Related
I have a data set of juvenile fish lengths collected in 1992 at different sites. I have also assigned each value a uniqueID (due to previous errors while using the pivot_wider function). The data is as follows:
df<-structure(list(year = c(92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,
92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L
), site = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 15L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L,
28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L,
33L, 33L, 33L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L,
35L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 37L, 37L,
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L,
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L,
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 38L, 38L,
38L, 38L, 38L, 38L, 38L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L,
42L, 42L, 42L, 42L, 43L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L,
44L, 44L, 44L, 44L, 44L, 44L, 44L), .Label = c("1", "2", "3",
"5", "10", "16", "17", "18", "19", "20", "26", "27", "28", "29",
"30", "32", "33", "34", "35", "40", "41", "46", "50", "51", "52",
"53", "57", "58", "65", "67", "68", "69", "70", "71", "72", "75",
"76", "77", "78", "79", "80", "81", "84", "85"), class = "factor"),
length = c(64L, 71L, 70L, 64L, 53L, 55L, 53L, 61L, 74L, 62L,
66L, 65L, 57L, 66L, 71L, 65L, 74L, 68L, 70L, 67L, 73L, 67L,
71L, 45L, 50L, 60L, 70L, 96L, 133L, 72L, 127L, 69L, 66L,
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136L, 128L, 146L, 144L, 129L, 121L, 130L, 129L, 200L, 124L,
101L, 86L, 83L, 90L, 59L, 61L, 88L, 57L, 56L, 59L, 67L, 58L,
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66L, 46L, 44L, 43L, 42L, 45L, 45L, 178L, 77L, 40L, 66L, 42L,
62L, 47L, 71L, 42L, 74L, 60L, 58L, 45L, 49L, 50L, 65L, 46L,
60L, 48L, 36L, 40L, 46L, 38L, 46L, 43L, 37L, 46L, 51L, 57L,
69L, 69L, 41L, 44L, 42L, 63L, 47L, 44L, 43L, 47L, 49L, 37L,
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44L, 38L, 42L, 42L, 42L, 42L, 42L, 42L, 45L, 36L, 43L, 48L,
44L, 40L, 44L, 44L, 41L, 39L, 40L, 36L, 42L, 41L, 43L, 38L,
39L, 44L, 40L, 47L, 43L, 50L, 44L, 45L, 36L, 46L, 43L, 46L,
38L, 39L, 49L, 40L, 232L, 222L, 131L, 154L, 151L, 75L, 44L,
40L, 44L, 44L, 43L, 46L, 223L, 198L, 167L, 146L, 84L, 45L,
50L, 42L, 40L, 47L, 40L, 45L, 55L, 40L, 51L, 42L, 46L, 43L,
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43L, 46L, 40L, 42L, 45L, 50L, 40L, 44L, 48L, 44L, 42L, 56L,
43L, 46L, 39L, 40L, 46L, 41L, 40L, 37L, 49L, 46L, 40L, 43L,
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163L, 157L, 138L, 130L, 98L, 98L, 137L, 92L, 85L, 82L, 89L,
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93L, 80L, 87L, 75L, 46L, 50L, 82L, 96L, 68L, 46L, 41L, 51L,
36L, 48L, 94L, 87L, 82L, 66L, 95L, 82L, 67L, 46L, 40L, 64L,
46L, 41L, 45L, 41L, 232L, 150L, 173L, 155L, 152L, 155L, 96L,
141L, 132L, 93L, 48L, 90L, 174L, 152L, 152L, 133L, 158L,
139L, 93L, 90L, 77L, 73L, 155L, 97L, 75L, 77L, 78L, 73L,
79L, 67L, 65L, 74L, 46L, 79L, 75L, 88L, 75L, 90L, 47L, 51L,
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88L, 85L, 95L, 87L, 92L, 83L, 90L, 88L, 85L, 78L, 79L, 88L,
76L, 84L, 87L, 79L, 82L, 90L, 67L, 83L, 80L, 84L, 88L, 84L,
90L, 86L, 85L, 79L, 81L, 80L, 82L, 84L, 77L, 92L, 88L, 90L,
88L, 81L, 91L, 87L, 88L, 80L, 90L, 91L, 87L, 84L, 87L, 82L,
76L, 85L, 75L, 73L, 87L, 89L, 77L, 96L, 82L, 82L, 89L, 86L,
84L, 84L, 92L, 91L, 86L, 87L, 93L, 77L, 83L, 82L, 93L, 87L,
86L, 76L, 82L, 68L, 91L, 92L, 76L, 94L, 88L, 86L, 98L, 91L,
84L, 83L, 100L, 95L, 79L, 98L, 89L, 88L, 79L, 84L, 93L, 87L,
103L, 92L, 85L, 94L, 83L, 97L, 96L, 83L, 91L, 73L, 84L, 87L,
96L, 79L, 96L, 69L, 85L, 95L, 91L, 89L, 86L, 77L, 80L, 54L,
153L, 90L, 80L, 139L, 94L, 89L, 91L, 98L, 95L, 87L, 91L,
86L, 94L, 86L, 92L, 92L, 98L, 82L, 85L, 87L, 87L, 87L, 84L,
86L, 85L, 96L, 83L, 83L, 139L, 97L, 80L, 85L, 86L, 150L,
