问题
I have a data with 4 columns date, ville, borne and combined. I want to reshape this data to long format using reshape package.
Head of data :
> head(newdata)
date ville borne comb
1 2010-01-01 00:00:00 46200 78500 124700
2 2010-01-01 01:00:00 46300 74100 120400
3 2010-01-01 02:00:00 46400 77600 124000
4 2010-01-01 03:00:00 46500 75600 122100
5 2010-01-01 04:00:00 46500 79000 125500
6 2010-01-01 05:00:00 46600 75500 122100
The reproducible data is as follows:
newdata <- structure(list(date = structure(list(sec = c(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, 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, 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, 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, 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,
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), min = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), hour = c(0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L), mday = 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, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L), mon = c(0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), year = c(110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L), wday = c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L), yday = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L
), isdst = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L)), .Names = c("sec", "min", "hour", "mday", "mon",
"year", "wday", "yday", "isdst"), class = c("POSIXlt", "POSIXt"
)), ville = c(46200, 46300, 46400, 46500, 46500, 46600, 46500,
46600, 46600, 46500, 46500, 46500, 46400, 46400, 46300, 46300,
40700, 40700, 40600, 40500, 40500, 40400, 40400, 40400, 40300,
40300, 40200, 33800, 34300, 34600, 35000, 35200, 35300, 35500,
35600, 38300, 38000, 37900, 37800, 37700, 37600, 37400, 37400,
37200, 37100, 37000, 36900, 33000, 33300, 33400, 33500, 33600,
33600, 33600, 33600, 33500, 33500, 33500, 33500, 33400, 34000,
31600, 31600, 31600, 31700, 31700, 31600, 31600, 31500, 31400,
31400, 31300, 31200, 31100, 31000, 32100, 32500, 32700, 32800,
32800, 32900, 32900, 32900, 32900, 32800, 32900, 32900, 32900,
32800, 32800, 32700, 32700, 32700, 32600, 32700, 32600, 32600,
32600, 32600, 32600, 32500, 32500, 32400, 32400, 28900, 29000,
29200, 29300, 29400, 30100, 31000, 30900, 30800, 30800, 30700,
30600, 30600, 30600, 30400, 30400, 30300, 30300, 30300, 30200,
30200, 30100, 30100, 30000, 30000, 30000, 29900, 29900, 30200,
29900, 27100, 27200, 27300, 27300, 27400, 26000, 23000, 23200,
23400, 23500, 23600, 25000, 25100, 27700, 27700, 27500, 27500,
27400, 27400, 27300, 27200, 27300, 28500, 27200, 27200, 27200,
27200, 27200, 27200, 27200, 27200, 27200, 26200, 26200, 26200,
26200, 26300, 26200, 26200, 26200, 26200, 26100, 26100, 26100,
26100, 26100, 26000, 26100, 26100, 26100, 26100, 26200, 26100,
26200, 26200, 26300, 26300, 26300, 26300, 26300, 26300, 26300,
26300, 26300, 26200, 26100, 26100, 26000, 25900, 25900, 25900,
25800, 25800, 25700, 25700, 25600, 25200, 24100, 21400, 21500,
21700, 21700, 21800, 21900, 21900, 22000, 21900, 22000, 22000,
22100, 22100, 22100, 22100, 22100, 23200, 22100, 22100, 22000,
22000, 22000, 21900, 21900, 21900, 21800, 21800, 21800, 21000,
19400, 19500, 19500, 19500, 19500, 19600, 19600, 19600, 19600,
19700, 19700, 19700, 19700, 19700, 19700, 19700, 19700, 19800,
19800, 19900, 20000, 20000, 20100, 20900, 22400, 22400, 22300,
22200, 22100, 22100, 22000, 21800, 21800, 21700, 20100, 21700,
20100, 20100, 20100, 20100, 20000, 19900, 19900, 19800, 19700,
19700, 19600, 15300, 15400, 15500, 15500, 15600, 15700, 15700,
15700, 15800, 15900, 15900, 15900, 17600, 16000, 16000, 16000,
16100, 16100, 16100, 16100, 16100, 16100, 16200, 16100, 16200,
16100, 16200, 16200, 16100, 16200, 16200, 16200, 16200, 16200,
