R：如何用前两列作为自变量重塑数据框？
R: how to reshape data frame with the first two columns as independent variable?
我正在尝试建立一个数据框，其中月份和地点作为自变量，长度作为唯一的因变量。但我不确定哪个功能可以提供帮助。目前，我有一个很长的数据框，我想把它变宽。
我试过用dcast（package: reshape2），但是它把因变量总结成了length。因此，它不是将每个数据点作为输出，而是告诉我每个月份和站点的组合有多少个数据点。
structure(list(month = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), site = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("port", "bluff", "palme"), class = "factor"),
length = c(14.5, 8, 9.6, 11.3, 16.8, 13.4, 13.3, 16.1, 12.1,
9.5, 13.6, 9.5, 20.1, 19, 21.8, 14, 13.6, 16.4, 22, 15.6,
15.7, 25.4, 32, 30, 11.5, 18.3, 10.2, 7.4, 9, 12.5, 36.45,
51.5, 56, 35.1, 35.5, 31.1, 39.9, 55.4, 39.4, 72, 48, 15.7,
39.95, 21.3, 25.9, 42.2, 25.2, 60, 26.1, 44.2, 31.5, 41.3,
34.6, 46.7, 33, 52.2, 20.8, 16.9, 16, 55.3, 108.3, 84.4,
100, 32.95, 43, 132, 144.4, 101.2, 106.4, 87.7, 113.7, 25,
123, 126.8, 61.25, 94, 126, 98.8, 102.6, 107.6, 137, 98.7,
29.1, 136.9, 83.5, 32.1, 63.7, 95.5, 95.8, 117.4, 47.4, 54.2,
78.4, 98.5, 47.1, 55.3, 122.4, 34.5, 60.2, 64.5, 44.9, 71,
66.3, 33.6, 33.7, 61.7, 38.5, 44.9, 36.8, 15.4, 15.6, 21.3,
16.4, 63.6, 40, 111.1, 39.1, 65.7, 45.9, 20, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 14, 18.7, 13.5, 15.2, 16.5, 19.3, 6.5,
19.8, 17, 19.1, 14.5, 14.1, 10.1, 15.4, 14.3, 13.4, 4.8,
15.4, 17.5, 11.6, 8.4, 10.1, 11.4, 6.9, 13, 29, 30.7, 17.2,
10.7, 15.5, 45.5, 34.5, 23.5, 41, 32.1, 21.9, 52.1, 25.1,
19, 57.8, 46.5, 26.8, 64.5, 65, 68, 69.4, 13.2, 71.6, 67,
45.6, 22.7, 61.5, 27.8, 35, 40.9, 49.3, 27.9, 15.2, 27.4,
11.5, 55.8, 123, 144.2, 88.2, 48.3, 56.1, 159, 111.4, 170.5,
69, 77.6, 67.9, 69.8, 18.1, 96.8, 116.7, 124.5, 25.2, 120,
122.4, 95, 16, 117.1, 26.3, 124.3, 100.8, 36.9, 47, 16.6,
94.2, 54.3, 40.6, 138.7, 56, 87.2, 82.5, 53, 77.5, 63.5,
73.8, 43.1, 35.3, 139, 77.6, 77.9, 91, 48.2, 121.3, 43.4,
163, 144.9, 67.6, 62.9, 52.9, 85.1, 67.5, 56.6, 47.1, 153.7,
43.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 28.8, 72.3, 21, 58.7, 52.5, 34.7, 61, 29.1,
47.9, 60.1, 73.4, 38.8, 64.2, 60.4, 35.6, 53.6, 66.4, 75.1,
29.3, 39.5, 38.6, 41.7, 61.4, 79.5, 69.8, 77.7, 74.2, 26.6,
