我有一个样本数据集如下（Out）：
Out <- structure(list(Dist_Out = structure(c(223L, 224L, 195L, 195L,
195L, 235L, 299L, 64L, 336L, 28L, 191L, 129L, 63L, 303L, 249L,
194L, 222L, 177L, 199L, 309L), .Label = c("0", "0.110574578321468",
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"1.83497057428696", "3.07757525439021"), class = "factor"), Speed_Out = structure(c(2L,
8L, 8L, 4L, 15L, 15L, 6L, 15L, 17L, 8L, 8L, 10L, 23L, 25L, 17L,
11L, 8L, 8L, 9L, 8L), .Label = c("0", "0.03", "0.06", "0.09",
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"0.37", "0.375", "0.4", "0.405", "0.435", "0.465", "0.495", "0.5",
"0.525", "0.555", "0.62", "0.715", "0.745", "0.775", "0.965",
"1.085", "1.12"), class = "factor"), Acceleration_Out = structure(c(20L,
25L, 6L, 20L, 28L, 9L, 19L, 28L, 7L, 2L, 21L, 19L, 31L, 9L, 19L,
6L, 21L, 2L, 23L, 6L), .Label = c("-0.012", "-0.014", "-0.024",
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"0.1", "0.112", "0.124", "0.162", "0.222", "0.286"), class = "factor"),
Absolute_Heading_Out = structure(c(113L, 114L, 166L, 275L,
275L, 273L, 121L, 211L, 260L, 288L, 1L, 1L, 92L, 213L, 134L,
274L, 22L, 54L, 74L, 183L), .Label = c("0", "104.038094380956",
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74L, 210L, 326L, 166L, 121L, 10L, 162L, 212L), .Label = c("-1.08941367216175e-06",
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), class = "factor"), Movement_Out = structure(c(2L, 1L,
1L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 3L, 3L, 3L, 2L, 4L, 2L, 1L,
3L, 3L, 2L), .Label = c("forward", "left", "non-moving",
"right"), class = "factor"), Changes_Out = structure(c(1L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L), .Label = c("0", "1"), class = "factor"),
AccPosNeg_Out = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), AccChange_Out = structure(c(2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("0", "1"), class = "factor")), .Names = c("Dist_Out",
"Speed_Out", "Acceleration_Out", "Absolute_Heading_Out", "Relative_Heading_Out",
"Movement_Out", "Changes_Out", "AccPosNeg_Out", "AccChange_Out"
), row.names = c(NA, 20L), class = "data.frame")
以及以下功能：
JRip <- function (Dist_Out, Speed_Out, Acceleration_Out, Absolute_Heading_Out,
Relative_Heading_Out, Movement_Out, Changes_Out, AccPosNeg_Out,
AccChange_Out)
{
if (max(Speed_Out) >= 2.45) {
Behaviour <- "walking"
}
else if (mean(Acceleration_Out[Acceleration_Out > 0]) >=
0.03 & (max(Dist_Out[Movement_Out != "non-moving"]) <=
2.17 & (mean(Speed_Out) >= 0.23 & (sum(Dist_Out[Movement_Out !=
"non-moving"]) >= 11.92)))) {
Behaviour <- "grazing"
}
else Behaviour <- "resting"
return(Behaviour)
}
我想分析5个实例的段中的数据集，然后分析到接下来的5个实例，并再次应用此函数。因此对于20个实例的这个数据集，我期望来自该函数的4个预测。似乎逻辑上要做的是在数据集上传递一个滑动窗口 - 经过多次试验并最终意识到rollapply不会采用混合类型，我想知道是否可以使用for循环完成此操作？该函数使用均值和最大值，虽然我尝试了很多for循环但我无法使其工作。有谁知道如何编写for循环来实现这一目标？
答案 0 :(得分：3)
首先，您需要将数据转换为数字类型，因为它们存储为因子：
Out[,-6] <- as.data.frame(sapply(Out[,-6], function(x) as.numeric(as.character(x))))
现在，您可以使用split-apply-combine将数据框分成5行块，将函数应用于每个，并获取结果：
sapply(split(Out, rep(1:4, each=5)), do.call, what=JRip)
# 1 2 3 4
# "resting" "resting" "resting" "resting"
对于提供的数据框，看起来每个5行段都处于静止状态。这很容易确认，因为max(Out$Speed_Out)是0.465，意味着没有分段行走，而sum(Out$Dist_Out)是8.94，这意味着没有分段正在放牧。