将（x，y）坐标映射到R中集合的最近点答案

【问题标题】：Mapping (x, y) coordinates to nearest point of a set in R将（x，y）坐标映射到R中集合的最近点
【发布时间】：2017-11-19 22:31:17
【问题描述】：

我正在构建一个闪亮的应用程序，我有一行代码目前让我慢了很多。我有以下数据框，具有 1008 个唯一 (x,y) 坐标（对于大型复制和粘贴表示歉意，尽管我认为共享整个数据框很有帮助）：

dput(rounded_coords)
structure(list(xspots = c(1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 
14.5, 16, 17.5, 19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 
34, 35.5, 37, 38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 
4.75, 6.25, 7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 
19.75, 21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 
33.25, 34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 
46.75, 48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 
17.5, 19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 
37, 38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 
6.25, 7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49, 1.75, 3.25, 4.75, 6.25, 
7.75, 9.25, 10.75, 12.25, 13.75, 15.25, 16.75, 18.25, 19.75, 
21.25, 22.75, 24.25, 25.75, 27.25, 28.75, 30.25, 31.75, 33.25, 
34.75, 36.25, 37.75, 39.25, 40.75, 42.25, 43.75, 45.25, 46.75, 
48.25, 1, 2.5, 4, 5.5, 7, 8.5, 10, 11.5, 13, 14.5, 16, 17.5, 
19, 20.5, 22, 23.5, 25, 26.5, 28, 29.5, 31, 32.5, 34, 35.5, 37, 
38.5, 40, 41.5, 43, 44.5, 46, 47.5, 49), yspots = c(1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 
2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 
5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8.5, 8.5, 8.5, 8.5, 8.5, 
8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 
8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 
8.5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 
11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 
11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 11.5, 
11.5, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 
13, 13, 13, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 
14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 
14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 
14.5, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
16, 16, 16, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 
17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 
17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 
17.5, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
19, 19, 19, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 
20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 
20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 20.5, 
20.5, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 
22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 
22, 22, 22, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 
23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 
23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 
23.5, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
25, 25, 25, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 
26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 
26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 
26.5, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
28, 28, 28, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 
29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 
29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 
29.5, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
31, 31, 31, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 
32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 
32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 32.5, 
32.5, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 
34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 
34, 34, 34, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 
35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 
35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 35.5, 
35.5, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 
37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 
37, 37, 37, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 
38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 
38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 38.5, 
38.5, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 
40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 
40, 40, 40, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 
41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 
41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 41.5, 
41.5, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 
43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 
43, 43, 43, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 
44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 
44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 44.5, 
44.5, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 
46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 
46, 46, 46)), .Names = c("xspots", "yspots"), row.names = c(NA, 
-1008L), class = "data.frame")

我还有第二个数据框，unrounded_coords，我想将 unrounded_coords 中的值四舍五入到 rounded_coords 中最接近的值.我的数据框 unrounded_coords 的一个子集在下面共享：

