我的基本想法是计算大型矩阵的块均值（按列），并将这些均值存储为数据帧的行。注意，这些块具有不同的大小（行数），并且存储在向量vec1中。下面是我的代码：
df <- setNames(data.frame(matrix(nrow = 4000, ncol = 3)),
c("Age","Weight", "height"))
#
i <- 1
j <- vec1[1] - 1
k <- 0
repeat {
elements <- as.vector(apply(mydata[i : (j + 1), 3:5], 2, mean))
df <- rbind(df, elements)
k <- k + 1
i = i + vec1[k]
j = j + vec1[k + 1]
if (j + 1 >= l){
break
}
}
N.B .：当我手动执行计算而不循环时，它可以工作。但是循环的结果会产生一个4000 * 3的矩阵，该矩阵除第一行外还用NA填充。
vec1是具有4000个条目的向量，其前500个元素-head(vec1, 500)-如下：
c(15L, 45L, 111L, 32L, 25L, 13L, 144L, 31L, 150L, 124L, 22L,
94L, 60L, 156L, 4L, 30L, 12L, 12L, 16L, 23L, 242L, 58L, 65L,
17L, 63L, 193L, 148L, 162L, 79L, 6L, 22L, 30L, 188L, 44L, 7L,
130L, 49L, 10L, 87L, 11L, 6L, 113L, 113L, 100L, 42L, 5L, 64L,
127L, 73L, 36L, 13L, 120L, 44L, 34L, 153L, 10L, 35L, 205L, 31L,
102L, 181L, 26L, 105L, 75L, 42L, 122L, 42L, 221L, 216L, 120L,
50L, 171L, 56L, 1L, 89L, 11L, 103L, 167L, 96L, 31L, 67L, 182L,
114L, 45L, 4L, 118L, 19L, 243L, 241L, 48L, 36L, 64L, 94L, 63L,
16L, 8L, 213L, 26L, 127L, 139L, 71L, 91L, 133L, 23L, 88L, 31L,
28L, 70L, 112L, 6L, 25L, 82L, 17L, 24L, 196L, 39L, 78L, 23L,
73L, 110L, 64L, 87L, 84L, 11L, 101L, 19L, 6L, 25L, 39L, 59L,
68L, 31L, 183L, 52L, 142L, 63L, 41L, 214L, 19L, 120L, 85L, 104L,
3L, 8L, 38L, 11L, 12L, 21L, 12L, 53L, 37L, 85L, 106L, 12L, 31L,
106L, 75L, 10L, 121L, 60L, 137L, 96L, 177L, 102L, 97L, 145L,
52L, 11L, 112L, 73L, 67L, 8L, 235L, 203L, 182L, 168L, 101L, 144L,
238L, 73L, 38L, 85L, 56L, 14L, 162L, 131L, 14L, 154L, 28L, 30L,
75L, 88L, 268L, 169L, 255L, 127L, 111L, 63L, 42L, 156L, 12L,
22L, 71L, 140L, 110L, 33L, 99L, 79L, 47L, 7L, 131L, 69L, 10L,
61L, 2L, 57L, 96L, 111L, 41L, 250L, 77L, 22L, 198L, 187L, 15L,
108L, 130L, 76L, 190L, 249L, 68L, 117L, 79L, 2L, 13L, 108L, 9L,
39L, 42L, 43L, 149L, 62L, 47L, 66L, 85L, 197L, 109L, 21L, 263L,
54L, 13L, 61L, 72L, 73L, 80L, 46L, 7L, 110L, 128L, 236L, 27L,
240L, 61L, 23L, 82L, 157L, 92L, 95L, 6L, 137L, 237L, 2L, 20L,
45L, 48L, 200L, 20L, 127L, 21L, 64L, 49L, 38L, 108L, 11L, 16L,
108L, 18L, 62L, 15L, 61L, 81L, 28L, 20L, 33L, 50L, 222L, 267L,
29L, 3L, 44L, 46L, 3L, 212L, 53L, 67L, 131L, 43L, 3L, 123L, 134L,
106L, 91L, 194L, 2L, 97L, 43L, 39L, 65L, 96L, 233L, 36L, 81L,
6L, 57L, 29L, 10L, 17L, 10L, 92L, 28L, 168L, 78L, 52L, 227L,
86L, 134L, 58L, 65L, 175L, 20L, 113L, 33L, 143L, 11L, 87L, 101L,
19L, 106L, 63L, 68L, 38L, 263L, 140L, 45L, 169L, 268L, 182L,
114L, 88L, 39L, 6L, 53L, 244L, 84L, 99L, 46L, 53L, 1L, 111L,
88L, 115L, 93L, 35L, 124L, 145L, 262L, 47L, 10L, 84L, 20L, 159L,
207L, 102L, 48L, 79L, 28L, 51L, 77L, 3L, 58L, 20L, 81L, 54L,
46L, 29L, 12L, 74L, 28L, 4L, 18L, 18L, 38L, 29L, 157L, 108L,
94L, 56L, 23L, 92L, 60L, 86L, 39L, 59L, 85L, 14L, 53L, 23L, 88L,
130L, 8L, 149L, 65L, 71L, 88L, 31L, 67L, 83L, 106L, 44L, 35L,
23L, 76L, 90L, 271L, 12L, 167L, 30L, 87L, 3L, 7L, 15L, 159L,
