Replace numbers with Boolean in r - r
I want to use weka to predict future instances. I have a csv file and a small portion of it is as following:
I used r to read the file but I am not sure how to substitute the zeros with "No error" and anything besides zero to "Error". I would have left like this but unfortunately weka is not able to predict instances with numbers as the status.
Edit 2: I tried your solution and even though it changed the numbbers to error/no error, it erased the other columns. Did I do something wrong? I also wrote it to a file so it would be easier to see.
Edit 3:
dput(data)
structure(list(BoxType = structure(c(3L, 3L, 6L, 6L, 3L, 8L,
3L, 3L, 6L, 4L, 4L, 3L, 3L, 4L, 6L, 6L, 3L, 6L, 2L, 4L, 3L, 3L,
8L, 3L, 6L, 8L, 2L, 3L, 8L, 8L, 3L, 8L, 2L, 2L, 8L, 8L, 2L, 3L,
8L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 6L, 4L, 3L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 4L, 2L, 6L, 6L, 4L, 4L, 4L, 6L, 3L, 4L, 6L, 3L,
3L, 2L, 2L, 6L, 3L, 3L, 3L, 2L, 6L, 8L, 3L, 8L, 3L, 4L, 3L, 8L,
6L, 2L, 6L, 6L, 3L, 3L, 4L, 3L, 4L, 4L, 2L, 4L, 2L, 3L, 2L, 6L,
3L, 3L, 4L, 3L, 3L, 6L, 3L, 6L, 3L, 3L, 4L, 6L, 4L, 4L, 3L, 4L,
4L, 2L, 6L, 2L, 6L, 6L, 3L, 3L, 4L, 3L, 4L, 6L, 3L, 4L, 6L, 6L,
4L, 4L, 3L, 6L, 4L, 4L, 3L, 4L, 6L, 3L, 6L, 2L, 3L, 2L, 2L, 6L,
4L, 4L, 3L, 6L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 6L, 6L,
6L, 3L, 2L, 3L, 3L, 4L, 4L, 3L, 6L, 3L, 4L, 3L, 3L, 3L, 3L, 8L,
6L, 3L, 6L, 2L, 8L, 2L, 3L, 3L, 6L, 3L, 2L, 2L, 3L, 4L, 6L, 2L,
6L, 3L, 3L, 4L, 6L, 3L, 4L, 4L, 4L, 2L, 4L, 3L, 6L, 3L, 3L, 3L,
4L, 3L, 3L, 2L, 4L, 3L, 3L, 3L, 2L, 3L, 4L, 6L, 3L, 3L, 3L, 2L,
3L, 6L, 3L, 3L, 3L, 3L, 3L, 6L, 8L, 4L, 3L, 3L, 2L, 7L, 8L, 6L,
6L, 4L, 6L, 8L, 3L, 2L, 4L, 4L, 6L, 3L, 2L, 6L, 8L, 4L, 6L, 4L,
4L, 4L, 3L, 3L, 3L, 6L, 4L, 4L, 3L, 3L, 3L, 6L, 6L, 3L, 6L, 6L,
6L, 4L, 4L, 3L, 2L, 4L, 3L, 6L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 3L,
4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 6L, 3L, 6L, 6L, 4L, 4L, 2L, 3L,
4L, 4L, 4L, 6L, 6L, 4L, 4L, 4L, 4L, 2L, 3L, 4L, 4L, 3L, 4L, 4L,
4L, 3L, 6L, 3L, 6L, 3L, 6L, 3L, 6L, 6L, 3L, 3L, 3L, 3L, 3L, 6L,
6L, 3L, 6L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 6L, 3L,
3L, 3L, 2L, 4L, 4L, 3L, 6L, 3L, 2L, 7L, 3L, 3L, 3L, 2L, 3L, 2L,
3L, 4L, 2L, 2L, 3L, 4L, 3L, 3L, 4L, 1L, 6L, 3L, 2L, 3L, 3L, 7L,
4L, 4L, 3L, 2L, 4L, 2L, 4L, 3L, 3L, 3L, 3L, 6L, 2L, 4L, 2L, 4L,
4L, 4L, 3L, 3L, 3L, 6L, 6L, 3L, 7L, 6L, 3L, 3L, 3L, 4L, 4L, 3L,
6L, 4L, 3L, 7L, 4L, 6L, 6L, 2L, 2L, 4L, 3L, 4L, 4L, 2L, 4L, 4L,
7L, 3L, 4L, 6L, 4L, 6L, 3L, 2L, 3L, 3L, 4L, 4L, 2L, 4L, 3L, 4L,
3L, 3L, 4L, 6L, 2L, 2L, 6L, 6L, 6L, 2L, 3L, 4L, 4L, 3L, 8L, 6L,
4L, 4L, 3L, 3L, 5L, 6L, 2L, 3L, 4L, 8L, 6L, 8L, 4L, 4L, 7L, 4L,
6L, 8L, 4L, 2L, 6L, 6L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 2L,
6L, 8L, 4L, 3L, 1L, 6L, 6L, 1L, 1L, 1L, 4L, 4L, 8L, 3L, 3L, 2L,
2L, 4L, 8L, 6L, 4L, 8L, 3L, 3L, 3L, 5L, 4L, 1L, 2L, 2L, 3L, 4L,
2L, 5L, 4L, 8L, 3L, 8L, 2L, 3L, 4L, 8L, 3L, 6L, 3L, 6L, 6L, 3L,
3L, 8L, 8L, 3L, 6L, 3L, 3L, 2L, 5L, 3L, 6L, 3L, 2L, 3L, 3L, 3L,
4L, 3L, 4L, 3L, 4L, 3L, 2L, 2L, 3L, 6L, 4L, 6L, 3L, 3L, 6L, 3L,
4L, 3L, 2L, 3L, 4L, 4L, 4L, 6L, 6L, 3L, 6L, 4L, 7L, 8L, 6L, 8L,
8L, 4L, 6L, 4L, 4L, 3L, 4L, 2L, 3L, 2L, 4L, 6L, 4L, 6L, 4L, 6L,
4L, 6L, 3L, 4L, 3L, 6L, 4L, 4L, 8L, 4L, 8L, 3L, 3L, 6L, 6L, 3L,
4L, 3L, 3L, 3L, 3L, 6L, 3L, 3L, 3L, 4L, 2L, 4L, 3L, 3L, 6L, 6L,
4L, 3L, 2L, 3L, 6L, 4L, 3L, 3L, 2L, 3L, 2L, 6L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 6L, 6L, 2L, 3L, 6L, 3L, 2L, 3L, 6L,
4L, 3L, 3L, 3L, 6L, 6L, 4L, 3L, 8L, 8L, 4L, 3L, 2L, 2L, 3L, 2L,
3L, 8L, 2L, 3L, 6L, 3L, 3L, 4L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 8L,
8L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 6L, 2L, 3L, 6L, 6L, 8L, 3L,
4L, 3L, 3L, 6L, 6L, 3L, 3L, 3L, 2L, 6L, 2L, 3L, 6L, 8L, 3L, 4L,
4L, 6L, 4L, 8L, 4L, 4L, 2L, 6L, 8L, 6L, 4L, 8L, 3L, 8L, 1L, 8L,
2L, 2L, 2L, 2L, 3L, 3L, 6L, 3L, 3L, 6L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 2L, 4L, 3L, 4L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L,
3L, 6L, 3L, 3L, 6L, 3L, 2L, 3L, 3L, 3L, 4L, 3L, 3L, 1L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 6L, 4L, 3L, 3L, 6L, 3L, 6L, 6L, 4L, 6L, 4L,
6L, 4L, 4L, 6L, 6L, 6L, 3L, 6L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
2L, 2L, 8L, 4L, 4L, 6L, 4L, 8L, 6L, 4L, 3L, 4L, 3L, 4L, 6L, 4L,
6L, 6L, 6L, 4L, 6L, 6L, 4L, 4L, 4L, 2L, 6L, 4L, 2L, 4L, 4L, 3L,
4L, 6L, 6L, 6L, 3L, 4L, 6L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
8L, 4L, 4L, 6L, 2L, 8L, 8L, 4L, 6L, 3L, 4L, 8L, 8L, 5L, 3L, 2L,
4L, 3L, 4L, 6L, 4L, 3L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 3L,
