计算回路中不同行程之间的距离

Question

我有一个数据框称为p.1，其中有16列信息，用于Argos系统记录的每个点，这是重要的列，日期，lon（纵向），lat（纬度），旅程是旅行的次数和到海岸的距离。

date    | lon  | lat  | trip |  distancetocoast 
2014-06-11 19:02:00 -58,3508585  -51,88438373 1  2236,067977 
2014-06-12 01:02:00 -58,35589725 -51,88349529 1  1000 
2014-06-12 13:02:00 -58,27224941 -51,91903677 1  7211,102551 
2014-06-12 19:02:00 -58,27974654 -51,90535003 1  5830,951895 
2014-06-13 01:02:00 -58,32331901 -51,89410464 1  3605,551275 
2014-06-13 07:02:00 -58,35833139 -51,88809227 1  1414,213562 
2014-06-13 13:02:00 -58,35617673 -51,88156281 1  1000 
2014-06-13 19:02:00 -58,34055711 -51,89002367 1  2236,067977 
2014-06-14 01:02:00 -58,34982536 -51,8715761  2  1000 
2014-06-14 13:02:00 -58,3073814  -51,92722937 2  7071,067812 
2014-06-14 19:02:00 -58,34581314 -51,86761133 3  1000 
2014-06-15 01:02:00 -58,34050624 -51,88382088 3  1414,213562 
2014-06-15 13:02:00 -58,2974691  -51,91795326 3  6324,55532 
2014-06-15 19:02:00 -58,19881901 -51,95172233 3  13000 
2014-06-16 01:02:00 -58,1348416  -51,98673766 3  18788,29423 
2014-06-16 07:02:00 -57,99399544 -52,06988191 3  28861,73938 
2014-06-16 13:02:00 -58,00469754 -52,02795069 3  26627,05391 
2014-06-16 19:02:00 -57,92758675 -52,02184666 3  29000 
2014-06-17 01:02:00 -57,91658235 -51,99748699 3  28284,27125 
2014-06-17 07:02:00 -57,77015528 -51,99031797 3  30805,8436 
2014-06-17 13:02:00 -57,99601712 -51,91519551 3  17804,49381 
2014-06-17 19:02:00 -58,06820013 -51,92972737 3  14866,06875 
2014-06-18 01:02:00 -58,19845185 -51,89522513 3  7615,773106 
2014-06-18 07:02:00 -58,35241361 -51,88015998 3  1000 
2014-06-18 13:02:00 -58,35603546 -51,88336878 3  1000 
2014-06-18 19:02:00 -58,33350332 -51,87308427 3  1000 
2014-06-19 01:02:00 -58,33839581 -51,87846631 3  1414,213562 
2014-06-19 07:02:00 -58,42661519 -51,80902388 4  0 
2014-06-19 13:02:00 -58,30461883 -51,93745837 4  7810,249676 
2014-06-19 19:02:00 -58,18362875 -51,96475914 4  14317,82106 

在beggining我只是SUMED从下一个功能每趟海岸的距离：

tripID<-c(1:length(unique(p.1$trip))) 
tripdistance<-aggregate(p.1$dist,by=list(unique.values=p.1$trip),FUN=sum) 

这显然是错误的，我怎么能计算每个行程的行驶距离，并从开始0在下一次旅行？

我一直在尝试其他功能：spDistsN1和trackDistance，但我不能让它工作。

Answer 1

添加当前行lat long和next row lat long之间的距离。把它放在if条件来检查旅行号码。最后总结一下。

首先在excel中执行此操作，然后转换为您的编码语言。

Answer 2

您可能会发现dplyr提供您需要的工具来执行此操作。首先，如果您计划在SO上发布更多问题，使用dput以便于其他人以reproduce的方式提供您的数据集的副本会很有帮助。在这个例子中，很容易通过readr来读取你的数据集。

library(readr) 
library(dplyr) 
library(trip) 