89L, 78L, 85L, 80L, 93L, 95L, 93L, 92L, 79L, 168L, 96L, 98L,
87L, 91L, 103L, 86L, 93L, 90L, 80L, 84L, 84L, 41L, 152L,
85L, 91L, 89L, 83L, 87L, 87L, 89L, 84L, 64L, 89L, 83L, 90L,
84L, 90L, 94L, 87L, 78L, 83L, 99L, 95L, 90L, 89L, 96L, 96L,
92L, 99L, 82L, 80L, 84L, 94L, 74L, 70L, 111L, 129L, 141L,
135L, 88L, 78L, 64L, 68L, 58L, 67L, 50L, 57L, 50L, 170L,
139L, 172L, 142L, 121L, 121L, 144L, 155L, 136L, 131L, 125L,
127L, 133L, 124L, 73L, 83L, 44L, 52L, 44L, 39L, 43L, 48L,
47L, 52L, 46L, 47L, 50L, 172L, 133L, 93L, 128L, 244L, 252L,
150L, 162L, 150L, 106L, 95L, 98L, 84L, 108L, 48L, 47L, 84L,
136L, 49L, 47L, 131L, 73L, 70L, 54L, 59L, 52L, 55L, 48L,
55L, 45L, 222L, 238L, 40L, 40L, 43L, 45L, 67L, 47L, 50L,
41L, 61L, 54L, 43L, 42L, 89L, 43L, 42L, 48L, 64L, 55L, 40L,
52L, 46L, 49L, 46L, 49L, 45L, 45L, 42L, 176L, 154L, 97L,
83L, 97L, 132L, 88L, 95L, 98L, 82L, 81L, 92L, 96L, 89L, 63L,
90L, 93L, 82L, 97L, 93L, 100L, 85L, 78L, 85L, 72L, 73L, 62L,
62L, 60L, 58L, 128L, 98L, 87L, 96L, 56L, 86L, 87L, 78L, 58L,
73L, 55L, 54L, 62L, 46L, 85L, 193L, 140L, 97L, 95L, 87L,
40L, 208L, 210L, 146L, 46L, 206L, 40L, 262L, 145L, 52L, 72L,
49L, 85L, 70L, 47L, 93L, 69L, 65L, 54L, 60L, 46L, 44L, 48L,
56L, 58L, 49L, 42L, 52L, 42L, 50L, 51L, 50L, 53L, 42L, 48L,
49L, 45L, 45L, 49L, 42L, 47L, 60L, 51L, 53L, 46L, 49L, 46L,
56L, 57L, 57L, 48L, 52L, 49L, 50L, 46L, 56L, 62L, 51L, 46L,
50L, 46L, 49L, 50L, 51L, 52L, 46L, 51L, 48L, 42L, 48L, 42L,
47L, 43L, 55L, 50L, 44L, 46L, 52L, 46L, 44L, 45L, 53L, 53L,
56L, 57L, 45L, 42L, 41L, 55L, 50L, 51L, 50L, 47L, 51L, 45L,
280L, 192L, 185L, 150L, 183L, 150L, 189L, 211L, 159L, 134L,
134L, 138L, 132L, 136L, 145L, 125L, 147L, 149L, 161L, 126L,
44L, 142L, 148L, 138L, 154L, 135L, 150L, 134L, 152L, 128L,
233L, 188L, 155L, 137L, 121L, 148L, 138L, 240L, 214L, 163L,
143L, 149L, 127L, 115L, 156L, 124L, 132L, 166L, 159L, 152L,
95L, 171L, 154L, 142L, 170L, 155L, 140L, 163L, 123L, 139L,
111L, 148L, 142L, 149L, 137L, 154L, 183L, 136L, 207L, 138L,
155L, 160L, 148L, 150L, 141L, 137L, 210L, 198L, 149L, 212L,
240L, 222L, 171L, 165L, 187L, 177L, 167L, 139L, 137L, 103L,
122L, 127L, 149L, 154L, 156L, 137L, 145L, 174L, 144L, 127L,
124L, 96L, 168L, 163L, 136L, 136L, 124L, 128L, 192L, 169L,
192L, 163L, 177L, 133L, 155L, 169L, 135L, 158L, 145L, 154L,
132L, 152L, 136L, 126L, 116L, 183L, 157L, 155L, 141L, 145L,
203L, 160L, 146L, 152L, 182L, 157L, 149L, 164L, 142L, 160L,
183L, 127L, 150L, 123L, 128L, 154L, 126L, 127L, 133L, 105L,
135L, 117L, 131L, 150L, 121L, 258L, 278L, 243L, 241L, 222L,
110L, 155L, 149L, 159L, 155L, 159L, 152L, 134L, 133L, 198L,
164L, 138L, 127L, 156L, 141L, 129L, 135L, 153L, 148L, 136L,
133L, 158L, 137L, 133L, 132L, 117L, 134L, 150L, 145L, 145L,
135L, 150L, 157L, 145L, 136L, 127L, 133L, 121L, 143L, 134L,
255L, 126L, 137L, 201L, 182L, 150L, 118L, 157L, 160L, 143L,
150L, 142L, 194L, 186L, 156L, 151L, 177L, 155L, 128L, 128L,
145L, 133L, 130L, 121L, 125L, 135L, 147L, 121L, 135L, 167L,
155L, 148L, 144L, 137L, 135L, 150L, 162L, 151L, 156L, 151L,
160L, 166L, 150L, 122L, 146L, 152L, 162L, 162L, 122L, 144L,
147L, 145L, 142L, 150L, 145L, 121L, 137L, 117L, 140L, 142L,
134L, 140L, 134L, 131L, 136L, 116L, 135L, 127L, 129L, 185L,
46L, 220L, 142L, 152L, 127L, 45L, 47L, 45L, 54L, 51L, 56L,
49L, 49L, 58L, 49L, 51L, 45L, 47L, 44L, 69L, 57L, 48L, 52L,
60L, 40L, 51L, 46L, 43L, 49L, 43L, 47L, 45L, 56L, 46L, 48L,
46L, 49L, 48L, 50L, 66L, 49L, 59L, 47L, 59L, 50L, 43L, 53L,
48L, 56L, 44L, 52L, 42L, 51L, 44L, 51L, 53L, 40L, 50L, 50L,
34L, 48L, 51L, 51L, 46L, 47L, 53L, 32L), uniqueID = 1:1282), row.names = c(NA,
-1282L), class = "data.frame")
I am trying to transform this data from long to wide format. I have been trying to do this by using:
df1<- df %>% group_by(length) %>% pivot_wider(names_from=site, values_from=length) %>% select(-uniqueID)
It gives me an output, but it has a lot of NA's. How do I make it look like table 1 and not table 2? Thanks in advance!