6L, 4L, 6L, 6L, 6L, 2L, 3L, 6L, 6L, 3L, 4L, 3L, 2L, 8L, 4L, 8L,
8L, 3L, 3L, 4L, 6L, 6L, 4L, 6L, 6L, 3L, 4L, 4L, 4L, 3L, 7L, 4L,
6L), .Label = c("", "IPH8005", "ISB7005", "VIP1200", "VIP1216",
"VIP1232", "VIP2262NA", "VIP2502W"), class = "factor"), BoxVendor = structure(c(2L,
2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L,
3L, 4L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 4L, 2L, 3L, 3L, 2L, 3L, 4L,
4L, 3L, 3L, 4L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 4L, 4L, 3L, 3L, 2L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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, 3L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 4L, 3L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 4L,
3L, 4L, 2L, 4L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 4L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 2L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L,
4L, 2L, 4L, 4L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 4L, 3L, 3L, 3L, 2L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 4L, 3L, 4L, 2L, 2L, 3L, 2L, 4L,
4L, 2L, 3L, 3L, 4L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 4L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 4L, 3L, 2L, 2L, 2L, 4L, 2L, 3L,
3L, 2L, 2L, 2L, 4L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L,
2L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 3L, 2L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 2L, 3L, 2L, 3L, 3L,
3L, 3L, 4L, 4L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L,
3L, 3L, 3L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 4L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 4L, 3L, 3L, 2L, 3L, 2L, 4L, 3L, 2L,
2L, 2L, 4L, 2L, 4L, 2L, 3L, 4L, 4L, 2L, 3L, 2L, 2L, 3L, 1L, 3L,
2L, 4L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 3L, 4L, 3L, 2L, 2L, 2L, 2L,
3L, 4L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 3L, 2L,
3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 4L, 2L, 2L, 3L,
3L, 4L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 4L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 4L, 2L, 2L, 4L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 1L, 3L,
3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L,
1L, 4L, 4L, 2L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 4L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 4L, 3L, 2L, 3L,
2L, 4L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 4L, 4L, 2L, 3L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 2L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 4L, 2L, 4L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 4L,
3L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 2L, 3L, 3L, 2L, 2L, 4L, 2L, 4L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 3L, 3L, 4L, 2L,
3L, 2L, 4L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
2L, 4L, 4L, 2L, 4L, 2L, 3L, 4L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 4L, 2L, 2L, 4L, 4L, 2L, 4L, 4L, 3L, 4L,
2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 4L, 3L, 4L,
2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 1L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 3L, 2L, 2L, 3L, 2L,
2L, 2L, 4L, 2L, 2L, 2L, 4L, 3L, 2L, 3L, 4L, 4L, 2L, 2L, 2L, 2L,
2L, 2L, 4L, 4L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 4L, 2L, 2L, 2L, 3L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L,
4L, 2L, 2L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L,
3L, 4L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 2L, 4L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 2L, 3L, 3L, 2L, 3L,
2L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 2L, 3L, 3L, 3L), .Label = c("", "CISCO", "MOTOROLA",
"PACE"), class = "factor"), Receiver_TotalVideoDecoderErrors = c(3L,
204L, 0L, 0L, 3393L, 909L, 1556L, 48L, 0L, 0L, 0L, 182L, 19L,
0L, 0L, 0L, 77L, 0L, 0L, 0L, 6L, 1002L, 10L, 0L, 0L, 6938L, 0L,
299L, 49L, 245L, 0L, 41L, 0L, 0L, 717L, 31L, 0L, 75L, 37L, 71L,
0L, 40L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1230L, 1230L, 1230L, 1230L, 1230L, 1230L, 1230L, 1230L,
1230L, 1230L, 1230L, 1230L, 1230L, 1230L, 1230L, 1230L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 22L, 0L, 0L, 1384L, 95L, 0L, 0L,
0L, 437L, 119L, 910L, 0L, 0L, 8679L, 20L, 68L, 7L, 0L, 0L, 16L,
0L, 0L, 0L, 0L, 74L, 1L, 0L, 82L, 0L, 0L, 0L, 0L, 0L, 21L, 0L,
0L, 279L, 40L, 0L, 1483L, 3L, 0L, 132L, 0L, 0L, 171L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 90L, 0L, 0L, 0L, 0L,
0L, 111L, 0L, 0L, 0L, 0L, 0L, 18L, 0L, 0L, 0L, 217L, 0L, 0L,
1687L, 0L, 0L, 25L, 0L, 0L, 0L, 0L, 0L, 60L, 0L, 0L, 7L, 0L,
0L, 0L, 0L, 1L, 20L, 0L, 0L, 0L, 0L, 0L, 230L, 0L, 169L, 0L,
