在下面的数据中(使用
我想计算每个个体的连续点之间的欧几里得距离。我的初步想法是在
dput
包含),我有三个人(IndIDII
)的重复观测值(纬度和经度)。请注意,每个人的位置数量不同。> Dat
IndIDII IndYear WintLat WintLong
1 BHS_265 BHS_265-2015 47.61025 -112.7210
2 BHS_265 BHS_265-2016 47.59884 -112.7089
3 BHS_770 BHS_770-2016 42.97379 -109.0400
4 BHS_770 BHS_770-2017 42.97129 -109.0367
5 BHS_770 BHS_770-2018 42.97244 -109.0509
6 BHS_377 BHS_377-2015 43.34744 -109.4821
7 BHS_377 BHS_377-2016 43.35559 -109.4445
8 BHS_377 BHS_377-2017 43.35195 -109.4566
9 BHS_377 BHS_377-2018 43.34765 -109.4892
我想计算每个个体的连续点之间的欧几里得距离。我的初步想法是在
dplyr
内使用lead()
,如下所示。 distm
函数需要一个矩阵作为参数,但我无法在dplyr
内创建这个矩阵。是否可能生成并使用矩阵作为distm
的参数?Dat %>%
group_by(IndIDII) %>%
mutate(WitnGeoDist = distm(as.matrix(c("WintLong", "WintLat")), lead(as.matrix(c("WintLong", "WintLat"))), fun = distVincentyEllipsoid))
或者,还有其他可能性吗?非常感谢您的帮助。
数据:
Dat <- structure(list(IndIDII = c("BHS_265", "BHS_265", "BHS_770", "BHS_770",
"BHS_770", "BHS_377", "BHS_377", "BHS_377", "BHS_377"), IndYear = c("BHS_265-2015",
"BHS_265-2016", "BHS_770-2016", "BHS_770-2017", "BHS_770-2018",
"BHS_377-2015", "BHS_377-2016", "BHS_377-2017", "BHS_377-2018"
), WintLat = c(47.6102519805014, 47.5988417247191, 42.9737859090909,
42.9712914772727, 42.9724390816327, 43.3474354347826, 43.3555934579439,
43.3519543396226, 43.3476466990291), WintLong = c(-112.720994832869,
-112.708887595506, -109.039964727273, -109.036693522727, -109.050923061224,
-109.482114456522, -109.444522149533, -109.45659254717, -109.489241553398
)), class = "data.frame", row.names = c(NA, -9L))
sqrt( (y2 - y1)^2 + (x2 - x1)^2 )
,无需使用distm
。 - SymbolixAU