You probably need an ID for each combination of year and site. Please test the following to see if this is what you need.
library(tidyverse)
df1 <- df %>%
select(-uniqueID) %>%
group_by(year, site) %>%
mutate(ID = 1:n()) %>%
pivot_wider(names_from=site, values_from=length)
entrezid_downgene=structure(list(SYMBOL = c("ARHGEF16", "ILDR1", "TMPRSS4", "MAP7", "SERINC2", "C9orf152", "TSPAN1", "RHEX", "TMC4", "CRB3", "UGT8", "CD24", "MAPK13", "AGR2", "GJB1", "ERBB3", "CNDP2", "LOC105378644", "GCNT3", "CEACAM1", "GPR160", "PRSS8", "HOOK1", "ABHD17C", "MOCOS", "CWH43", "EHF", "ACSL5", "SLC44A4", "RAP1GAP", "MUC13", "PPM1H", "ATP2C2", "RAB25", "H2BC5", "H4C12", "TJP3", "RXFP1", "GSTO2", "OVOL2", "TMEM125", "LIMS1", "DLX5", "ST6GALNAC1", "HNF1B", "STX19", "F2RL1", "MT1G", "PLPP2", "TMEM238", "SLC30A2", "GABRP", "EPCAM", "CLDN10", "HOXB5", "PRAME", "MAL2", "PLA2G10", "TSPAN12", "FAM174B", "TMC5", "ASRGL1", "SCNN1A", "FOXL2", "ALDH3B2", "ELF3", "SLC7A1", "MT1F", "CLDN3", "SPINT2", "SFN", "VWC2", "C9orf116", "SLC39A6", "TCN1", "IL20RA", "ACSM3", "FOXL2NB", "HGD", "PAX8", "IDO1", "C4BPA", "RHPN2", "HMGCR", "UGT2B11", "PIGR", "MUC20", "SLC3A1", "PLLP", "PSAT1", "SCGB2A1", "WNT5A", "DEFB1", "FGL1", "SLC2A8", "HOXB8", "CYP2J2", "WWC1", "MUC1", "PRKX", "RASEF", "BAIAP2L2", "PAPSS1", "MME", "HOMER2", "STRA6", "ARG2", "MOGAT1", "CDS1", "SCGB2A2", "MPZL2", "PHYHIPL", "INAVA", "IDO2", "GALNT4", "TMEM101", "HSD17B2", "AOC1", "CDCA7", "CAPS", "TFCP2L1", "PAEP", "PLAC9P1", "GAL", "RORB", "CCNO", "XDH", "C15orf48", "SLC1A1", "GPT2", "VNN1", "NWD1", "HABP2", "UGT2B7", "CYP26A1", "MSX1", "ENPP3", "KIR2DL3", "ADAMTS9", "KIR2DL4", "BRINP1", "PROM1", "APCDD1", "AGR3", "EYA2", "SLC2A1", "GNLY", "COL7A1", "FOXJ1", "MS4A8", "C20orf85", "RSPH1", "SCGB1D2", "SPP1", "RASD1", "CST1", "SCGB1D4", "LEFTY1", "LAMC3", "TEKT1", "LCN2", "VTCN1", "IRX3", "ROPN1L", "FAM183A", "NDP", "TUBB3", "DIO2", "IL2RB", "ADAMTS8", "SERPINA5", "NKG7", "ABCC8", "STC1", "LRRC26"),
ENTREZID = c("27237", "286676", "56649", "9053", "347735", "401546", "10103", "440712", "147798", "92359", "7368", "100133941", "5603", "10551", "2705", "2065", "55748", "105378644", "9245", "634", "26996", "5652", "51361", "58489", "55034", "80157", "26298", "51703", "80736", "5909", "56667", "57460", "9914", "57111", "3017", "8362", "27134", "59350", "119391", "58495", "128218", "3987", "1749", "55808", "6928", "415117", "2150", "4495", "8612", "388564", "7780", "2568", "4072", "9071", "3215", "23532", "114569", "8399", "23554", "400451", "79838", "80150", "6337", "668", "222", "1999", "6541", "4494", "1365", "10653", "2810", "375567", "138162", "25800", "6947", "53832", "6296", "401089", "3081", "7849", "3620", "722", "85415", "3156", "10720", "5284", "200958", "6519", "51090", "29968", "4246", "7474", "1672", "2267", "29988", "3218", "1573", "23286", "4582", "5613", "158158", "80115", "9061", "4311", "9455", "64220", "384", "116255", "1040", "4250", "10205", "84457", "55765", "169355", "8693", "84336", "3294", "26", "83879", "828", "29842", "5047", "389033", "51083", "6096", "10309", "7498", "84419", "6505", "84706", "8876", "284434", "3026", "7364", "1592", "4487", "5169", "3804", "56999", "3805", "1620", "8842", "147495", "155465", "2139", "6513", "10578", "1294", "2302", "83661", "128602", "89765", "10647", "6696", "51655", "1469", "404552", "10637", "10319", "83659", "3934", "79679", "79191", "83853", "440585", "4693", "10381", "1734", "3560", "11095", "5104", "4818", "6833", "6781", "389816")),
row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L), class = "data.frame")
down_ekk <- enrichKEGG(gene= c(entrezid_downgene$ENTREZID),
organism = 'hsa',
pvalueCutoff = 0.05,
minGSSize = 50,
maxGSSize = 500,
)
dot <- dotplot(down_ekk,font.size=6,title='down_kegg')
dot
Error in ans[ypos] <- rep(yes, length.out = len)[ypos] : Change the
parameter length to zero Warning message: In rep(yes, length.out =
len) : 'x' is NULL so the result will be NULL
please How to solve the error?
This is normal you can't plot the dotplot because you have no significant ontologies.
You can check with down_ekk :
downekk