0L, 0L, 889L, 0L, 3L, 0L, 48L, 2951L, 10L, 531L, 0L, 0L, 0L,
0L, 0L, 232L, 0L, 0L, 125L, 0L, 39L, 0L, 0L, 262L, 0L, 0L, 0L,
0L, 1270L, 6L, 0L, 0L, 88L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 297L,
124L, 419L, 0L, 483L, 280L, 0L, 0L, 127L, 93L, 368L, 0L, 209571L,
0L, 0L, 21L, 62L, 11L, 0L, 501L, 0L, 169L, 34L, 32L, 25L, 188L,
0L, 1596L, 0L, 41L, 183L, 0L, 805L, 3L, 0L, 0L, 0L, 0L, 297L,
90L, 0L, 0L, 0L, 0L, 691L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 23L,
52L, 0L, 0L, 0L, 0L, 58L, 18L, 93L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 9L, 0L, 0L, 11381L, 0L, 34L, 0L, 0L, 26L, 0L, 0L, 0L, 318L,
0L, 0L, 36L, 0L, 6534L, 22L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 18L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 35L, 0L, 0L,
30L, 0L, 0L, 0L, 51L, 0L, 7L, 0L, 84L, 0L, 9L, 0L, 0L, 48L, 65L,
23L, 0L, 60312L, 0L, 0L, 28L, 0L, 32L, 0L, 0L, 283L, 406L, 44L,
0L, 0L, 0L, 2L, 824L, 0L, 0L, 2487L, 95L, 0L, 0L, 0L, 0L, 0L,
56L, 0L, 1L, 4640L, 12L, 3626L, 0L, 0L, 0L, 420L, 0L, 0L, 0L,
49L, 0L, 78L, 8L, 0L, 0L, 0L, 380L, 0L, 0L, 7L, 1194L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 29L, 489L, 584L, 47L, 2L, 0L, 0L, 0L, 0L,
0L, 0L, 899L, 120L, 0L, 0L, 0L, 26L, 656L, 0L, 0L, 0L, 50L, 0L,
0L, 0L, 0L, 0L, 6L, 14L, 0L, 0L, 0L, 0L, 0L, 0L, 89L, 0L, 0L,
0L, 0L, 0L, 104L, 0L, 0L, 0L, 0L, 0L, 217L, 0L, 50L, 14L, 0L,
0L, 0L, 0L, 21L, 0L, 73L, 403L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
14L, 0L, 0L, 2769L, 5878L, 0L, 0L, 0L, 37L, 0L, 0L, 0L, 0L, 0L,
0L, 258L, 0L, 5560L, 0L, 0L, 722L, 0L, 0L, 707L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 95L, 0L, 0L, 13L, 0L, 37L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 150L, 65L, 0L, 0L, 0L, 78L,
0L, 0L, 0L, 4L, 10L, 25L, 0L, 0L, 0L, 0L, 0L, 13L, 0L, 0L, 0L,
0L, 0L, 47L, 103L, 0L, 9L, 0L, 116L, 27L, 0L, 7L, 0L, 0L, 6L,
3L, 831L, 1396L, 545L, 0L, 226L, 79L, 0L, 0L, 101L, 0L, 3370L,
0L, 36L, 98L, 25L, 0L, 290L, 0L, 3L, 0L, 193L, 0L, 0L, 10L, 0L,
0L, 0L, 188L, 4L, 0L, 3L, 0L, 0L, 0L, 155L, 0L, 0L, 0L, 0L, 0L,
58L, 0L, 0L, 56L, 0L, 0L, 209L, 60L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 458L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 40L, 0L, 0L, 4L, 1L, 0L, 0L, 5L, 0L, 548L, 123L, 387L,
723L, 0L, 522L, 384L, 0L, 0L, 0L, 0L, 472L, 1L, 0L, 0L, 0L, 24L,
0L, 91L, 0L, 0L, 11L, 47L, 0L, 11L, 0L, 0L, 27L, 17L, 156L, 0L,
29L, 5L, 0L, 0L, 0L, 15L, 79L, 0L, 0L, 0L, 137L, 0L, 75L, 0L,
0L, 0L, 0L, 0L, 0L, 670L, 0L, 0L, 0L, 17L, 23L, 1L, 0L, 160L,
0L, 0L, 235L, 0L, 272L, 16L, 0L, 1803L, 0L, 4780L, 26L, 0L, 90L,
17L, 0L, 1518L, 151L, 1L, 768L, 151L, 42L, 0L, 9161L, 57L, 0L,
0L, 73L, 0L, 0L, 0L, 0L, 3086L, 0L, 0L, 0L, 1896L, 0L, 240L,
165L, 0L, 0L, 722L, 282L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 4L, 0L,
0L, 0L, 0L, 212L, 0L, 0L, 0L, 0L, 285L, 0L, 0L, 9L, 1349L, 1417L,
30792L, 2163L, 0L, 0L, 0L, 0L, 969L, 0L, 0L, 0L, 8L, 0L, 100L,
109L, 48L, 0L, 100L, 109L, 48L, 0L, 0L, 61L, 0L, 0L, 0L, 86L,
967L, 2679L, 86L, 967L, 2679L, 0L, 0L, 0L, 32L, 0L, 0L, 32L,
0L, 4L, 0L, 0L, 54L, 341L, 0L, 54L, 341L, 0L, 0L, 0L, 0L, 401L,
83L, 83L, 6L, 0L, 0L, 46L, 442L, 0L, 46L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 245L, 0L, 245L, 599L, 78L, 0L, 58L, 54L,
0L, 54L, 0L, 0L, 16L, 0L, 0L, 0L, 0L, 30L, 0L, 0L, 305L, 0L,
305L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 27L, 0L, 0L, 0L, 0L, 536L, 0L, 0L, 0L, 10L, 66L,
20L, 66L, 899L, 4L, 0L, 0L, 233L, 0L, 0L, 0L, 0L, 233L, 3L, 0L,
0L, 8L, 0L, 0L, 0L, 0L, 13L, 0L, 0L, 428L, 0L, 0L, 0L, 428L,
0L, 172L, 0L, 0L, 0L, 12L, 13L, 13L, 0L, 35L, 0L, 0L, 0L, 0L,
0L, 0L, 49L, 0L, 0L, 0L, 0L, 639L, 0L, 78L, 0L, 4386L, 78L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2076L, 6L, 0L, 0L
)), .Names = c("BoxType", "BoxVendor", "Receiver_TotalVideoDecoderErrors"
), class = "data.frame", row.names = c(NA, -999L))
The ifelse way
Assuming your CSV has been loaded into a data frame called boxdata with the same column names as in the CSV:
ifelse(boxdata$Receiver_TotalVideoDecoderErrors, 'Error', 'No error')
Explanation
The best way to demonstrate ifelse is by example:
x <- 1:5
x_lessthan_4 <- x < 4
x_lessthan_4
# [1] TRUE TRUE TRUE FALSE FALSE
if_lessthan_4 <- -x
if_lessthan_4
# [1] -1 -2 -3 -4 -5
if_notlessthan_4 <- x + 100
if_notlessthan_4
# [1] 101 102 103 104 105
ifelse(test = if_notlessthan_4,
yes = if_lessthan_4,
no = if_notlessthan_4)
# [1] -1 -2 -3 104 105
Hopefully it's clear what this function does. Obviously you don't need to always name the arguments as long as they're in the right order; I'm just doing it here so you can see exactly what's going on.