199L, 7L, 35L, 193L, 207L, 6L, 98L, 61L, 81L, 95L, 66L, 2L, 65L,
242L, 221L, 51L, 6L, 5L, 265L, 119L, 126L, 7L, 159L, 74L, 63L,
188L, 15L, 42L, 26L, 41L, 116L, 50L, 62L, 121L, 67L, 1L, 10L,
192L, 59L, 42L, 84L, 187L, 26L, 32L, 35L, 60L, 117L, 227L, 20L,
20L, 125L, 191L, 24L, 270L, 13L, 14L, 59L, 214L, 96L, 100L, 15L,
22L, 100L, 49L, 146L, 137L, 257L, 93L, 91L, 23L, 234L, 108L,
52L, 7L, 124L, 48L, 2L, 42L, 82L, 99L, 85L, 11L, 141L, 185L,
30L, 1L, 269L, 83L, 25L, 187L, 122L, 222L, 11L, 201L, 95L, 40L,
146L, 75L, 218L, 3L, 39L, 76L, 205L, 21L, 23L, 36L, 43L, 105L,
89L, 10L, 155L, 32L, 144L, 160L, 181L, 144L, 139L, 5L, 2L, 26L,
48L, 55L, 177L, 178L, 108L, 221L, 149L, 32L, 77L, 29L, 160L,
115L, 23L, 193L, 113L, 1L, 154L, 87L, 239L, 221L, 36L, 100L,
34L, 42L, 77L, 62L, 20L, 73L, 81L, 17L, 21L, 33L, 3L, 33L, 84L,
92L, 31L, 9L, 65L, 187L, 62L, 87L, 48L, 218L, 6L, 41L, 90L, 102L,
67L, 27L, 1L, 270L, 159L, 46L, 31L, 50L, 19L, 2L, 30L, 35L, 211L,
103L, 12L, 99L, 75L, 37L, 99L, 83L, 49L, 38L, 125L, 53L, 29L,
11L, 23L, 50L, 41L, 114L, 72L, 44L, 32L, 105L, 25L, 67L, 203L,
24L, 82L, 167L, 205L, 28L, 89L, 75L, 52L, 36L, 29L, 16L, 137L,
95L, 230L, 43L, 4L, 194L, 12L, 21L, 25L, 6L, 176L, 48L, 6L, 142L,
24L, 15L, 101L, 160L, 43L, 9L, 125L, 122L, 53L, 55L, 226L, 241L,
259L, 150L, 142L, 47L, 89L, 13L, 2L, 173L, 147L, 5L, 15L, 159L,
7L, 27L, 117L, 97L, 38L, 71L, 7L, 35L, 91L, 172L, 149L, 103L,
51L, 117L, 67L, 142L, 63L, 53L, 87L, 105L, 2L, 1L, 17L, 30L,
114L, 55L, 202L, 34L, 70L, 50L, 37L, 167L, 45L, 7L, 102L, 238L,
176L, 27L, 7L, 86L, 43L, 269L, 88L, 1L, 18L, 41L, 14L, 71L, 88L,
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178L, 249L, 42L, 184L, 43L, 5L, 13L, 90L, 111L, 173L, 220L, 71L,
223L, 5L, 178L, 42L, 126L, 56L, 6L, 15L, 249L, 254L, 148L, 60L,
133L, 218L, 111L, 29L, 77L, 16L, 71L, 128L, 100L, 4L, 13L, 72L,
21L, 133L, 130L, 51L, 62L, 14L, 189L, 99L, 32L, 211L, 5L, 15L,
35L, 72L, 153L, 59L, 85L, 165L, 18L, 51L, 21L, 123L, 15L, 93L,
53L, 2L, 210L, 126L, 196L, 62L, 156L, 57L, 179L, 79L, 27L, 22L,
52L, 167L, 33L, 150L, 72L, 30L, 3L, 65L, 36L, 89L, 54L, 18L,
55L, 137L, 119L, 258L, 33L, 21L, 32L, 116L, 12L, 176L, 91L, 168L,
74L, 6L, 4L, 138L, 149L, 39L, 47L, 49L, 81L, 35L, 61L, 4L, 58L,
31L, 172L, 30L, 27L, 184L, 41L, 51L, 24L, 115L, 81L, 71L, 61L,
154L, 206L, 182L, 149L, 42L, 49L, 6L, 104L, 2L, 217L, 27L, 148L,
37L, 159L, 182L, 139L, 49L, 30L, 41L, 20L, 2L, 15L, 35L, 157L,
86L, 261L, 161L, 145L, 105L, 87L, 220L, 12L, 99L, 233L, 190L,
59L, 95L, 151L, 38L, 46L, 32L, 56L, 48L, 71L, 22L, 44L, 143L,
34L, 34L, 7L, 20L, 87L, 106L, 114L, 26L, 7L, 110L, 93L, 113L,
83L, 76L, 43L, 22L, 2L, 101L, 22L, 65L, 17L, 112L, 116L, 138L,
122L, 68L, 5L, 247L, 155L, 149L, 4L, 49L, 130L, 46L, 13L, 223L,
74L, 15L, 175L, 24L, 2L, 96L, 114L, 125L, 56L, 27L, 67L, 30L,
