r - 计算大型数据集 R 的托管索引

Question

我有一个复杂的多部分问题。如果我没有说清楚，我很抱歉。我也是一个相当新手的 R 用户，如果这看起来很简陋，请原谅我。我想计算鲸鱼潜水数据和猎物分布数据的托管指数。这需要：

通过从猎物（鱼和动物）数据中潜水到深度箱中计算鲸鱼潜水深度数据的频率分布。
对于每次潜水，计算重心 (CG) 和惯性 (I)。
对于每次潜水，计算一个全球托管指数 (GIC) 与每种猎物类型。

我希望能够编写一个函数（或一系列函数），这样我就不必通过潜水来分离我的数据并手动为每次潜水重新运行函数。

鲸鱼数据示例，如果潜水次数（有时 40 次以上潜水），潜水等于深度，分类与潜水类型有关。[IMG] http://i41.tinypic.com/33vc5rs.jpg[/IMG]

深度箱来自包含猎物信息的单独数据集：

在此处输入图像描述

我有以下代码可用于整个潜水数据，但需要编写一个循环或包含一个应用函数，以便我可以为单个文件中包含的每次潜水的数据运行此代码。所以，对于 40 次潜水的鲸鱼，我需要 40 次鲸鱼频率、40 次鲸鱼 CG、40 次鲸鱼 Is 等。每次潜水的猎物分布都是相同的！最终，我想要一个包含增量 GIC 值列表的表。

#bin whale dive depths
dive.cut=cut(whale,c(0 ,depths), right=FALSE) 
dive.freq=table(dive.cut) 

# compute CG 
fish.CG=sum(depths*fish)/sum(fish)
whale.CG=sum(depths*whale.freq)/sum(whale.freq)
zoop.CG=sum(depths*zoop)/sum(zoop)

# compute Inertia 
fish.I=sum((depths-fish.CG)^2*fish)/sum(fish)
whale.I=sum((depths-whale.CG)^2*whale.freq)/sum(whale.freq)
zoop.I=sum((depths-zoop.CG)^2*zoop)/sum(zoop)

#compute GIC as per 
# compute delta CG
deltaCG.fish_whale=fish.CG-whale.CG
GIC.fish_whale= 1-((deltaCG.fish_whale)^2/((deltaCG.fish_whale)^2+fish.I+whale.I))
deltaCG.zoop_whale=zoop.CG-whale.CG
GIC.zoop_whale= 1-((deltaCG.zoop_whale)^2/((deltaCG.zoop_whale)^2+zoop.I+whale.I))

更新我已经粘贴了猎物和鲸鱼潜水的示例数据。

猎物数据

 depths        fish       zoop
1      5     0.00000    0.000000
2     10     0.00000    0.000000
3     15     0.00000    0.000000
4     20    21.24194    0.000000
5     25   149.51694   14.937945
6     30   170.43214    0.000000
7     35   296.93453    0.737109
8     40    16.61643    4.295556
9     45    92.68130   26.384844
10    50    50.68548   55.902301
11    55    37.47343  218.673781
12    60    32.74443  204.452678
13    65    20.62983  113.112452
14    70    13.75121   83.014457
15    75    16.15562   55.051358
16    80    22.65562   96.746271
17    85    42.99768  302.229135
18    90 16315.65099  783.868978
19    95 43006.20482 1713.133161
20   100 23476.24740 3440.034642
21   105 30513.66346 6667.914707
22   110 17411.64500 9398.790964
23   115 12127.70195 7580.233165
24   120  4526.63393 7205.768739
25   125  3328.89644 6567.175766
26   130  1864.21486 4567.446886
27   135  2202.07464 4295.772442
28   140  2719.29417 4419.903403
29   145  1710.75599 5102.689940
30   150  2033.69552 4496.121974
31   155  2796.81788 3269.193606
32   160   967.09406 2310.203528
33   165   437.30896  447.940140
34   170   193.15526   63.731336
35   175   143.88043   38.004799
36   180   406.31373   22.565211
37   185   786.30087   31.889927
38   190  1643.52542   36.580063
39   195  1665.69794   14.084152
40   200  1281.15790    0.000000
41   205   753.75309   35.343794
42   210   252.48867    0.000000

鲸鱼数据：

  Number Dive Class
1       1 95.1     F
2       1 95.9     F
3       1 95.1     F
4       1 95.9     F
5       1 96.8     F
6       1 97.2     F
7       1 96.8     F
8       2 95.5     N
9       2 94.2     N
10      3 94.7     F
11      3 94.2     F
12      3 94.2     F
13      3 95.9     F
14      3 95.9     F
15      4 93.8     F
16      4 97.7     F
17      4 99.4     F
18      4 94.7     F
19      4 92.5     F
20      4 98.1     F
21      5 97.2     N
22      5 98.5     N
23      5 95.5     N
24      5 97.2     N
25      5 98.5     N
26      5 96.4     N
27      5 94.7     N
28      5 95.5     N