However, you'll notice that the expression
ifelse(boxdata$Receiver_TotalVideoDecoderErrors, 'Error', 'No error')
does not conform to this standard. It works because two things happen "under the hood":
test is "coerced" to logical, so if I pass in something like test = c(1, 3, 0) the value of test will be replaced with as.logical(test), so test = c(1, 3, 0) becomes test = c(TRUE, TRUE, FALSE).
yes and no are "recycled" if they are shorter than test, and truncated if they are longer.
Recycling is again best demonstrated by example:
test <- c(TRUE, FALSE, TRUE, TRUE, FALSE)
yes <- c(1, 2, 3)
no <- c(99, 100, 101, 102, 103, 104)
c(length(test), length(yes), length(no))
# [1] 5 3 6
ifelse(test, yes, no)
# [1] 1 100 3 1 103
These things are documented, but they're easy to miss if you're not used to reading the R help files.
And finally, the help file also says this, which is worth pointing out:
Missing values in test give missing values in the result.
This means that ifelse(c(NA, 1, 0, 1), 99, 100) returns c(NA, 99, 100, 99).
So
ifelse(boxdata$Receiver_TotalVideoDecoderErrors, 'Error', 'No error')
is equivalent to
test <- as.logical(boxdata$Receiver_TotalVideoDecoderErrors)
yes <- rep('Error', length(test))
no <- rep('No error', length(test))
ifelse(test, yes, no)
The slick way
Or let argument recycling to do the work for you. Shorter and more efficient, but maybe less readable to someone who isn't familiar with R:
c('No error', 'Error')[as.logical(boxdata$Receiver_TotalVideoDecoderErrors) + 1]
or
c('Error', 'No error')[!as.logical(boxdata$Receiver_TotalVideoDecoderErrors) + 1]
Explanation
First, the statement
as.logical(boxdata$Receiver_TotalVideoDecoderErrors) + 1
is equivalent to:
i <- as.logical(boxdata$Receiver_TotalVideoDecoderErrors)
i <- as.numeric(i) # FALSE -> 0, TRUE -> 1
i <- i + rep(1, length(i))
Finally, recycling is applied to subsetting as well:
c('No error', 'Error')[c(1, 2, 1, 1, 2)]
# [1] "No error" "Error" "No error" "No error" "Error"
So the entire thing is equivalent to:
i <- as.logical(boxdata$Receiver_TotalVideoDecoderErrors)
i <- as.numeric(i)
i <- i + rep(1, length(i))
c('No error', 'Error')[i]
The long way (suggested here)
This way doesn't need much explanation. It's a lot more typing but it's easy to read and it's flexible:
x <- boxdata$Receiver_TotalVideoDecoderErrors
x[x > 0] <- 'Error'
x[x == 0] <- 'No error'
Try using the which command and some logical operators to quickly determine the index locations for the values that are equal to 0 and those that are greater than 0. From there you can replace the original numeric values with a string. Try something like this:
data$Receiver_TotalVideoDecoderErrors[which(data$Receiver_TotalVideoDecoderErrors == 0)] = "no error"
data$Receiver_TotalVideoDecoderErrors[which(data$Receiver_TotalVideoDecoderErrors > 0)] = "error"
Hope that helps.
Related
merging plots from 2 models from sjplot
I need two models' plots combined into one graph, as seen in the image below. model 1 estimate's top line and model 2 estimates's bottom line.
I create the graph using one model, but I'm not sure how to incorporate an estimate from another model.