16200, 16300, 16300, 16400, 16400, 16500, 16500, 16500, 16500,
16500, 16500, 16400, 16500, 16500), borne = c(78500, 74100, 77600,
75600, 79000, 75500, 76600, 72300, 75700, 75400, 75700, 78700,
76900, 76500, 72800, 75100, 74700, 80200, 75200, 74900, 74700,
73600, 69900, 73600, 70600, 74100, 75800, 73100, 71400, 72300,
71300, 72400, 72700, 72200, 69400, 72600, 68900, 67700, 66000,
64800, 66700, 68400, 65500, 66600, 63600, 106000, 106000, 109000,
110000, 110000, 110000, 110000, 114000, 112000, 112000, 111000,
110000, 109000, 108000, 108000, 106000, 105000, 110000, 113000,
113000, 112000, 111000, 110000, 93500, 62600, 62700, 63300, 63300,
63300, 63300, 63000, 63200, 62900, 62600, 62900, 62500, 62400,
62900, 62800, 62200, 62500, 62200, 62100, 62200, 62100, 59300,
60000, 60000, 60100, 60500, 60700, 60800, 60700, 60900, 61100,
60800, 61000, 60900, 60800, 60300, 60500, 60600, 59300, 59500,
59400, 59800, 60000, 59300, 56200, 56800, 56700, 56600, 56100,
55700, 54400, 54100, 54100, 54000, 53800, 53700, 52800, 52400,
52200, 52300, 52400, 48400, 48800, 49100, 49400, 49800, 49800,
49700, 49800, 50200, 47100, 47400, 47500, 47700, 47500, 47600,
47700, 47500, 47300, 47300, 47300, 47200, 47300, 46900, 45500,
45700, 45900, 45600, 45700, 46000, 45800, 45700, 45900, 46000,
45800, 45900, 45900, 46100, 46000, 45900, 46000, 45900, 43600,
43700, 43700, 43600, 43700, 43500, 43500, 43500, 43200, 43300,
43200, 41400, 41500, 41800, 41900, 41700, 41700, 41900, 41900,
41900, 41900, 41700, 41800, 41800, 41800, 41800, 41800, 41700,
41800, 41700, 41700, 41600, 41700, 41800, 41600, 40700, 40700,
40800, 37800, 38100, 38600, 38800, 38700, 38700, 38400, 38200,
38200, 38100, 37900, 37700, 37600, 37400, 37300, 37200, 37000,
36900, 36800, 36600, 33200, 30400, 30900, 31300, 31500, 31700,
31900, 32000, 32100, 32300, 32300, 32400, 32500, 32500, 32600,
32700, 32800, 32800, 32900, 33000, 33000, 33100, 33200, 33300,
33300, 33400, 33600, 33600, 33800, 33900, 34000, 34000, 34100,
30900, 30900, 31000, 30900, 30800, 30700, 30600, 30600, 30500,
30400, 30400, 30300, 30200, 30300, 30300, 30200, 30100, 30100,
30000, 29900, 29900, 29900, 29800, 29700, 29700, 29700, 29700,
29600, 29500, 29400, 29400, 29400, 29400, 29500, 29400, 29400,
29300, 29200, 29200, 29300, 29100, 29100, 29200, 29100, 29100,
29100, 29100, 29000, 29000, 29000, 29000, 29000, 28900, 28900,
28900, 28900, 28900, 28800, 28900, 28800, 28800, 28800, 28700,
28700, 28700, 28700, 28700, 28600, 28600, 28600, 28500, 28500,
28400, 30700), comb = c(124700, 120400, 124000, 122100, 125500,
122100, 123100, 118900, 122300, 121900, 122200, 125200, 123300,
122900, 119100, 121400, 115400, 120900, 115800, 115400, 115200,
114000, 110300, 114000, 110900, 114400, 116000, 106900, 105700,
106900, 106300, 107600, 108000, 107700, 105000, 110900, 106900,
105600, 103800, 102500, 104300, 105800, 102900, 103800, 100700,
143000, 142900, 142000, 143300, 143400, 143500, 143600, 147600,
145600, 145600, 144500, 143500, 142500, 141500, 141400, 140000,
136600, 141600, 144600, 144700, 143700, 142600, 141600, 125000,
94000, 94100, 94600, 94500, 94400, 94300, 95100, 95700, 95600,
95400, 95700, 95400, 95300, 95800, 95700, 95000, 95400, 95100,
95000, 95000, 94900, 92000, 92700, 92700, 92700, 93200, 93300,
93400, 93300, 93500, 93700, 93300, 93500, 93300, 93200, 89200,
89500, 89800, 88600, 88900, 89500, 90800, 90900, 90100, 87000,
87500, 87300, 87200, 86700, 86100, 84800, 84400, 84400, 84300,
84000, 83900, 82900, 82500, 82200, 82300, 82400, 78300, 78700,
79300, 79300, 76900, 77000, 77000, 77100, 77600, 73100, 70400,
70700, 71100, 71000, 71200, 72700, 72600, 75000, 75000, 74800,
74700, 74700, 74300, 72800, 72900, 73200, 74100, 72900, 73200,
73000, 72900, 73100, 73200, 73000, 73100, 73100, 72300, 72200,
72100, 72200, 72200, 69800, 69900, 69900, 69800, 69800, 69600,
69600, 69600, 69300, 69300, 69300, 67500, 67600, 67900, 68100,