#
# over-representation test
#
#...#organism hsa
#...#ontology KEGG
#...#keytype kegg
#...#gene chr [1:175] "27237" "286676" "56649" "9053" "347735" "401546" "10103" "440712" "147798" "92359" "7368" "100133941" "5603" "10551" "2705" "2065" "55748" "105378644" "9245" "634" "26996" "5652" ...
#...pvalues adjusted by 'BH' with cutoff <0.05
#...0 enriched terms found
#...Citation
Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
clusterProfiler: an R package for comparing biological themes among
gene clusters. OMICS: A Journal of Integrative Biology
2012, 16(5):284-287
"0 enriched terms found" so this is why you get the error as no dotplot can be plotted
I am trying to use case_when in order to pad out a string in R, dependent on the string length.
I take the following 3 examples with lengths 11, 12 and 13:
V1 V2
74300000330 00074300000330
811693200042 08011693200042
8829999820128 88029999820128
V1 is the column I am trying to match with V2
The first row in V1 has 11 digits, if the row has 11 digits then add 3 zeros at the begining of the number.
I have tried the following code without any luck (I have also tried it with paste0());
df %>%
mutate(col3 = case_when(length(col1) == 11 ~ str_pad(14, width = 3, pad = "0")))
The second has 12 digits, so I should add one zero at the begining of the number and then another zero between (counting from the left) the first digit and (counting from right) 11th digit, so row 2 would go from 81169... to 0801169....
The third row has 13 digits so I should paste a zero between the (counting from the left) 2nd digit and (counting from the right) the 11th digit. So the begining of the sequence goes from 88299 to 880299.
The total number of digits in the sequence should be exactly 14.
Data:
df <- structure(list(col1 = structure(c(1L, 1L, 1L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 11L, 12L, 12L, 13L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L,
21L, 21L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 26L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 29L, 30L, 30L, 30L,
31L, 32L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L, 36L,
36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 38L, 38L, 38L, 38L,
38L, 39L, 39L, 39L, 39L, 40L, 41L, 41L, 41L, 42L, 42L, 43L, 44L,
45L, 45L, 45L, 45L, 45L, 46L, 46L, 47L, 47L, 47L, 47L, 47L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L,
47L, 48L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 51L, 51L, 51L, 51L, 51L, 51L, 51L,
51L, 51L, 51L, 51L, 52L, 52L, 53L, 53L, 53L, 53L, 54L, 55L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 57L, 58L, 59L, 59L, 60L, 60L,
60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 61L, 61L, 61L, 61L,
61L, 62L, 62L, 63L, 64L, 65L, 66L, 66L, 66L, 66L, 66L, 66L, 66L,
66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 67L, 67L, 68L,
68L, 69L, 69L, 69L, 70L, 70L, 70L, 70L, 70L, 70L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 72L, 72L,
72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L,
73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 74L,
74L, 74L, 74L, 74L, 75L, 75L, 75L, 76L, 77L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 83L, 83L, 83L, 83L, 83L, 83L, 83L, 83L, 84L, 84L,
84L, 85L, 86L, 86L, 87L, 87L, 87L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 93L, 93L, 94L, 94L, 95L, 95L, 95L, 95L, 95L, 96L, 97L, 97L,
97L, 98L, 99L, 100L, 100L, 100L, 100L, 101L, 102L, 102L, 103L,
104L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 106L,
107L, 107L, 108L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 111L, 111L, 111L,
111L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 113L, 113L, 113L,
113L, 113L, 113L, 114L, 114L, 114L, 114L, 114L, 114L, 114L, 114L,
115L, 116L, 116L, 117L, 117L, 117L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 119L, 119L, 119L, 119L, 119L, 119L,
119L, 119L, 119L, 120L, 120L, 120L, 121L, 122L, 122L, 122L, 122L,
122L, 122L, 122L, 123L, 123L, 123L, 123L, 123L, 123L, 123L), .Label = c("11114110010",
"11114110022", "11114110029", "11114110036", "11114110210", "11114110230",
"11114110261", "11114110271", "11114110281", "11114110291", "11114110316",