library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(ggplot2)
library(MASS)
library(pscl)
library(boot)
zinb <- zeroinfl(ivdays~age,
link="logit",
dist = "negbin",
data=caterpillor)
summary(zinb)
plot_model(zinb, type="est")
zinb <- zeroinfl(ivdays~age+sex+edu,
link="logit",
dist = "negbin",
data=caterpillor)
summary(zinb)
plot_model(zinb, type="est", terms = c("count_ageb", "count_agec", "zero_ageb", "zero_agec"))
############ second model#######
zinb_sub <- zeroinfl(ivdays~age,
link="logit",
dist = "negbin",
data=subset(caterpillor, country=="eng"))
summary(zinb_sub)
plot_model(zinb_sub, type="est")
zinb_sub2 <- zeroinfl(ivdays~age+sex+edu,
link="logit",
dist = "negbin",
data=subset(caterpillor, country=="eng"))
summary(zinb_sub2)
plot_model(zinb_sub2, type="est", terms = c("count_ageb", "count_agec", "zero_ageb", "zero_agec"))
Data:
caterpillor=structure(list(id = 1:100,
age = structure(c(1L, 1L, 2L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L),
.Label = c("a", "b", "c"), class = "factor"),
sex = structure(c(2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 1L),
.Label = c("F", "M"), class = "factor"),
country = structure(c(1L,
1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
2L, 2L, 2L),
.Label = c("eng", "scot", "wale"), class = "factor"),
edu = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L),
.Label = c("x", "y", "z"), class = "factor"),
lungfunction = c(45L,
23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L,
70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L,
50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L,
23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L,
70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L,
50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L,
23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 25L, 45L, 70L, 69L,
90L, 50L, 62L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 25L, 45L,
70L, 69L, 90L),
ivdays = c(15L, 26L, 36L, 34L, 2L, 4L, 5L,
8L, 9L, 15L, 26L, 36L, 34L, 2L, 4L, 5L, 8L, 9L, 15L, 26L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 5L, 8L, 9L, 36L, 34L, 2L, 4L, 5L, 8L,
9L, 36L, 34L, 2L, 4L, 5L),
no2_quintile = structure(c(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, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 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, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L),
.Label = c("q1", "q2",
"q3", "q4", "q5"), class = "factor")),
class = "data.frame", row.names = c(NA,
-100L))
ggplot2 select categories for bar chart and create labels
I am trying make bar chart with ggplot2 with the dataset below. When I use the code
ggplot(p.data, aes(x = `Period Number`, y = `Total Jumps`)) +
stat_summary(data = subset(p.data, Status = "Starter"), fun ="mean", geom = "bar")
I get this graph:
The most concerning aspect is the for period 2, 3, 4, and 5 the bars should be taller (period 2 should be around 9.9). Additionally, I would like to remove period 0 and period 1 and add bar labels with the raw data and without creating an additional data frame.
p.data <- structure(list(`Period Number` = c(0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L),
`Total Jumps` = c(112L, 97L, 28L, 132L, 162L, 19L, 92L, 112L,
97L, 141L, 68L, 86L, 76L, 26L, 105L, 125L, 19L, 92L, 112L,
64L, 101L, 68L, 4L, 8L, 0L, 8L, 12L, 0L, 0L, 0L, 13L, 8L,
0L, 8L, 2L, 2L, 5L, 12L, 0L, 0L, 0L, 5L, 11L, 0L, 0L, 6L,
0L, 9L, 8L, 0L, 0L, 0L, 7L, 10L, 0L, 14L, 5L, 0L, 5L, 5L,
0L, 0L, 0L, 8L, 11L, 0L, 108L, 131L, 47L, 136L, 159L, 35L,
114L, 116L, 111L, 190L, 64L, 75L, 95L, 47L, 116L, 123L, 27L,
103L, 108L, 70L, 152L, 64L, 4L, 7L, 0L, 14L, 10L, 0L, 0L,
0L, 15L, 10L, 0L, 4L, 0L, 0L, 3L, 7L, 7L, 8L, 8L, 5L, 10L,
0L, 7L, 14L, 0L, 3L, 10L, 1L, 0L, 0L, 11L, 7L, 0L, 18L, 15L,
0L, 0L, 9L, 0L, 3L, 0L, 10L, 11L, 0L, 118L, 96L, 48L, 143L,
170L, 37L, 118L, 117L, 116L, 165L, 56L, 80L, 68L, 48L, 114L,
130L, 36L, 114L, 107L, 80L, 123L, 56L, 2L, 10L, 0L, 8L, 11L,
0L, 0L, 0L, 5L, 9L, 0L, 4L, 12L, 0L, 6L, 5L, 0L, 4L, 8L,
12L, 8L, 0L, 7L, 4L, 0L, 10L, 10L, 0L, 0L, 0L, 12L, 13L,
0L, 25L, 2L, 0L, 5L, 14L, 1L, 0L, 2L, 7L, 12L, 0L), Status = structure(c(1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L), .Label = c("Bench", "Starter"), class = "factor")), row.names = c(NA,
198L), class = "data.frame")
Thank you for your help!
It's best to pass that data you actually want to plot to the plotting function, rather than trying to coerce it within the plotting function. In this case you were trying to subset a different data frame from the one you passed to ggplot inside stat_summary. The call to ggplot had already set up the aesthetics you wanted mapped, then in your only geom layer, you were telling ggplot you wanted a completely different set of aesthetics. You don't need to create another data frame to reshape your data. Here's how you could do it using dplyr: library(dplyr) library(ggplot2) p.data %>% filter(Status == "Starter") %>% group_by(`Period Number`) %>% summarise(`Total Jumps` = mean(`Total Jumps`)) %>% filter(`Period Number` > 1) %>% ggplot(aes(x = `Period Number`, y = `Total Jumps`)) + geom_col(fill = "dodgerblue", colour = "black") + geom_text(aes(y = `Total Jumps` + 1, label = signif(`Total Jumps`, 2)))
Box plot containing multiple variables? / Violin plot?
I'm pretty new to R and struggling to get my box plot to include all the individual's variables in one graph. The data is looking at site locations used by 3 different species. So far I can only get it to work within a bar graph, which isn't ideal.
If possible, would anyone know how to do this also as a violin plot?
below is my code and current attempts.
> str(pips)
'data.frame': 732 obs. of 8 variables:
$ Site : Factor w/ 4 levels "Allerthorpe",..: 4 4 4 4 4 4 4 4 4 4 ..
$ Box.Type : Factor w/ 4 levels "Combi","Standard",..: 2 2 2 2 2 2 2 2 2 2 ..
$ Size : Factor w/ 4 levels "Large Woodcrete",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Species : Factor w/ 3 levels "P. nathusii",..: 2 2 2 2 2 2 2 2 2 2 ...
$ No..Bats : int 1 4 1 3 1 3 3 2 1 2 ...
barplot(xtabs(No..Bats~Species + Site, data=pips),
legend=c("P. nathusii", "P. pipistrellus", "P. pygmaeus")
EDIT. I have now managed to create a violin plot. However I am still needing to adjust my box plot.
