我有两个数据表。第一个表是具有坐标和降水量的矩阵。它由四列组成，分别是纬度，经度，降水量和监测日期。该表的示例为：
latitude_1 longitude_1 precipitation day_mon
54.17 62.15 5 34
69.61 48.65 3 62
73.48 90.16 7 96
66.92 90.27 7 19
56.19 96.46 9 25
72.23 74.18 5 81
88.00 95.20 7 97
92.44 44.41 6 18
95.83 52.91 9 88
99.68 96.23 8 6
81.91 48.32 8 96
54.66 52.70 0 62
95.31 91.82 2 84
60.32 96.25 9 71
97.39 47.91 7 76
65.21 44.63 9 3
第二个表由5列组成：站号，经度，纬度，开始监视的日期，结束监视的日期。看起来像：
station latitude_2 longitude_2 day_begin day_end
15 50.00 93.22 34 46
11 86.58 85.29 15 47
14 93.17 63.17 31 97
10 88.56 61.28 15 78
13 45.29 77.10 24 79
6 69.73 99.52 13 73
4 45.60 77.36 28 95
13 92.88 62.38 9 51
1 65.10 64.13 7 69
10 60.57 86.77 34 64
3 53.62 60.76 23 96
16 87.82 59.41 38 47
1 47.83 95.89 21 52
11 75.42 46.20 38 87
3 55.71 55.26 2 73
16 71.65 96.15 36 93
我想对1个表的降水量求和。但是我有两个条件：
day_begin
从最接近的点开始的总降水量。监视点之间的距离（坐标包括
经度_1和纬度_1）和测站（坐标由
经度_2和纬度_2）应最小。距离由以下公式计算：
R = 6400*arccos(sin(latitude_1)*sin(latitude_2)+cos(latitude_1)*cos(latitude_2))*cos(longitude_1-longitude_2))
最后我想得到结果如表：
station latitude_2 longitude_2 day_begin day_end Sum
15 50 93.22 34 46 188
11 86.58 85.29 15 47 100
14 93.17 63.17 31 97 116
10 88.56 61.28 15 78 182
13 45.29 77.1 24 79 136
6 69.73 99.52 13 73 126
4 45.6 77.36 28 95 108
13 92.88 62.38 9 51 192
1 65.1 64.13 7 69 125
10 60.57 86.77 34 64 172
3 53.62 60.76 23 96 193
16 87.82 59.41 38 47 183
1 47.83 95.89 21 52 104
11 75.42 46.2 38 87 151
3 55.71 55.26 2 73 111
16 71.65 96.15 36 93 146
我知道如何用C ++计算它。我应该在R中使用什么功能？
谢谢您的帮助！
答案 0 :(得分：0)
我不确定我是否能正确解决您的问题...但是到了。
我使用了data.table方法。
library( tidyverse )
library( data.table )
#step 1. join days as periods
#create a dummy variables to create a virtual period in dt1
dt1[, point_id := .I]
dt1[, day_begin := day_mon]
dt1[, day_end := day_mon]
setkey(dt2, day_begin, day_end)
#overlap join finding all stations for each point that overlap periods
dt <- foverlaps( dt1, dt2, type = "within" )
#step 2. calculate the distance station for each point based on TS-privided formula
dt[, distance := 6400 * acos( sin( latitude_1 ) * sin( latitude_2 ) + cos( latitude_1 ) * cos( latitude_2 ) ) * cos( longitude_1 - longitude_2 ) ]
#step 3. filter (absolute) minimal distance based on point_id
dt[ , .SD[which.min( abs( distance ) )], by = point_id ]
# point_id station latitude_2 longitude_2 day_begin day_end latitude_1 longitude_1 precipitation day_mon i.day_begin i.day_end distance
# 1: 1 1 47.83 95.89 21 52 54.17 62.15 5 34 34 34 -248.72398
# 2: 2 6 69.73 99.52 13 73 69.61 48.65 3 62 62 62 631.89228
# 3: 3 14 93.17 63.17 31 97 73.48 90.16 7 96 96 96 -1519.84886
# 4: 4 11 86.58 85.29 15 47 66.92 90.27 7 19 19 19 1371.54757
# 5: 5 11 86.58 85.29 15 47 56.19 96.46 9 25 25 25 1139.46849
# 6: 6 14 93.17 63.17 31 97 72.23 74.18 5 81 81 81 192.99264
# 7: 7 14 93.17 63.17 31 97 88.00 95.20 7 97 97 97 5822.81529
# 8: 8 3 55.71 55.26 2 73 92.44 44.41 6 18 18 18 -899.71206
# 9: 9 3 53.62 60.76 23 96 95.83 52.91 9 88 88 88 45.16237
# 10: 10 3 55.71 55.26 2 73 99.68 96.23 8 6 6 6 -78.04484
# 11: 11 14 93.17 63.17 31 97 81.91 48.32 8 96 96 96 -5467.77459
# 12: 12 3 53.62 60.76 23 96 54.66 52.70 0 62 62 62 -1361.57863
# 13: 13 11 75.42 46.20 38 87 95.31 91.82 2 84 84 84 -445.18765
# 14: 14 14 93.17 63.17 31 97 60.32 96.25 9 71 71 71 -854.86321
# 15: 15 3 53.62 60.76 23 96 97.39 47.91 7 76 76 76 1304.41634
# 16: 16 3 55.71 55.26 2 73 65.21 44.63 9 3 3 3 -7015.57516
dt1 <- read.table( text = "latitude_1 longitude_1 precipitation day_mon
54.17 62.15 5 34
69.61 48.65 3 62
73.48 90.16 7 96
66.92 90.27 7 19
56.19 96.46 9 25
72.23 74.18 5 81
88.00 95.20 7 97
92.44 44.41 6 18
95.83 52.91 9 88
99.68 96.23 8 6
81.91 48.32 8 96
54.66 52.70 0 62
95.31 91.82 2 84
60.32 96.25 9 71
97.39 47.91 7 76
65.21 44.63 9 3", header = TRUE ) %>%
setDT()
dt2 <- read.table( text = "station latitude_2 longitude_2 day_begin day_end
15 50.00 93.22 34 46
11 86.58 85.29 15 47
14 93.17 63.17 31 97
10 88.56 61.28 15 78
13 45.29 77.10 24 79
6 69.73 99.52 13 73
4 45.60 77.36 28 95
13 92.88 62.38 9 51
1 65.10 64.13 7 69
10 60.57 86.77 34 64
3 53.62 60.76 23 96
16 87.82 59.41 38 47
1 47.83 95.89 21 52
11 75.42 46.20 38 87
3 55.71 55.26 2 73
16 71.65 96.15 36 93", header = TRUE ) %>%
setDT()