R，计算两个数据集的最小欧氏距离，并自动标记

Question

我正在使用 Euclidean Distance有一对数据集。首先是我的数据。

centers <- data.frame(x_ce = c(300,180,450,500),
                      y_ce = c(23,15,10,20),
                      center = c('a','b','c','d'))

points <- data.frame(point = c('p1','p2','p3','p4'),
                     x_p = c(160,600,400,245),
                     y_p = c(7,23,56,12))

我的目标是，对于points中的每个点，找到centers中所有中心的最小距离，并附加中心将名称命名为 points 数据集(显然是最小的)，并使此过程自动化。

所以我从基础开始:

#Euclidean distance
sqrt(sum((x-y)^2))

事实上，我已经想到了它应该如何工作，但我无法管理如何使其自动化。

1. 选择一行点，以及所有行的中心
2. 计算行与每行中心之间的欧氏距离
3. 选择最小的距离
4. 贴上距离最小的标签
5. 对第二行重复...直到 points
结束

所以我设法手动完成，让所有步骤自动完成:

# 1.  
x = (points[1,2:3])   # select the first of points
y1 = (centers[1,1:2]) # select the first center
y2 = (centers[2,1:2]) # select the second center
y3 = (centers[3,1:2]) # select the third center
y4 = (centers[4,1:2]) # select the fourth center

# 2.
# then the distances
distances <- data.frame(distance = c(
                                    sqrt(sum((x-y1)^2)),
                                    sqrt(sum((x-y2)^2)),
                                    sqrt(sum((x-y3)^2)),
                                    sqrt(sum((x-y4)^2))),
                                    center = centers$center
                                    )

# 3.
# then I choose the row with the smallest distance
d <- distances[which(distances$distance==min(distances$distance)),]

# 4.
# last, I put the label near the point
cbind(points[1,],d)

# 5. 
# then I restart for the second point

问题是我无法自动管理它。你有没有想过让这个过程对 points 的每个点自动进行？此外，我是否在重新发明轮子，即它是否存在我不知道的更快的过程(作为函数)？

Answer 1

centers <- data.frame(x_ce = c(300,180,450,500),
                      y_ce = c(23,15,10,20),
                      center = c('a','b','c','d'))

points <- data.frame(point = c('p1','p2','p3','p4'),
                     x_p = c(160,600,400,245),
                     y_p = c(7,23,56,12))

library(tidyverse)

points %>%
  mutate(c = list(centers)) %>%
  unnest() %>%                       # create all posible combinations of points and centers as a dataframe
  rowwise() %>%                      # for each combination
  mutate(d = sqrt(sum((c(x_p,y_p)-c(x_ce,y_ce))^2))) %>%   # calculate distance
  ungroup() %>%                                            # forget the grouping
  group_by(point, x_p, y_p) %>%                            # for each point
  summarise(closest_center = center[d == min(d)]) %>%      # keep the closest center
  ungroup()                                                # forget the grouping

# # A tibble: 4 x 4
#   point   x_p   y_p closest_center
#   <fct> <dbl> <dbl> <fct>         
# 1 p1      160     7 b             
# 2 p2      600    23 d             
# 3 p3      400    56 c             
# 4 p4      245    12 a