Dataset is Below (I wasnt sure how to post this)
> dput(pips)
structure(list(Site = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 1L, 1L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 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, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 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, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 1L, 1L,
1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 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, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
1L, 3L, 3L, 3L, 4L), .Label = c("Allerthorpe", "Millington",
"Top Hill", "Wheldrake"), class = "factor"), Box.Type = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Combi",
"Standard", "Warwickshire", "Woodcrete"), class = "factor"),
Size = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("Large Woodcrete", "Medium Woodcrete",
"Mixed", "Small"), class = "factor"), Species = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 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, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L), .Label = c("P. nathusii",
"P. pipistrellus", "P. pygmaeus"), class = "factor"), Male = c(0L,
1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 2L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 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, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 0L, 3L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L,
2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
2L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L,
1L, 2L, 1L, 0L, 1L, 1L, 0L, 3L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L), Female = c(1L,
3L, 1L, 2L, 0L, 2L, 2L, 2L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 3L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 2L, 1L, 0L, 1L,
1L, 0L, 1L, 1L, 2L, 1L, 1L, 0L, 0L, 2L, 1L, 0L, 0L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L,
2L, 1L, 1L, 3L, 1L, 9L, 2L, 1L, 1L, 0L, 1L, 0L, 0L, 2L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 1L, 3L, 2L, 1L, 1L,
1L, 2L, 0L, 4L, 1L, 1L, 1L, 1L, 0L, 2L, 0L, 1L, 0L, 2L, 1L,
1L, 1L, 3L, 1L, 2L, 1L, 0L, 1L, 1L, 2L, 1L, 2L, 0L, 1L, 1L,
0L, 4L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 2L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 2L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 1L, 1L, 0L, 2L, 1L, 2L, 2L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 2L, 0L, 1L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 0L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 0L, 0L, 1L, 1L, 2L, 1L, 1L, 2L, 0L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 4L, 1L, 0L, 0L, 1L, 0L, 1L, 2L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 0L, 3L, 2L, 2L,
1L, 0L, 2L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 0L, 0L, 1L, 2L, 1L, 2L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 1L, 2L, 1L, 0L, 1L, 1L, 1L,
0L, 1L, 0L, 2L, 5L, 4L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 2L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 5L, 3L, 3L, 1L, 2L, 7L,
0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 5L, 0L, 1L, 2L, 2L, 3L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 4L, 1L, 0L, 2L, 0L, 1L, 0L, 0L,
1L, 2L, 0L, 0L, 3L, 1L, 1L, 0L, 0L, 1L, 3L, 1L, 0L, 2L, 0L,
1L, 2L, 2L, 1L, 0L, 7L, 13L, 0L, 0L, 0L, 0L, 6L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 4L, 3L, 1L, 0L, 4L,
4L, 3L, 0L, 1L, 1L, 3L, 5L, 2L, 2L, 0L, 0L, 0L, 2L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 7L, 0L, 0L, 0L, 12L, 1L, 2L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 5L, 2L, 2L, 0L, 0L, 0L, 1L, 0L, 1L, 2L, 2L,
1L, 1L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 1L, 3L,
2L, 5L, 1L, 0L, 3L, 3L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 3L, 0L, 2L, 1L, 0L, 1L, 1L, 1L, 2L, 2L, 0L, 0L, 1L,
1L, 0L, 4L, 9L, 3L, 3L, 1L, 1L, 4L, 0L, 1L, 1L, 2L, 1L, 1L,
0L, 1L, 1L, 1L, 0L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L,
4L, 2L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 2L, 6L,
3L, 1L, 3L, 6L, 5L, 3L, 0L, 0L, 10L, 11L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 1L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 4L, 0L, 0L, 0L, 4L, 0L, 2L, 0L, 0L, 7L, 0L, 2L, 0L,
2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 2L, 3L, 2L, 2L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 3L, 4L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 2L, 0L, 1L, 1L, 0L, 2L, 0L, 1L, 2L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 4L, 0L, 3L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 3L, 1L, 0L, 2L),
Unidentified = 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, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 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, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
5L, 0L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 6L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
3L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 12L,
0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 5L, 4L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), No..Bats = c(1L, 4L, 1L, 3L, 1L, 3L, 3L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 9L, 3L, 1L,
2L, 1L, 5L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 4L,
1L, 4L, 1L, 4L, 2L, 2L, 2L, 1L, 2L, 1L, 5L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 4L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 4L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 5L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 4L, 2L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 5L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 5L, 4L, 3L, 1L, 2L, 8L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 6L, 2L, 2L, 2L, 2L, 5L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 6L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 7L, 15L,
1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 5L, 4L, 2L, 1L, 5L, 5L, 4L, 1L, 2L, 2L, 4L, 6L, 3L,
3L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 1L, 1L, 1L, 18L,
2L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 3L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 4L, 3L, 5L, 1L, 1L, 4L, 3L, 1L, 3L, 1L,
1L, 1L, 5L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 6L, 9L, 3L, 3L, 1L, 1L, 4L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 7L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 12L, 4L, 1L, 2L, 6L, 3L, 1L, 6L, 10L, 6L, 4L, 6L, 4L,
10L, 11L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 4L, 5L, 1L, 1L, 1L, 5L, 1L,
3L, 1L, 1L, 8L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 2L, 1L, 3L, 4L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L,
5L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
2L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 5L,
1L, 7L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 2L, 1L, 3L)), class = "data.frame", row.names = c(NA,
-732L))
Generated some example data. In ggplot, you can use the geom_violin.
set.seed(1)
df <- data.frame(letters=sample(c(rep(c('a','b','c'), 400))),
numbers=sample(c(rep(c(1:4), 300))),
value=sample(1:10, 1200, replace=T))
require(ggplot2)
ggplot(data = df, aes(x=letters, y=value)) +
geom_violin() +
facet_grid(.~numbers)
If you don't mind ggplot graphics, this might be what you are looking for. First I will make up a dataset, since you have not posted one. set.seed(4890) # Make the results reproducible n <- 732 pips <- data.frame(Site = sample(LETTERS[1:4], n, TRUE), Species = sample(letters[1:3], n, TRUE), No = sample.int(20, n, TRUE)) Now the graph. library(ggplot2) ggplot(pips, aes(Species, No, fill = Species)) + geom_boxplot() + scale_fill_grey() + facet_grid(cols = vars(Site)) + theme_bw() With the datset you have posted, I have made a small change to the above code. The x axis labels would be over each other, so I added a call to theme(axis.text.x) and rotated them 45 degrees. ggplot(pips, aes(x = Species, y = `No..Bats`, fill = Species)) + geom_boxplot() + scale_fill_grey() + facet_grid(cols = vars(Site)) + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
How to plot predictions of binomial GLM that has both continuous and categorical variables
I have a binomial GLM in R, with several predictors that are both continuous and categorical.