27, 62.9, 54.9, 56.5, 50, 36.2, 49.8, 46.4, 36, 38.3, 28.4,
96.2, 118.3, 133.4, 98.5, 140.4, 145, 144.9, 123.85, 59.2,
137.9, 137.1, 51.9, 115.1, 48.6, 123.5, 136.8, 148, 135.8,
15, 143.9, 83.4, 38.2, 25.4, 26.3, 82, 106.2, 83.3, 99.8,
102.9, 107.5, 71.6, 69.3, 68.5, 128.8, 143.3, 125.5, 172.9,
154.1, 141.9, 111.3, 78.2, 118.9, 168.1, 26.1, 160.6, 78.1,
74.8, 36.5, 152.4, 39.8, 116.1, 56.2, 51.3, 69.7, 34.9, 35.5,
31.2, 57, 49, 64, 54.2, 30.5, 47.2, 63, 65.3, 27.8, 26.5,
24.2, 32.9, 33.4, 33.6, 68.9, 70, 18.4, 33.2, 31.4, 23.5,
40.7, 21, 51.9, 23.3)), row.names = c(NA, -450L), class = "data.frame")
理想情况下，生成的数据框一列代表月份，一列代表网站，每个月和网站组合 30 列（即，使数据框从长到宽）。
Desireable output
你可以试试tidyverse
library(tidyverse)
df %>%
as_tibble() %>%
group_by(month, site) %>%
mutate(index=1:n()) %>%
spread(index, length)
# A tibble: 15 x 32
# Groups: month, site [15]
month site `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` `12` `13` `14` `15` `16` `17` `18`
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 port 14.5 8 9.6 11.3 16.8 13.4 13.3 16.1 12.1 9.5 13.6 9.5 20.1 19 21.8 14 13.6 16.4
2 1 bluff 14 18.7 13.5 15.2 16.5 19.3 6.5 19.8 17 19.1 14.5 14.1 10.1 15.4 14.3 13.4 4.8 15.4
3 1 palme 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 2 port 36.4 51.5 56 35.1 35.5 31.1 39.9 55.4 39.4 72 48 15.7 40.0 21.3 25.9 42.2 25.2 60
5 2 bluff 45.5 34.5 23.5 41 32.1 21.9 52.1 25.1 19 57.8 46.5 26.8 64.5 65 68 69.4 13.2 71.6
6 2 palme 28.8 72.3 21 58.7 52.5 34.7 61 29.1 47.9 60.1 73.4 38.8 64.2 60.4 35.6 53.6 66.4 75.1
7 3 port 108. 84.4 100 33.0 43 132 144. 101. 106. 87.7 114. 25 123 127. 61.2 94 126 98.8
8 3 bluff 55.8 123 144. 88.2 48.3 56.1 159 111. 170. 69 77.6 67.9 69.8 18.1 96.8 117. 124. 25.2
9 3 palme 54.9 56.5 50 36.2 49.8 46.4 36 38.3 28.4 96.2 118. 133. 98.5 140. 145 145. 124. 59.2
10 4 port 47.4 54.2 78.4 98.5 47.1 55.3 122. 34.5 60.2 64.5 44.9 71 66.3 33.6 33.7 61.7 38.5 44.9
11 4 bluff 54.3 40.6 139. 56 87.2 82.5 53 77.5 63.5 73.8 43.1 35.3 139 77.6 77.9 91 48.2 121.
12 4 palme 38.2 25.4 26.3 82 106. 83.3 99.8 103. 108. 71.6 69.3 68.5 129. 143. 126. 173. 154. 142.