"11114110526", "11780900029", "11780900050", "11780900660", "11780900661",
"12451500878", "12451567602", "12550000033", "12550000365", "12550000366",
"12550000367", "12550000371", "12550000376", "12550000377", "12550000384",
"12550000388", "12550000392", "12550000393", "12550000397", "12550000401",
"12550000402", "12550000538", "12550006763", "12550006764", "12550020040",
"12550020042", "12550020043", "12550020044", "12550020188", "12550020204",
"12550020212", "12550090015", "12800046631", "12800063141", "12800070612",
"14300002922", "14300002923", "14300002924", "14300002925", "14300002934",
"14300002940", "14300002941", "14300002942", "14300003300", "14300004091",
"14300004296", "14300004299", "14300004301", "14300004648", "14300004650",
"14300004651", "14300070522", "15543760143", "15543760145", "15543760186",
"15543760235", "15543760253", "17089302817", "17103800044", "17103800047",
"17103800048", "17103800053", "17103800056", "17103800058", "17103800059",
"17103801173", "17103801175", "17232305018", "17447100091", "17510100575",
"17510100576", "17510121064", "17510121065", "17510181458", "17732447059",
"17762300048", "17762300060", "18903644280", "19955508003", "19955508050",
"19955508060", "19955508061", "19955508531", "19955508534", "19955508758",
"19955508792", "19955508800", "19955508801", "19955508832", "19955508992",
"19955509803", "19955538570", "19955538696", "19955538725", "19955538792",
"21291912261", "21780900078", "22550081121", "22550081122", "22800025406",
"22800030050", "24300070590", "25543760142", "25543760521", "29955539550",
"31291912240", "39955508520", "41114110525", "57103800060", "74300000330",
"8,11693E+11", "8,83E+12"), class = "factor"), col2 = structure(c(1L,
1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 12L, 12L, 13L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L,
26L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L,
28L, 29L, 30L, 30L, 30L, 31L, 32L, 33L, 33L, 33L, 33L, 33L, 34L,
34L, 34L, 34L, 35L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L,
37L, 38L, 38L, 38L, 38L, 38L, 39L, 39L, 39L, 39L, 40L, 41L, 41L,
41L, 42L, 42L, 43L, 44L, 45L, 45L, 45L, 45L, 45L, 46L, 46L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L, 47L,
47L, 47L, 47L, 47L, 47L, 47L, 48L, 49L, 49L, 49L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 51L, 51L,
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 52L, 52L, 53L, 53L,
53L, 53L, 54L, 55L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 57L,
58L, 59L, 59L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L, 60L,
60L, 61L, 61L, 61L, 61L, 61L, 62L, 62L, 63L, 64L, 65L, 66L, 66L,
66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L,
66L, 66L, 67L, 67L, 68L, 68L, 69L, 69L, 69L, 70L, 70L, 70L, 70L,
70L, 70L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 72L,
72L, 72L, 72L, 72L, 72L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L,
73L, 73L, 73L, 73L, 74L, 74L, 74L, 74L, 74L, 75L, 75L, 75L, 76L,
77L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 83L, 83L, 83L, 83L, 83L,
83L, 83L, 83L, 84L, 84L, 84L, 85L, 86L, 86L, 87L, 87L, 87L, 87L,
88L, 89L, 90L, 91L, 92L, 93L, 93L, 93L, 94L, 94L, 95L, 95L, 95L,
95L, 95L, 96L, 97L, 97L, 97L, 98L, 99L, 100L, 100L, 100L, 100L,
101L, 102L, 102L, 103L, 104L, 105L, 105L, 105L, 105L, 105L, 105L,
105L, 105L, 105L, 106L, 107L, 107L, 108L, 109L, 109L, 109L, 109L,
109L, 109L, 109L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 111L, 111L, 111L, 111L, 112L, 112L, 112L, 112L, 112L, 112L,
112L, 113L, 113L, 113L, 113L, 113L, 113L, 114L, 114L, 114L, 114L,
114L, 114L, 114L, 114L, 115L, 116L, 116L, 117L, 117L, 117L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 119L, 119L,
119L, 119L, 119L, 119L, 119L, 119L, 119L, 120L, 120L, 120L, 121L,
123L, 122L, 123L, 123L, 123L, 123L, 123L, 127L, 124L, 126L, 126L,
127L, 127L, 125L), .Label = c("00011114110010", "00011114110022",
"00011114110029", "00011114110036", "00011114110210", "00011114110230",
"00011114110261", "00011114110271", "00011114110281", "00011114110291",
"00011114110316", "00011114110526", "00011780900029", "00011780900050",