p.1 <- 
    readr::read_delim(delim = "|", 
        locale = locale(decimal_mark = ",", grouping_mark = "."), 
        trim_ws = TRUE, 
        file = 
"date    | lon   | lat   | trip | distancetocoast 
2014-06-11 19:02:00 | -58,3508585 | -51,88438373 | 1 | 2236,067977 
2014-06-12 01:02:00 | -58,35589725 | -51,88349529 | 1 | 1000 
2014-06-12 13:02:00 | -58,27224941 | -51,91903677 | 1 | 7211,102551 
2014-06-12 19:02:00 | -58,27974654 | -51,90535003 | 1 | 5830,951895 
2014-06-13 01:02:00 | -58,32331901 | -51,89410464 | 1 | 3605,551275 
2014-06-13 07:02:00 | -58,35833139 | -51,88809227 | 1 | 1414,213562 
2014-06-13 13:02:00 | -58,35617673 | -51,88156281 | 1 | 1000 
2014-06-13 19:02:00 | -58,34055711 | -51,89002367 | 1 | 2236,067977 
2014-06-14 01:02:00 | -58,34982536 | -51,8715761 | 2 | 1000 
2014-06-14 13:02:00 | -58,3073814 | -51,92722937 | 2 | 7071,067812 
2014-06-14 19:02:00 | -58,34581314 | -51,86761133 | 3 | 1000 
2014-06-15 01:02:00 | -58,34050624 | -51,88382088 | 3 | 1414,213562 
2014-06-15 13:02:00 | -58,2974691 | -51,91795326 | 3 | 6324,55532 
2014-06-15 19:02:00 | -58,19881901 | -51,95172233 | 3 | 13000 
2014-06-16 01:02:00 | -58,1348416 | -51,98673766 | 3 | 18788,29423 
2014-06-16 07:02:00 | -57,99399544 | -52,06988191 | 3 | 28861,73938 
2014-06-16 13:02:00 | -58,00469754 | -52,02795069 | 3 | 26627,05391 
2014-06-16 19:02:00 | -57,92758675 | -52,02184666 | 3 | 29000 
2014-06-17 01:02:00 | -57,91658235 | -51,99748699 | 3 | 28284,27125 
2014-06-17 07:02:00 | -57,77015528 | -51,99031797 | 3 | 30805,8436 
2014-06-17 13:02:00 | -57,99601712 | -51,91519551 | 3 | 17804,49381 
2014-06-17 19:02:00 | -58,06820013 | -51,92972737 | 3 | 14866,06875 
2014-06-18 01:02:00 | -58,19845185 | -51,89522513 | 3 | 7615,773106 
2014-06-18 07:02:00 | -58,35241361 | -51,88015998 | 3 | 1000 
2014-06-18 13:02:00 | -58,35603546 | -51,88336878 | 3 | 1000 
2014-06-18 19:02:00 | -58,33350332 | -51,87308427 | 3 | 1000 
2014-06-19 01:02:00 | -58,33839581 | -51,87846631 | 3 | 1414,213562 
2014-06-19 07:02:00 | -58,42661519 | -51,80902388 | 4 | 0 
2014-06-19 13:02:00 | -58,30461883 | -51,93745837 | 4 | 7810,249676 
2014-06-19 19:02:00 | -58,18362875 | -51,96475914 | 4 | 14317,82106") 

dplyr提供了一个group_by功能，会做什么这听起来像，创建一个或多个列中的价值观定义的组。 mutate命令创建数据集的列。

矢量distance_traveled已创建为每个trip的第一个条目从0开始，然后使用spDist来计算行进的距离。

还报告了每个trip的累计距离和总距离。如果您不熟悉%>%话务员，请阅读help("%>%", package = "magrittr")。

p.1 %>% 
group_by(trip) %>% 
arrange(date) %>% 
mutate(distance_traveled  = c(0, spDists(x = cbind(lon, lat), longlat = TRUE, segments = TRUE)), 
     cumlative_trip_distance = cumsum(distance_traveled), 
     total_trip_distance  = sum(distance_traveled)) %>% 
ungroup() %>% 
print.data.frame 