The response variable is "Presence", which is binary (0/1).
Length is a continuous variable, while all others are categorical.
I am trying to plot predictions for each of the variables in the final model, particularly for "length", but I'm having difficulties.
My data are the following:
MyData<-structure(list(site = structure(c(3L, 1L, 3L, 2L, 1L, 4L, 3L,
4L, 1L, 2L, 4L, 5L, 5L, 1L, 4L, 3L, 2L, 4L, 1L, 4L, 5L, 1L, 5L,
4L, 3L, 1L, 3L, 5L, 5L, 4L, 4L, 3L, 1L, 5L, 1L, 3L, 1L, 4L, 4L,
3L, 4L, 4L, 2L, 3L, 1L, 4L, 2L, 1L, 1L, 4L, 4L, 4L, 1L, 3L, 3L,
2L, 1L, 4L, 2L, 5L, 5L, 3L, 3L, 2L, 5L, 2L, 4L, 5L, 2L, 4L, 4L,
2L, 5L, 2L, 3L, 5L, 4L, 4L, 5L, 1L, 1L, 3L, 2L, 4L, 3L, 1L, 4L,
3L, 1L, 4L, 3L, 3L, 4L, 5L, 1L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 1L,
5L, 5L, 1L, 5L, 2L, 3L, 4L, 4L, 3L, 2L, 3L, 3L, 5L, 3L, 3L, 3L,
5L, 1L, 5L, 2L, 3L, 4L, 5L, 5L, 1L, 4L, 2L, 5L, 3L, 2L, 5L, 4L,
3L, 3L, 3L, 1L, 1L, 4L, 1L, 2L, 4L, 5L, 1L, 1L, 2L, 2L, 5L, 3L,
4L, 4L, 1L, 5L, 2L, 4L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 4L, 3L, 1L,
5L, 3L, 3L, 3L, 4L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 5L, 1L,
3L, 4L, 3L, 2L, 1L, 1L, 2L, 5L, 2L, 1L, 5L, 3L, 1L, 4L, 1L, 3L,
3L, 3L, 3L, 5L, 1L, 4L, 1L, 1L, 3L, 3L, 4L, 1L, 3L, 3L, 4L, 2L,
5L, 5L, 5L, 1L, 4L, 4L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 4L, 3L,
1L, 1L, 5L, 3L, 1L), .Label = c("R1a", "R1b", "R2", "Za", "Zb"
), class = "factor"), species = structure(c(1L, 1L, 3L, 3L, 3L,
1L, 3L, 1L, 4L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 1L,
1L, 1L, 4L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 3L, 1L, 4L,
3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 3L, 4L, 3L, 1L,
1L, 3L, 1L, 1L, 4L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 4L,
1L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 3L,
1L, 1L, 4L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 1L,
3L, 1L, 4L, 3L, 1L, 4L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 4L, 3L, 4L, 3L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 3L, 4L, 3L,
1L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 4L, 3L, 3L,
1L, 3L, 1L, 4L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 3L), .Label = c("Monogyna", "Other",
"Prunus", "Rosa"), class = "factor"), aspect = structure(c(4L,
4L, 4L, 4L, 4L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 4L,
3L, 4L, 3L, 1L, 4L, 4L, 3L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L,
4L, 4L, 2L, 4L, 3L, 3L, 1L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
3L, 3L, 3L, 4L, 1L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 4L, 1L,
4L, 3L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 3L, 3L, 4L, 4L, 4L,
2L, 4L, 3L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 4L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L,
3L, 2L, 3L, 1L, 2L, 5L, 2L, 4L, 4L, 4L, 3L, 3L, 1L, 2L, 4L, 3L,
4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 1L,
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 4L, 4L, 3L, 4L, 2L, 3L, 4L, 4L, 2L, 3L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 2L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L,
3L, 4L, 2L, 5L, 3L, 4L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 2L,
4L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 2L, 4L), .Label = c("East",
"Flat", "North", "South", "West"), class = "factor"), length = c(260L,
60L, 60L, 40L, 240L, 80L, 30L, 100L, 100L, 200L, 70L, 50L, 60L,
35L, 120L, 60L, 500L, 40L, 20L, 70L, 250L, 80L, 50L, 130L, 350L,
170L, 50L, 60L, 90L, 50L, 40L, 110L, 60L, 70L, 70L, 500L, 140L,
50L, 50L, 360L, 50L, 150L, 60L, 270L, 280L, 130L, 130L, 50L,
60L, 30L, 70L, 70L, 60L, 400L, 20L, 30L, 70L, 160L, 340L, 100L,
210L, 60L, 70L, 130L, 50L, 40L, 50L, 80L, 390L, 40L, 110L, 130L,
40L, 230L, 120L, 70L, 80L, 80L, 90L, 70L, 150L, 120L, 50L, 100L,
120L, 10L, 40L, 80L, 180L, 160L, 200L, 40L, 70L, 90L, 50L, 40L,
80L, 80L, 70L, 480L, 90L, 60L, 100L, 140L, 190L, 20L, 70L, 360L,
70L, 130L, 60L, 50L, 320L, 210L, 130L, 180L, 90L, 20L, 300L,
90L, 50L, 130L, 70L, 70L, 40L, 40L, 50L, 40L, 100L, 20L, 70L,
100L, 340L, 70L, 110L, 40L, 230L, 200L, 80L, 35L, 110L, 200L,
50L, 110L, 100L, 50L, 150L, 110L, 50L, 50L, 40L, 70L, 80L, 60L,
100L, 90L, 40L, 300L, 140L, 180L, 140L, 40L, 190L, 100L, 170L,
40L, 120L, 15L, 70L, 340L, 40L, 40L, 70L, 60L, 130L, 140L, 170L,
120L, 90L, 130L, 210L, 50L, 180L, 120L, 100L, 50L, 90L, 70L,
360L, 80L, 30L, 170L, 70L, 300L, 40L, 130L, 120L, 90L, 40L, 40L,
140L, 80L, 400L, 70L, 80L, 60L, 420L, 320L, 200L, 40L, 50L, 70L,
50L, 80L, 50L, 110L, 100L, 120L, 170L, 20L, 110L, 20L, 20L, 30L,
30L, 90L, 150L, 80L, 40L, 90L, 300L, 30L, 70L, 50L, 90L, 200L
), sun = structure(c(1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L,
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L,
2L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 3L, 3L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 1L,
3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L,
3L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L,
3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L,
1L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L,
3L, 3L), .Label = c("Half", "Shade", "Sun"), class = "factor"),