"00011780900660", "00011780900661", "00012451500878", "00012451567602",
"00012550000033", "00012550000365", "00012550000366", "00012550000367",
"00012550000371", "00012550000376", "00012550000377", "00012550000384",
"00012550000388", "00012550000392", "00012550000393", "00012550000397",
"00012550000401", "00012550000402", "00012550000538", "00012550006763",
"00012550006764", "00012550020040", "00012550020042", "00012550020043",
"00012550020044", "00012550020188", "00012550020204", "00012550020212",
"00012550090015", "00012800046631", "00012800063141", "00012800070612",
"00014300002922", "00014300002923", "00014300002924", "00014300002925",
"00014300002934", "00014300002940", "00014300002941", "00014300002942",
"00014300003300", "00014300004091", "00014300004296", "00014300004299",
"00014300004301", "00014300004648", "00014300004650", "00014300004651",
"00014300070522", "00015543760143", "00015543760145", "00015543760186",
"00015543760235", "00015543760253", "00017089302817", "00017103800044",
"00017103800047", "00017103800048", "00017103800053", "00017103800056",
"00017103800058", "00017103800059", "00017103801173", "00017103801175",
"00017232305018", "00017447100091", "00017510100575", "00017510100576",
"00017510121064", "00017510121065", "00017510181458", "00017732447059",
"00017762300048", "00017762300060", "00018903644280", "00019955508003",
"00019955508050", "00019955508060", "00019955508061", "00019955508531",
"00019955508534", "00019955508758", "00019955508792", "00019955508800",
"00019955508801", "00019955508832", "00019955508992", "00019955509803",
"00019955538570", "00019955538696", "00019955538725", "00019955538792",
"00021291912261", "00021780900078", "00022550081121", "00022550081122",
"00022800025406", "00022800030050", "00024300070590", "00025543760142",
"00025543760521", "00029955539550", "00031291912240", "00039955508520",
"00041114110525", "00057103800060", "00074300000330", "08011693200041",
"08011693200042", "88029999819907", "88029999820074", "88029999820083",
"88029999820128"), class = "factor")), row.names = c(NA, -513L
), class = "data.frame")
A few issues here. Your columns appear to be factors, which can create confusing problems when you apply string functions to them. You want them to be character, not factor. The correct way to check the length of a string is with nchar (spoiler alert: does not work with factor data!).
Your rules for padding seem a little arbitrary, but the following should work. For padding "within" the digit string, gsub and regular expressions work wonders.
df2 <- mutate_at(df, vars(col1, col2), as.character) %>%
mutate(col3 = case_when(
nchar(col1) == 11 ~ str_pad(col1, width = 14, pad = '0'),
nchar(col1) == 12 ~ gsub('(\\d)(\\d+)', '0\\10\\2', col1),
nchar(col1) == 13 ~ gsub('(\\d\\d)(\\d+)', '\\10\\2', col1),
T ~ col1
))
col1 col3
<chr> <chr>
1 74300000330 00074300000330
2 811693200042 08011693200042
3 8829999820128 88029999820128
My basic idea is to compute the Means of chunks (column-wise) of a large matrix and store these Means as rows of a data frame. Note, the chunks have different sizes (number of rows) and these are stored in a vector vec1. Below is my code:
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.: When I perform the computations manually without looping it works. But the result of the loop yields a 4000 * 3 matrix filled with NA apart from the first row.
vec1 is a vector with 4000 entries, and whose first 500 elements - head(vec1, 500) -are below:
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,
144L, 44L, 19L, 189L, 258L, 76L, 13L, 44L, 20L, 152L, 133L, 86L,
32L, 1L, 56L, 140L, 65L, 74L, 131L, 155L, 40L, 40L, 112L, 186L,
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,
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122L, 155L, 36L, 4L, 60L, 39L, 25L, 2L, 182L, 105L, 157L, 5L,
70L, 16L, 55L, 52L, 39L, 156L, 14L, 118L, 88L, 91L, 132L, 52L,
18L, 38L, 31L, 35L, 75L, 186L, 45L, 110L, 232L, 52L, 135L, 33L,
11L, 29L, 129L, 147L, 20L, 20L, 59L, 46L, 6L, 53L, 251L, 120L,
192L, 41L, 87L, 38L, 134L, 5L, 120L, 130L, 71L, 121L, 84L, 183L,