score 1 · Accepted Answer

试试这个代码。我根据您发布的数据对其进行了测试。我使用了猎物数据框的深度。不确定这是否是您想要做的。而且，这一次我猜你使用了whale$Dive 作为你的dive.freq。如果没有，你将不得不改变它。（注意，这个问题也被交叉发布到 r-help 列表中。）

prey <- structure(list(depths = c(5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 
    45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
    105L, 110L, 115L, 120L, 125L, 130L, 135L, 140L, 145L, 150L, 155L, 
    160L, 165L, 170L, 175L, 180L, 185L, 190L, 195L, 200L, 205L, 210L
    ), fish = c(0, 0, 0, 21.24194, 149.51694, 170.43214, 296.93453, 
    16.61643, 92.6813, 50.68548, 37.47343, 32.74443, 20.62983, 13.75121, 
    16.15562, 22.65562, 42.99768, 16315.65099, 43006.20482, 23476.2474, 
    30513.66346, 17411.645, 12127.70195, 4526.63393, 3328.89644, 
    1864.21486, 2202.07464, 2719.29417, 1710.75599, 2033.69552, 2796.81788, 
    967.09406, 437.30896, 193.15526, 143.88043, 406.31373, 786.30087, 
    1643.52542, 1665.69794, 1281.1579, 753.75309, 252.48867), zoop = c(0, 
    0, 0, 0, 14.937945, 0, 0.737109, 4.295556, 26.384844, 55.902301, 
    218.673781, 204.452678, 113.112452, 83.014457, 55.051358, 96.746271, 
    302.229135, 783.868978, 1713.133161, 3440.034642, 6667.914707, 
    9398.790964, 7580.233165, 7205.768739, 6567.175766, 4567.446886, 
    4295.772442, 4419.903403, 5102.68994, 4496.121974, 3269.193606, 
    2310.203528, 447.94014, 63.731336, 38.004799, 22.565211, 31.889927, 
    36.580063, 14.084152, 0, 35.343794, 0)), .Names = c("depths", 
    "fish", "zoop"), class = "data.frame", row.names = c("1", "2", 
    "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", 
    "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", 
    "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", 
    "37", "38", "39", "40", "41", "42"))

whale <- structure(list(Number = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
    3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L), Dive = c(95.1, 95.9, 95.1, 95.9, 96.8, 97.2, 96.8, 
    95.5, 94.2, 94.7, 94.2, 94.2, 95.9, 95.9, 93.8, 97.7, 99.4, 94.7, 
    92.5, 98.1, 97.2, 98.5, 95.5, 97.2, 98.5, 96.4, 94.7, 95.5), 
    Class = c("F", "F", "F", "F", "F", "F", "F", "N", "N", "F", 
    "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "N", "N", 
    "N", "N", "N", "N", "N", "N")), .Names = c("Number", "Dive", 
    "Class"), class = "data.frame", row.names = c("1", "2", "3", 
    "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", 
    "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", 
    "27", "28"))

# split the data frame into a list with a different element for each dive
dives <- split(whale, whale$Dive)

# define a single function that does all of your computations
compute <- function(whale, depths, fish, zoop) {
    # you don't say what part of the whale data you are counting ... I'll assume it's the dive
    dive.freq <- table(cut(whale$Dive, c(0, depths)))
    #compute Center of Gravity
    fish.CG <- sum(depths*fish)/sum(fish) #calculate CG for fish distribution ONCE for each whale
    zoop.CG <- sum(depths*zoop)/sum(zoop) #calculate CG for zoop distribution ONCE for each whale
    whale.CG <- sum(depths*dive.freq/sum(dive.freq)) #calculate for EACH dive
    #compute Inertia
    fish.I <- sum((depths-fish.CG)^2*fish)/sum(fish) 
    zoop.I <- sum((depths-zoop.CG)^2*zoop)/sum(zoop)
    whale.I <- sum((depths-whale.CG)^2*dive.freq)/sum(dive.freq) #needs to be calculated for EACH dive
    # compute delta CG
    deltaCG.fish_whale <- fish.CG-whale.CG
    GIC.fish_whale <- 1-((deltaCG.fish_whale)^2/((deltaCG.fish_whale)^2+fish.I+whale.I))
    deltaCG.zoop_whale <- zoop.CG-whale.CG
    GIC.zoop_whale <- 1-((deltaCG.zoop_whale)^2/((deltaCG.zoop_whale)^2+zoop.I+whale.I))
    # then list off all the variables you want to keep as output from the function here
    c(fish.CG=fish.CG, whale.CG=whale.CG, zoop.CG=zoop.CG, fish.I=fish.I, whale.I=whale.I, zoop.I=zoop.I, 
        GIC.fish_whale=GIC.fish_whale, GIC.zoop_whale=GIC.zoop_whale)
    }

# apply the compute function to each element of the dives list
t(sapply(dives, function(dat) compute(whale=dat, depths=prey$depths, fish=prey$fish, zoop=prey$zoop)))

r - 计算大型数据集 R 的托管索引

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