rdataframereshape2dcast
R: how to reshape data frame with the first two columns as independent variable?
I am trying to set up a data frame with month and site as the independent variable, and length as the only dependent variable. But I am not sure which function can help. Currently, I have a long data frame, and I want to make it wide.
I tried to use dcast (package: reshape2), but it summarized the dependent variable into length. So instead of having each data point as an output, it tells me how many data points I have for each combination of month and site.
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")
Ideally, the resulting data frame would have one column for month, one for site, and 30 columns for each month and site combination (i.e. making data frame long to wide).
Solution
You can try a 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>
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