ║ 18 ║ 2.039608 ║
║ 2 ║ farm ║ 15 ║ 0.447214 ║
║ 3 ║ lake ║ 17.3 ║ 1.769181 ║
║ 4 ║ Mountain ║ 18 ║ 7.605919 ║
╚═══╩════════════╩═════════════╩═════════════╝
我尝试过这个:
A<- read.table(header = TRUE, text = "
Name x y
city 50.3 4.2
farm 14.8 8.6
lake 18.7 9.8
mountain 44 9.8")
B<- read.table(header = TRUE, text = "
Temp x y
18 50.7 6.2
17.3 20 11
15 15 9")
C<- data.frame(Name=character(),
Temp=numeric(),
Distance=numeric())
for(i in 1:nrow(A)) {
x1<- A[i,]$x
y1<- A[i,]$y
min = 100
index = 0
for(j in 1:nrow(B)) {
x2<- B[j,]$x
y2<- B[j,]$y
tmp = sqrt((((x2-x1)^2)+((y2-y1)^2)))
if (tmp < min) {
index = j
min = tmp
}
}
df <- list(Name=A[i,]$Name, Temp=B[index,]$Temp, Distance=min)
C <- rbind(C, df)
}
print(C)
但是我的第一个数据集大约有1,500,000行,而我的第二个数据集大约有5000行,这种算法非常慢.有更好的方法吗?
But my first dataset is about 1,500,000 rows and my second one is about 5000 and this algorythm is very very slow. Is there a better way to do it ?
推荐答案
如果您想在R中进行 hack ,则可以使用R的outer
函数(并且意识到R擅长矢量化)可以有效地产生所有A[, c(x,y)]
中所有元素的A[, c(x,y)]
,即获取A
(行)中的位置与B
(列)中的每个位置(例如
If you want a hack in R, you can use R's outer
-function (and the awareness that R is good at vectorization) to efficiently produce the distances of all in A[, c(x,y)]
from all in B[, c(x,y)]
, that is, obtaining a Matrix of distances of the locations in A
(row) from each of the locations in B
(columns) e.g.,
A<- read.table(header = TRUE, text = "
Name x y
city 50.3 4.2
farm 14.8 8.6
lake 18.7 9.8
mountain 44 9.8")
B<- read.table(header = TRUE, text = "
Temp x y
18 50.7 6.2
17.3 20 11
15 15 9
18 ")
d <- sqrt(outer(A$x, B$x, "-")^2 + outer(A$y, B$y, "-")^2)
d
## [,1] [,2] [,3]
## [1,] 2.039608 31.053663 35.6248509
## [2,] 35.980133 5.727128 0.4472136
## [3,] 32.201863 1.769181 3.7854986
## [4,] 7.605919 24.029981 29.0110324
接下来,您可以有效地通过matrixStats包
Next you can efficiently obtain its value via the rowMins
-method in matrixStats package
minD <- matrixStats::rowMins(d)
并假设B
中有一个唯一的最近位置,可通过d
与minD
And assuming there is a unique closest location in B
obtain its index via (row-wise) comparison of d
to minD
ind <- (d == minD) %*% 1:ncol(d)
如果B
中有多个等距的位置,则无论如何您都需要某种选择规则.
最后,只需将数据堆叠在一起.
If there are multiple equaly distanced locations in B
you'll anyways need some kind of rule as to which to choose.
Last, just stack the data together.
C <- data.frame(Name = A$Name,
Temp = B$Temp[ind],
Distance = minD)
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