13 5 port 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
14 5 bluff 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15 5 palme 56.2 51.3 69.7 34.9 35.5 31.2 57 49 64 54.2 30.5 47.2 63 65.3 27.8 26.5 24.2 32.9
# ... with 12 more variables: `19` <dbl>, `20` <dbl>, `21` <dbl>, `22` <dbl>, `23` <dbl>, `24` <dbl>, `25` <dbl>, `26` <dbl>,
# `27` <dbl>, `28` <dbl>, `29` <dbl>, `30` <dbl>
我正在尝试建立一个数据框，其中月份和地点作为自变量，长度作为唯一的因变量。但我不确定哪个功能可以提供帮助。目前，我有一个很长的数据框，我想把它变宽。
我试过用dcast（package: reshape2），但是它把因变量总结成了length。因此，它不是将每个数据点作为输出，而是告诉我每个月份和站点的组合有多少个数据点。
structure(list(month = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), site = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("port", "bluff", "palme"), class = "factor"),
length = c(14.5, 8, 9.6, 11.3, 16.8, 13.4, 13.3, 16.1, 12.1,
9.5, 13.6, 9.5, 20.1, 19, 21.8, 14, 13.6, 16.4, 22, 15.6,
15.7, 25.4, 32, 30, 11.5, 18.3, 10.2, 7.4, 9, 12.5, 36.45,
51.5, 56, 35.1, 35.5, 31.1, 39.9, 55.4, 39.4, 72, 48, 15.7,
39.95, 21.3, 25.9, 42.2, 25.2, 60, 26.1, 44.2, 31.5, 41.3,
34.6, 46.7, 33, 52.2, 20.8, 16.9, 16, 55.3, 108.3, 84.4,
100, 32.95, 43, 132, 144.4, 101.2, 106.4, 87.7, 113.7, 25,
123, 126.8, 61.25, 94, 126, 98.8, 102.6, 107.6, 137, 98.7,
29.1, 136.9, 83.5, 32.1, 63.7, 95.5, 95.8, 117.4, 47.4, 54.2,
78.4, 98.5, 47.1, 55.3, 122.4, 34.5, 60.2, 64.5, 44.9, 71,
66.3, 33.6, 33.7, 61.7, 38.5, 44.9, 36.8, 15.4, 15.6, 21.3,
16.4, 63.6, 40, 111.1, 39.1, 65.7, 45.9, 20, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 14, 18.7, 13.5, 15.2, 16.5, 19.3, 6.5,
19.8, 17, 19.1, 14.5, 14.1, 10.1, 15.4, 14.3, 13.4, 4.8,
15.4, 17.5, 11.6, 8.4, 10.1, 11.4, 6.9, 13, 29, 30.7, 17.2,
10.7, 15.5, 45.5, 34.5, 23.5, 41, 32.1, 21.9, 52.1, 25.1,
19, 57.8, 46.5, 26.8, 64.5, 65, 68, 69.4, 13.2, 71.6, 67,
45.6, 22.7, 61.5, 27.8, 35, 40.9, 49.3, 27.9, 15.2, 27.4,
11.5, 55.8, 123, 144.2, 88.2, 48.3, 56.1, 159, 111.4, 170.5,
69, 77.6, 67.9, 69.8, 18.1, 96.8, 116.7, 124.5, 25.2, 120,
122.4, 95, 16, 117.1, 26.3, 124.3, 100.8, 36.9, 47, 16.6,
94.2, 54.3, 40.6, 138.7, 56, 87.2, 82.5, 53, 77.5, 63.5,
73.8, 43.1, 35.3, 139, 77.6, 77.9, 91, 48.2, 121.3, 43.4,
163, 144.9, 67.6, 62.9, 52.9, 85.1, 67.5, 56.6, 47.1, 153.7,
43.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 28.8, 72.3, 21, 58.7, 52.5, 34.7, 61, 29.1,
47.9, 60.1, 73.4, 38.8, 64.2, 60.4, 35.6, 53.6, 66.4, 75.1,
29.3, 39.5, 38.6, 41.7, 61.4, 79.5, 69.8, 77.7, 74.2, 26.6,
27, 62.9, 54.9, 56.5, 50, 36.2, 49.8, 46.4, 36, 38.3, 28.4,