166L, 20L, 8L, 20L, 74L, 201L, 35L, 176L, 189L, 17L, 231L, 48L,
38L, 3L, 142L, 53L, 199L, 135L, 6L, 38L, 256L, 76L, 6L, 56L,
154L, 25L, 76L, 69L, 149L, 107L, 113L, 246L, 61L, 23L, 6L, 76L,
3L, 68L, 70L, 89L, 130L, 226L, 31L, 157L, 24L, 80L, 170L, 169L,
64L, 12L, 110L, 47L, 141L, 159L, 22L, 53L, 167L, 61L, 81L, 98L,
172L, 261L, 99L, 9L, 13L, 132L, 103L, 16L, 97L, 186L, 35L, 128L,
73L, 136L, 62L, 187L, 30L, 31L, 26L, 115L, 76L, 260L, 54L, 11L,
169L, 227L, 43L, 6L, 23L, 212L, 23L, 68L, 119L, 181L, 34L, 137L,
144L, 48L, 101L, 25L, 10L, 92L, 5L, 92L, 132L, 206L, 44L, 113L,
9L, 25L, 249L, 69L, 250L, 67L, 35L, 6L, 60L, 251L, 6L, 32L, 94L,
13L, 224L, 21L, 43L, 81L, 9L, 9L, 95L, 11L, 7L, 26L, 172L, 46L,
17L, 3L, 2L, 39L, 26L, 7L, 18L, 57L, 88L, 16L, 47L, 136L, 135L,
73L, 26L, 60L, 56L, 77L, 158L, 23L, 1L, 139L, 234L, 76L, 99L,
28L, 22L, 83L, 114L, 6L, 122L, 7L, 36L, 59L, 4L, 33L, 79L, 25L,
26L, 8L, 28L, 19L, 33L, 2L, 23L, 44L, 158L, 56L, 14L, 8L, 56L,
16L, 36L, 90L, 18L, 22L, 7L, 74L, 70L, 2L, 51L, 13L, 130L, 25L,
17L, 23L, 48L, 37L, 60L, 17L, 58L, 15L, 41L, 261L, 245L, 35L,
17L, 41L, 234L, 13L, 11L, 192L, 3L, 5L, 29L, 14L, 34L, 4L, 110L,
63L, 47L, 157L, 9L, 116L, 120L, 29L, 126L, 26L, 106L, 219L, 209L,
93L, 255L, 137L, 88L, 96L, 87L, 229L, 23L, 128L, 101L, 62L, 2L,
193L, 58L, 1L, 8L, 146L, 44L, 12L, 27L, 99L, 270L, 54L, 41L,
161L, 231L, 53L, 126L, 139L, 77L, 55L, 32L, 6L, 159L, 131L, 54L,
266L, 87L, 13L, 205L, 154L, 3L, 82L, 35L, 11L, 2L, 56L, 84L,
110L, 116L, 28L, 30L, 60L, 74L, 12L, 147L, 31L, 206L, 31L, 56L,
209L, 115L, 149L, 33L, 198L, 205L, 71L, 28L, 40L, 201L, 32L,
3L, 40L, 75L, 91L, 32L, 9L, 4L, 192L, 11L, 41L, 30L, 46L, 57L,
44L, 243L, 67L, 118L, 108L, 181L, 83L, 45L, 93L, 13L, 2L, 104L,
163L, 92L, 8L, 17L, 14L, 150L, 5L, 60L, 123L, 100L, 105L, 110L,
225L, 249L, 207L, 100L, 188L, 138L, 6L, 176L, 68L, 91L, 8L, 20L,
18L, 21L, 79L, 20L, 4L, 99L, 136L, 28L, 156L, 7L, 36L, 226L,
33L, 42L, 1L, 28L, 227L, 11L, 9L, 157L, 206L, 34L, 17L, 61L,
113L, 112L, 158L, 24L, 18L, 36L, 75L, 40L, 18L, 183L, 3L, 37L,
92L, 69L, 13L, 213L, 48L, 163L, 188L, 251L, 59L, 75L, 1L, 12L,
46L, 232L, 13L, 74L, 32L, 149L, 219L, 22L, 59L, 109L, 264L, 25L,
141L, 5L, 67L, 41L, 5L, 71L, 19L, 63L, 114L, 28L, 76L, 80L, 86L,
71L, 18L, 166L, 40L, 57L, 185L, 88L, 115L)
The problem is that you initially created 4000 * 3 data.frame filled in with NA. Please see the corrected code. I did not put your actual data vec1 (too long) and simulated vec1 with sampling from exponential distribution. Additionally I used colMeans as more effective than apply. See the code below:
# 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
Output:
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
I have the following piece of my data-set:
> dput(test)
structure(list(X2002.06.26 = structure(c(99L, 88L, 65L, 94L,
60L, 101L, 27L, 83L, 16L, 12L, 54L, 97L, 63L, 41L, 13L, 2L, 58L,
9L, 82L, 22L, 14L, 77L, 55L, 32L, 45L, 80L, 39L, 70L, 114L, 103L,
69L, 104L, 106L, 108L, 38L, 10L, 64L, 1L, 112L, 102L, 67L, 98L,
66L, 19L, 81L, 72L, 89L, 23L, 48L, 4L, 25L, 91L, 26L, 62L, 33L,
3L, 28L, 57L, 17L, 20L, 73L, 78L, 90L, 84L, 5L, 92L, 43L, 74L,
75L, 93L, 100L, 56L, 36L, 79L, 111L, 52L, 24L, 105L, 29L, 53L,
110L, 71L, 18L, 8L, 34L, 50L, 109L, 61L, 35L, 21L, 11L, 47L,
59L, 51L, 113L, 44L, 30L, 42L, 107L, 7L, 87L, 6L, 68L, 96L, 86L,
15L, 46L, 85L, 31L, 49L, 40L, 76L, 95L, 115L, 37L), .Label = c("BMG4388N1065",
"BMG812761002", "GB00BYMT0J19", "IE00BLS09M33", "IE00BQRQXQ92",
"US0003611052", "US0015471081", "US0025671050", "US0028962076",
"US0044981019", "US0116591092", "US01741R1023", "US0185223007",
"US01988P1084", "US0305061097", "US0311001004", "US03662Q1058",
"US0375981091", "US0383361039", "US03836W1036", "US03937C1053",
"US0396701049", "US0462241011", "US06652V2088", "US0997241064",
"US1033041013", "US1096961040", "US1170431092", "US1250711009",
"US1258961002", "US12686C1099", "US1311931042", "US1416651099",
"US1423391002", "US1431301027", "US1564311082", "US1718711062",
"US1778351056", "US2193501051", "US2289031005", "US23331A1097",
"US2537981027", "US2829141009", "US2925621052", "US2966891028",
"US3116421021", "US34354P1057", "US3498531017", "US3693851095",