206L, 38L, 42L, 9L, 118L, 24L, 11L, 156L, 109L, 154L, 40L, 175L,
107L, 193L, 30L, 75L, 72L, 44L, 232L, 37L, 130L, 47L, 81L, 18L,
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200L, 78L, 94L, 140L, 222L, 143L, 56L, 37L, 101L, 83L, 48L, 53L,
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132L, 5L, 175L, 12L, 51L, 81L, 29L, 232L, 126L, 20L, 157L, 158L,
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135L, 183L, 77L, 41L, 12L, 101L, 12L, 25L, 113L, 38L, 28L, 95L,
232L, 6L, 98L, 67L, 13L, 46L, 9L, 107L, 88L, 164L, 79L, 18L,
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153L, 150L, 161L, 198L, 174L, 167L, 15L, 154L, 160L, 171L, 169L,
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45L, 1L, 48L, 163L, 3L, 56L, 76L, 77L, 107L, 183L, 7L, 30L, 145L,
4L, 26L, 174L, 76L, 83L, 73L, 172L, 226L, 2L, 18L, 1L, 8L, 90L,
36L, 8L, 44L, 36L, 90L, 64L, 89L, 127L, 24L, 67L, 7L, 263L, 71L,
178L, 21L, 21L, 28L, 236L, 116L, 46L, 82L, 79L, 17L, 18L, 131L,
49L, 90L, 65L, 168L, 93L, 2L, 267L, 59L, 35L, 126L, 35L, 185L,
6L, 45L, 31L, 42L, 71L, 67L, 85L, 11L, 9L, 30L, 22L, 24L, 123L,
119L, 14L, 98L, 31L, 101L, 137L, 81L, 47L, 79L, 4L, 167L, 78L,
11L, 30L, 9L, 115L, 32L, 12L, 80L, 33L, 68L, 36L, 130L, 31L,
7L, 169L, 54L, 9L, 155L, 61L, 250L, 89L, 149L, 2L, 101L, 66L,
166L, 41L, 4L, 62L, 9L, 160L, 189L, 144L, 101L, 190L, 129L, 11L,
124L, 22L, 13L, 151L, 1L, 58L, 173L, 195L, 47L, 3L, 3L, 24L,
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212L, 224L, 77L, 53L, 135L, 2L, 11L, 65L, 60L, 115L, 78L, 55L,
66L, 31L, 88L, 72L, 87L, 181L, 198L, 75L, 239L, 111L, 10L, 128L,
103L, 68L, 27L, 127L, 4L, 24L, 102L, 3L, 19L, 103L, 268L, 5L,
153L, 216L, 9L, 56L, 154L, 3L, 13L, 128L, 252L, 17L, 10L, 78L,
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235L, 61L, 7L, 4L, 35L, 28L, 85L, 118L, 69L, 92L, 158L, 40L,
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51L, 19L, 145L, 34L, 184L, 16L, 217L, 62L, 67L, 44L, 16L, 5L,
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71L, 18L, 166L, 40L, 57L, 185L, 88L, 115L)
答案 0 :(得分：0)
问题是，您最初创建了4000 * 3 data.frame，并用NA填充。请查看更正的代码。我没有将您的实际数据vec1（太长）放置在vec1上，而没有对指数分布进行抽样。另外，我使用colMeans比apply更有效。请参见下面的代码：
# vec1, mydata, l - simulation
set.seed(123)
vec1 <- (sample(1:271, 4000, replace = TRUE, prob = dexp(1:271, rate = .01)))
mydata <- matrix(1:(300 * 300), nrow = 300)
l <- 300
# data given by OP
df <- data.frame(Age = 1, Weight = 1, height = 1 )
df <- df[-1, ]
i <- 1
j <- vec1[1] - 1
k <- 0
repeat{
elements <- as.vector(colMeans(mydata[i:(j + 1), 3:5]))
df <- rbind(df, elements)
k <- k + 1
i = i + vec1[k]
j = j + vec1[k + 1]
if (j + 1 >= l){
break
}
}
df <- setNames(df, c("Age","Weight", "height"))
df
输出：
Age Weight height
1 608.0 908.0 1208.0
2 638.0 938.0 1238.0
3 716.0 1016.0 1316.0
4 787.5 1087.5 1387.5
5 816.0 1116.0 1416.0
6 835.0 1135.0 1435.0