96.2, 118.3, 133.4, 98.5, 140.4, 145, 144.9, 123.85, 59.2,
137.9, 137.1, 51.9, 115.1, 48.6, 123.5, 136.8, 148, 135.8,
15, 143.9, 83.4, 38.2, 25.4, 26.3, 82, 106.2, 83.3, 99.8,
102.9, 107.5, 71.6, 69.3, 68.5, 128.8, 143.3, 125.5, 172.9,
154.1, 141.9, 111.3, 78.2, 118.9, 168.1, 26.1, 160.6, 78.1,
74.8, 36.5, 152.4, 39.8, 116.1, 56.2, 51.3, 69.7, 34.9, 35.5,
31.2, 57, 49, 64, 54.2, 30.5, 47.2, 63, 65.3, 27.8, 26.5,
24.2, 32.9, 33.4, 33.6, 68.9, 70, 18.4, 33.2, 31.4, 23.5,
40.7, 21, 51.9, 23.3)), row.names = c(NA, -450L), class = "data.frame")
理想情况下，生成的数据框一列代表月份，一列代表网站，每个月和网站组合 30 列（即，使数据框从长到宽）。
Desireable output
你可以试试tidyverse
library(tidyverse)
df %>%
as_tibble() %>%
group_by(month, site) %>%
mutate(index=1:n()) %>%
spread(index, length)
# A tibble: 15 x 32
# Groups: month, site [15]
month site `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` `12` `13` `14` `15` `16` `17` `18`
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 port 14.5 8 9.6 11.3 16.8 13.4 13.3 16.1 12.1 9.5 13.6 9.5 20.1 19 21.8 14 13.6 16.4
2 1 bluff 14 18.7 13.5 15.2 16.5 19.3 6.5 19.8 17 19.1 14.5 14.1 10.1 15.4 14.3 13.4 4.8 15.4
3 1 palme 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 2 port 36.4 51.5 56 35.1 35.5 31.1 39.9 55.4 39.4 72 48 15.7 40.0 21.3 25.9 42.2 25.2 60
5 2 bluff 45.5 34.5 23.5 41 32.1 21.9 52.1 25.1 19 57.8 46.5 26.8 64.5 65 68 69.4 13.2 71.6
6 2 palme 28.8 72.3 21 58.7 52.5 34.7 61 29.1 47.9 60.1 73.4 38.8 64.2 60.4 35.6 53.6 66.4 75.1
7 3 port 108. 84.4 100 33.0 43 132 144. 101. 106. 87.7 114. 25 123 127. 61.2 94 126 98.8
8 3 bluff 55.8 123 144. 88.2 48.3 56.1 159 111. 170. 69 77.6 67.9 69.8 18.1 96.8 117. 124. 25.2
9 3 palme 54.9 56.5 50 36.2 49.8 46.4 36 38.3 28.4 96.2 118. 133. 98.5 140. 145 145. 124. 59.2
10 4 port 47.4 54.2 78.4 98.5 47.1 55.3 122. 34.5 60.2 64.5 44.9 71 66.3 33.6 33.7 61.7 38.5 44.9
11 4 bluff 54.3 40.6 139. 56 87.2 82.5 53 77.5 63.5 73.8 43.1 35.3 139 77.6 77.9 91 48.2 121.
12 4 palme 38.2 25.4 26.3 82 106. 83.3 99.8 103. 108. 71.6 69.3 68.5 129. 143. 126. 173. 154. 142.
13 5 port 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
14 5 bluff 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15 5 palme 56.2 51.3 69.7 34.9 35.5 31.2 57 49 64 54.2 30.5 47.2 63 65.3 27.8 26.5 24.2 32.9
# ... with 12 more variables: `19` <dbl>, `20` <dbl>, `21` <dbl>, `22` <dbl>, `23` <dbl>, `24` <dbl>, `25` <dbl>, `26` <dbl>,
# `27` <dbl>, `28` <dbl>, `29` <dbl>, `30` <dbl>