"US3984331021", "US3989051095", "US4158641070", "US4222451001",
"US4285671016", "US4586653044", "US4835481031", "US5261071071",
"US5367971034", "US5463471053", "US55305B1017", "US5535301064",
"US5562691080", "US5663301068", "US5871181005", "US59001A1025",
"US6081901042", "US62914B1008", "US6517185046", "US6900701078",
"US6907684038", "US6936561009", "US7081601061", "US7132781094",
"US7234561097", "US7310681025", "US7415034039", "US7496851038",
"US7549071030", "US7595091023", "US76009N1000", "US7703231032",
"US7811821005", "US7835491082", "US8081941044", "US8308791024",
"US83088M1027", "US83545G1022", "US8354951027", "US8528572006",
"US8545021011", "US85590A4013", "US8581191009", "US8589121081",
"US8681571084", "US8685361037", "US8712371033", "US8793691069",
"US8799391060", "US8832031012", "US8851601018", "US8865471085",
"US8873891043", "US88830M1027", "US8968181011", "US89785X1019",
"US8990355054", "US90385D1072", "US9134831034", "US9202531011",
"US92552R4065", "US9410531001", "US9427491025", "US9433151019",
"US9633201069", "US9837721045"), class = "factor"), X2002.06.27 = structure(c(57L,
43L, 73L, 70L, 35L, 114L, 58L, 88L, 55L, 7L, 72L, 28L, 16L, 84L,
110L, 44L, 75L, 20L, 99L, 18L, 10L, 80L, 113L, 52L, 66L, 36L,
60L, 101L, 107L, 103L, 34L, 22L, 81L, 40L, 1L, 46L, 108L, 106L,
91L, 37L, 98L, 9L, 104L, 115L, 54L, 100L, 42L, 2L, 3L, 26L, 21L,
71L, 23L, 62L, 50L, 97L, 11L, 94L, 27L, 53L, 79L, 4L, 51L, 76L,
49L, 78L, 87L, 32L, 59L, 96L, 13L, 86L, 15L, 48L, 109L, 29L,
85L, 68L, 17L, 41L, 64L, 31L, 8L, 38L, 90L, 45L, 12L, 56L, 6L,
39L, 92L, 63L, 5L, 82L, 19L, 89L, 69L, 74L, 25L, 95L, 105L, 61L,
67L, 14L, 112L, 111L, 102L, 83L, 93L, 33L, 30L, 47L, 65L, 24L,
77L), .Label = c("CH0044328745", "GB00BVVBC028", "LR0008862868",
"US0003611052", "US0010841023", "US0044981019", "US0079731008",
"US0116591092", "US0305061097", "US0311001004", "US0383361039",
"US03937C1053", "US0462241011", "US06652V2088", "US0733021010",
"US0952291005", "US0997241064", "US1096411004", "US1096961040",
"US1265011056", "US12686C1099", "US1311931042", "US1431301027",
"US1564311082", "US1628251035", "US1630721017", "US1897541041",
"US2017231034", "US23331A1097", "US2829141009", "US2925621052",
"US29444U7000", "US2974251009", "US3024913036", "US3138551086",
"US34354P1057", "US3596941068", "US3693851095", "US3719011096",
"US3825501014", "US3984331021", "US3989051095", "US4108671052",
"US4130861093", "US4158641070", "US4456581077", "US4586653044",
"US4606901001", "US48666K1097", "US5006432000", "US5053361078",
"US5138471033", "US5179421087", "US5246601075", "US5260571048",
"US5463471053", "US5526761086", "US5535301064", "US5663301068",
"US5766901012", "US59001A1025", "US6117421072", "US63935N1072",
"US6515871076", "US67066G1040", "US6795801009", "US6819191064",
"US6900701078", "US6907684038", "US6935061076", "US6936561009",
"US6951561090", "US7004162092", "US73179P1066", "US7376301039",
"US7401891053", "US74762E1029", "US7496851038", "US7549071030",
"US7757111049", "US7811821005", "US8305661055", "US8308791024",
"US8335511049", "US83545G1022", "US8354951027", "US8358981079",
"US8545021011", "US85590A4013", "US86732Y1091", "US8681681057",
"US8712371033", "US87305R1095", "US8799391060", "US8851601018",
"US88830M1027", "US8894781033", "US8962391004", "US8968181011",
"US89785X1019", "US9022521051", "US90385D1072", "US9046772003",
"US9111631035", "US9134831034", "US92552R4065", "US92552V1008",
"US9258151029", "US9292361071", "US9410531001", "US9427491025",
"US9433151019", "US9699041011", "US9746371007", "US9807451037"
), class = "factor")), .Names = c("X2002.06.26", "X2002.06.27"
), class = "data.frame", row.names = c(NA, -115L))
The actual data extends over 3000+ columns and there are approximately 1150 unique values.
I need to count how many times each of these values appears in the Data-Set.
We can try to flat the elements in the data frame first, then apply the table() method:
tab1 <- table(do.call(c, lapply(df, as.character)))
Another option is to convert the data frame to matrix then apply table method:
tab2 <- table(as.matrix(df))
identical(tab1, tab2)
[1] TRUE