dput(unrounded_coords[1:100, ])
structure(list(xmapped = c(24.9, 23.2, 3.6, 11.9, 26, 29.7, 46.7, 
13.3, 23.9, 24.5, 24.1, 34.6, 24.3, 39.8, 35.9, 25.2, 19.7, 36.7, 
3.2, 24.5, 34.2, 36.2, 24.4, 10.5, 31.5, 25.8, 31.4, 18, 25.3, 
25, 25.9, 24.5, 24.8, 8.6, 5.8, 23.4, 48.2, 38.5, 24, 9, 30.4, 
8.2, 32, 22.3, 24.9, 41.9, 28.1, 26.3, 34.8, 19.8, 2.2, 25.8, 
14.3, 25.3, 6.3, 25.3, 23.8, 5.1, 6.7, 24.8, 24.2, 25.6, 47.2, 
25.3, 25, 39.1, 48.9, 23.9, 40, 24.1, 12.4, 12.5, 5.4, 32, 14.3, 
26.4, 41.2, 32.7, 25.2, 23.7, 38.1, 25.6, 31.9, 43.3, 22.2, 27.9, 
34.6, 41.6, 36.8, 26.3, 25.9, 25, 24.4, 24.4, 27, 29.1, 24.8, 
4, 18, 27.4), ymapped = c(31.9, 41.5, 28.1, 33.6, 39.6, 22.9, 
36.3, 38.4, 41.3, 40.8, 32.8, 17.4, 38.8, 40.4, 29.8, 40.3, 16.1, 
19.7, 27.8, 40.6, 27.6, 26.9, 37.5, 25.7, 27.7, 41.3, 18.2, 17.6, 
39.7, 42.5, 40.8, 40.9, 41.8, 21.7, 32.7, 40.2, 39.2, 19.8, 41.9, 
21.9, 36.9, 39.8, 35.6, 31.4, 33.4, 35, 17.1, 40.2, 30.4, 29.1, 
42, 41.8, 18.6, 41.1, 23.6, 41, 39.7, 25.4, 22, 41.4, 40.4, 40.7, 
42.2, 41.1, 42.5, 30.4, 35.8, 40.4, 19.4, 38.4, 19.9, 38.1, 19.2, 
35.1, 17.7, 41.6, 34.6, 17.1, 40.3, 40.5, 30.2, 24.5, 27.3, 38.7, 
32.9, 18.5, 26.2, 33.6, 39, 39.8, 40.6, 30.6, 33.6, 39.8, 28.9, 
17.5, 41.1, 25.7, 40.2, 39.9)), .Names = c("xmapped", "ymapped"
), row.names = c(NA, 100L), class = "data.frame")

目前我使用以下代码将 unrounded_coords 中的值映射到 rounded_coords 中最接近的值：

output.df <- apply(unrounded_coords, 1, FUN = function(x) {
    zed <- sqrt((x[1] - rounded_coords$xspots)^2 + (x[2] - rounded_coords$yspots)^2)
    return(c(rounded_coords$xspots[which.min(zed)], rounded_coords$yspots[which.min(zed)]))
  }) %>%
    t() %>%
    as.data.frame() %>%
    setNames(c('xrounded', 'yrounded'))

不幸的是，我的整个数据帧上的这个函数运行时间太长（我在一个闪亮的应用程序中使用它，这个函数必须经常运行）。非常感谢任何关于如何加快速度的想法。

编辑 - 代码需要很长时间的原因是，对于 unrounded_coords 中的每个点（我的数据框有 40,000 行，并且此数据框每天都在增长），它需要计算与 rounded_coords 中约 1,000 个点中的每个点的距离。那是 40,000,000 次计算，我不知道如何降低这个数字。

EDIT2 - unrounded_coords 数据框中的 xmapped 和 ymaped 列名可能具有误导性。这些显然是未映射的值，列名可能应该是 xraw、yraw 或类似的名称。

【问题讨论】：

您的数据是否实时流式传输到应用程序？它存储在数据库中吗？鉴于您希望应用程序执行大量计算，并且正如您所说的那样不断增长，您确实需要卸载该工作（例如 SQL），然后直接提取计算值
使用 API 拉入 R。为此，将需要留在 R 内。
没有理由不能让进程从 API 中提取数据并独立于闪亮的应用程序进行处理，尝试内联执行所有操作将导致用户体验不合格
@Canovic 然后制作一个加载屏幕，或者，也许看看使用多核版本的代码来加速它
github.com/daattali/advanced-shiny#loading-screen

标签： r data-manipulation

【解决方案1】：

不确定这是否会更快，但您可以试一试。

library(spatstat)

# this calculates a distance matrix
# each value in rounded values has every distance to a point in unrounded as a row
distances <- crossdist(X = rounded_coords[, 1], 
                      Y = rounded_coords[, 2],
                      x2 = unrounded_coords[, 1],
                      y2 = unrounded_coords[, 2])

# if each column is an unrounded, you want the column min position
min_locations <- apply(distances, 2, which.min)

# then subset rounded_coords to get the coordinate that are closest
mapped_coords <- rounded_coords[min_locations, ]
names(mapped_coords) <- c('xrounded', 'yrounded')

【讨论】：

比 OPs 代码更简洁，但是，我同意它不太可能提供在 Shiny 中提供体面的用户体验所需的性能改进
是的，它的运行速度与我的代码大致相同，不过感谢分享，绝对干净。