p.1 
#     date  lon  lat trip distancetocoast distance_traveled cumlative_trip_distance total_trip_distance 
# 1 2014-06-11 19:02:00 -58.35086 -51.88438 1  2236.068   0.0000000    0.0000000   16.875313 
# 2 2014-06-12 01:02:00 -58.35590 -51.88350 1  1000.000   0.3607526    0.3607526   16.875313 
# 3 2014-06-12 13:02:00 -58.27225 -51.91904 1  7211.103   6.9846980    7.3454506   16.875313 
# 4 2014-06-12 19:02:00 -58.27975 -51.90535 1  5830.952   1.6078870    8.9533376   16.875313 
# 5 2014-06-13 01:02:00 -58.32332 -51.89410 1  3605.551   3.2496828    12.2030205   16.875313 
# 6 2014-06-13 07:02:00 -58.35833 -51.88809 1  1414.214   2.5015138    14.7045343   16.875313 
# 7 2014-06-13 13:02:00 -58.35618 -51.88156 1  1000.000   0.7415006    15.4460348   16.875313 
# 8 2014-06-13 19:02:00 -58.34056 -51.89002 1  2236.068   1.4292778    16.8753126   16.875313 
# 9 2014-06-14 01:02:00 -58.34983 -51.87158 2  1000.000   0.0000000    0.0000000   6.846905 
# 10 2014-06-14 13:02:00 -58.30738 -51.92723 2  7071.068   6.8469049    6.8469049   6.846905 
# 11 2014-06-14 19:02:00 -58.34581 -51.86761 3  1000.000   0.0000000    0.0000000   103.020176 
# 12 2014-06-15 01:02:00 -58.34051 -51.88382 3  1414.214   1.8402281    1.8402281   103.020176 
# 13 2014-06-15 13:02:00 -58.29747 -51.91795 3  6324.555   4.8164202    6.6566483   103.020176 
# 14 2014-06-15 19:02:00 -58.19882 -51.95172 3  13000.000   7.7558235    14.4124718   103.020176 
# 15 2014-06-16 01:02:00 -58.13484 -51.98674 3  18788.294   5.8746536    20.2871254   103.020176 
# 16 2014-06-16 07:02:00 -57.99400 -52.06988 3  28861.739  13.3804466    33.6675720   103.020176 
# 17 2014-06-16 13:02:00 -58.00470 -52.02795 3  26627.054   4.7230685    38.3906405   103.020176 
# 18 2014-06-16 19:02:00 -57.92759 -52.02185 3  29000.000   5.3362603    43.7269008   103.020176 
# 19 2014-06-17 01:02:00 -57.91658 -51.99749 3  28284.271   2.8138115    46.5407123   103.020176 
# 20 2014-06-17 07:02:00 -57.77016 -51.99032 3  30805.844  10.0892378    56.6299501   103.020176 
# 21 2014-06-17 13:02:00 -57.99602 -51.91520 3  17804.494  17.6348017    74.2647518   103.020176 
# 22 2014-06-17 19:02:00 -58.06820 -51.92973 3  14866.069   5.2225348    79.4872866   103.020176 
# 23 2014-06-18 01:02:00 -58.19845 -51.89523 3  7615.773   9.7503783    89.2376649   103.020176 
# 24 2014-06-18 07:02:00 -58.35241 -51.88016 3  1000.000  10.7319100    99.9695749   103.020176 
# 25 2014-06-18 13:02:00 -58.35604 -51.88337 3  1000.000   0.4355097    100.4050846   103.020176 
# 26 2014-06-18 19:02:00 -58.33350 -51.87308 3  1000.000   1.9279737    102.3330583   103.020176 
# 27 2014-06-19 01:02:00 -58.33840 -51.87847 3  1414.214   0.6871181    103.0201763   103.020176 
# 28 2014-06-19 07:02:00 -58.42662 -51.80902 4   0.000   0.0000000    0.0000000   25.433053 
# 29 2014-06-19 13:02:00 -58.30462 -51.93746 4  7810.250  16.5773793    16.5773793   25.433053 
# 30 2014-06-19 19:02:00 -58.18363 -51.96476 4  14317.821   8.8556734    25.4330527   25.433053