leaf = structure(c(2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 4L, 2L,
2L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 4L, 2L, 2L, 1L,
2L, 2L, 4L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L,
2L, 4L, 1L, 2L, 4L, 1L, 2L, 4L, 2L, 4L, 2L, 2L, 2L, 1L, 4L,
4L, 1L, 4L, 1L, 2L, 4L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L,
2L, 2L, 2L, 2L, 4L, 1L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 4L, 2L, 2L, 1L, 4L, 2L, 2L, 2L, 1L, 4L, 2L, 2L, 1L, 1L,
1L, 2L, 4L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L,
4L, 2L, 2L, 4L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 4L, 4L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 4L, 2L, 2L, 2L, 4L, 2L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 2L,
4L, 2L, 2L, 1L, 2L, 2L, 4L, 2L, 4L, 4L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 4L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 4L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L,
2L), .Label = c("Large", "Medium", "Scarce", "Small"), class = "factor"),
Presence = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L
)), .Names = c("site", "species", "aspect", "length", "sun",
"leaf", "Presence"), row.names = c(NA, 236L), class = "data.frame")
(note that this is a reduced dataset, and I have already removed variables that were dropped during model selection)
The optimal model is:
model <- glm(Presence ~ site + species + aspect + length + sun
+ leaf, data=MyData, family=binomial)
I tried the following, but it wants the other variables too, so I get an error:
plot(MyData$length, MyData$Presence)
mydat1 <- data.frame(length = seq(from = 10, to = 500, by = 1)
pred1 <- predict(model, newdata = mydat1, type = "response")
lines(MyData$length, pred1)
So I tried specifying all variables, but then it only puts a horizontal line through the presence data points (and that means I need to specify all possible combinations of factor variables I suppose):
plot(MyData$length, MyData$Presence)
mydat2 <- data.frame(length = seq(from = 10, to = 500, by = 1),
site = "R1a",
species = "Monogyna",
aspect = "Flat",
sun = "Sun",
leaf = "Scarce")
pred2 <- predict(model, newdata = mydat2, type = "response")
lines(MyData$length, pred2)
Finally, I tried the following code:
pred <- predict(model, type = "response")
par(mfrow=c(2,2))
for(i in names(MyData)){
plot(MyData[,i],pred,xlab=i, ylab="Probability")
}
I am confused by this last one, as I am not able to obtain the curve, plus the output gives me predicted values for variables that are not even in the optimal model.
What I should expect under this model, is a sinusoidal curve, I suppose. But that's not what I'm getting.
How can I produce a meaningful plot of predictions?
Any help would be greatly appreciated.
I would use the effects package for some easier results for a single predictor. Here is how:
library(effects)
fit <- as.data.frame(effect('length', model, xlevels = 100))
Plotting is easy (although note the overplotting):
plot(MyData$length, MyData$Presence)
lines(fit$length, fit$fit)
Or we can use ggplot2:
library(ggplot2)
ggplot() +
geom_count(aes(length, Presence), MyData) +
geom_line(aes(length, fit), fit, size = 1, col = 'red') +
geom_ribbon(aes(length, ymin = lower, ymax = upper), fit, alpha = 0.15) +
scale_size_area()
We can see that the effect of length is not very impressive.
Reorder Factor in rCharts dPlot
How to do I reorder the factor in my dPlot, as such that "Exceptional" is located at the most left, followed by "Fine", "Mediocre", "Trouble", "Danger", and "Critical".
Dataset:
dat<-structure(list(MainDept = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L,
5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("Business Development",
"Finance Department", "HR & Admin Department", "Manufacturing & Engineering Department",
"QA Department", "Supply Chain Department"), class = "factor"),
Level = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("Critical",
"Danger", "Exceptional", "Fine", "Mediocre", "Trouble"), class = "factor"),
Freq = c(0L, 0L, 0L, 29L, 0L, 0L, 0L, 0L, 0L, 108L, 0L, 0L,
0L, 0L, 2L, 7L, 12L, 15L, 0L, 7L, 15L, 16L, 73L, 59L, 12L,
0L, 0L, 191L, 0L, 0L, 11L, 0L, 0L, 128L, 0L, 0L)), .Names = c("MainDept",
"Level", "Freq"), row.names = c(NA, -36L), class = "data.frame")
The code to generate the plot:
require(rCharts)
plot<-dPlot(y="MainDept", x="Freq",data=dat,groups="Level",type="bar",width=1200)
plot$yAxis(type="addCategoryAxis")
plot$xAxis(type="addPctAxis")
plot$legend(
x = 0,
y = 0,
horizontalAlign = "right"
)
I tried with:
dat$Level<-as.character(dat$Level)
dat$Level[dat$Level=="Exceptional"]<-1
dat$Level[dat$Level=="Fine"]<-2
dat$Level[dat$Level=="Mediocre"]<-3
dat$Level[dat$Level=="Trouble"]<-4
dat$Level[dat$Level=="Danger"]<-5
dat$Level[dat$Level=="Critical"]<-6
dat$Level<-as.factor(dat$Level)
Thank you.
I don't know about dPlot, but if it relies on the order of the levels in the grouping variable you should try
dat$Level <- factor(dat$Level, levels = c("Exceptional", "Fine", "Mediocre",
"Trouble", "Danger", "Critical"))
As I do not know in which library dPlot can be found this is of course untested. Hence, for future reference, a minimal working example including the necessary libraries would help.