67800, 67900, 68100, 68200, 68200, 68200, 68000, 68100, 68100,
68100, 68100, 68100, 67900, 67900, 67800, 67700, 67500, 67600,
67700, 67400, 66500, 66400, 66500, 63400, 63300, 62700, 60200,
60200, 60400, 60100, 60000, 60100, 60000, 59900, 59600, 59600,
59400, 59400, 59300, 59100, 59000, 58900, 59800, 55300, 52500,
52900, 53300, 53500, 53600, 53800, 53900, 53900, 54100, 54100,
53400, 51900, 52000, 52100, 52200, 52300, 52400, 52500, 52600,
52600, 52800, 52900, 53000, 53000, 53100, 53300, 53300, 53500,
53700, 53800, 53900, 54100, 50900, 51000, 51900, 53300, 53200,
53000, 52800, 52700, 52600, 52400, 52200, 52100, 51900, 50400,
52000, 50300, 50200, 50200, 50100, 49900, 49800, 49800, 49600,
49400, 49400, 49300, 45000, 45000, 45000, 44900, 45000, 45100,
45100, 45200, 45200, 45300, 45200, 45100, 46800, 45300, 45100,
45100, 45300, 45200, 45200, 45200, 45200, 45100, 45200, 45100,
45200, 45100, 45100, 45100, 45000, 45100, 45100, 45000, 45100,
45000, 45000, 45100, 45000, 45100, 45100, 45200, 45200, 45100,
45100, 45100, 45000, 44900, 44900, 47200)), .Names = c("date",
"ville", "borne", "comb"), row.names = c(NA, 336L), class = "data.frame")
I used the following formula to reshape the data:
renewdata <- melt(newdata,id=c("date"))
but I got the following error:
Error in data.frame(ids, variable, value, stringsAsFactors = FALSE) :
arguments imply differing number of rows: 336, 1008
However, if I use the above formula with only two data columns, then it works fine. Does melt function work with only two columns ? Any suggestions to solve this problem is highly appreciated.
The desired output is as follows:
date variable value
1 2010-01-01 00:00:00 ville 46200
2 2010-01-01 01:00:00 ville 46300
3 2010-01-01 02:00:00 ville 46400
4 2010-01-01 03:00:00 ville 46500
5 2010-01-01 04:00:00 ville 46500
6 2010-01-01 05:00:00 ville 46600
回答1:
I just tried it using reshape2 package and I get the same error. The problem seems to be recycling. That is, columns of class POSIXlt POSIXt don't seem to recycle.
Let me explain in detail now. First type reshape2:::melt.data.frame to have a look at this function. If you type now:
debugonce(reshape2:::melt.data.frame)
melt(newdata, id.var = "date")
Then, you'll be in debug mode. Keep hitting enter until you see this output:
debug: df <- data.frame(ids, variable, value, stringsAsFactors = FALSE)
Browse[2]>
Before you hit enter, check what ids looks like. It's a data.frame with one column that's exactly the same as your first column (date). That is it's dimensions are 336*1. Now, if you hit enter one more time, you'll see the error message you posted:
Error in data.frame(ids, variable, value, stringsAsFactors = FALSE) :
arguments imply differing number of rows: 336, 1008
So, what's happening here? The error comes from the line:
df <- data.frame(ids, variable, value, stringsAsFactors = FALSE)
Here, because ids is a data.frame whose column is of type POSIXlt POSIXt, the recycling doesn't seem to happen. That is, if you do:
x <- data.frame(date = 1:4)
data.frame(x, y = 1:20)
you see that the date column gets recycled and this works fine! Now, let's try with x as a POSIXlt POSIXt object.
x <- data.frame(date = newdata$date[1:4])
# Note: the above assignment will convert x$date to POSIXct
# (as POSIXlt is internally a list)
x$date <- as.POSIXlt(x$date) # get x$date to POSIXlt
data.frame(x, y = 1:20) # the way reshape2 handles melt function
Error in data.frame(x, y = 1:20) :
arguments imply differing number of rows: 4, 20
So, basically, POSIXlt POSIXt is not recycled. You can convert the date column to POSIXct for example and then try it. For ex, this works:
x <- data.frame(date = newdata$date[1:4])
data.frame(x, y = 1:20)
# works fine!
Hope this helps.
来源：https://stackoverflow.com/questions/16971937/melt-the-data-frame-with-4-columns-to-three-columns