Tôi đang sử dụng tập dữ liệu này (ở dưới cùng) để tạo một ô mật độ, nhưng có vấn đề với yếu tố và làm cho nó tập hợp chính xác. Tôi muốn đồ thị trông như thế này:Tạo một ô mật độ với ggplot2 sử dụng hệ số
ggplot(sample, aes(as.numeric(value), colour=shortname)) + geom_density()
Nhưng tôi muốn trục x có nhãn thực tế của các yếu tố. Nhưng khi tôi sử dụng điều này:
ggplot(sample, aes(value, colour=shortname)) + geom_density()
biểu đồ không tổng hợp chúng thành hai giá trị riêng biệt của biến số shortname
.
Tôi đang làm gì sai? Tôi đã đọc về việc sử dụng scale_x_discrete()
, nhưng tôi không nghĩ rằng tôi nên cần phải kể từ khi tôi đã có một yếu tố ...
UPDATE: Ngay cả nếu tôi sử dụng scale_x_discrete
theo cách sau:
ggplot(sample, aes(value, colour=shortname)) + geom_density() + scale_x_discrete(breaks=1:27, labels=c("<A",LETTERS))
chỉ cần xóa các nhãn trục x cùng nhau ...
Cảm ơn bạn trước!
sample <- structure(list(shortname = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("H1",
"H2"), class = "factor"), value = structure(c(7L, 17L, 8L, 15L,
18L, 17L, 14L, 19L, 20L, 17L, 17L, 12L, 16L, 21L, 2L, 21L, 19L,
22L, 12L, 15L, 22L, 19L, 16L, 13L, 19L, 24L, 15L, 24L, 23L, 12L,
24L, 21L, 15L, 16L, 16L, 18L, 18L, 8L, 23L, 8L, 21L, 24L, 13L,
10L, 18L, 1L, 7L, 14L, 13L, 21L, 16L, 10L, 15L, 21L, 17L, 18L,
18L, 21L, 14L, 9L, 22L, 14L, 11L, 16L, 13L, 18L, 12L, 1L, 23L,
8L, 15L, 18L, 11L, 10L, 20L, 16L, 12L, 10L, 22L, 25L, 24L, 7L,
19L, 13L, 16L, 16L, 20L, 3L, 13L, 21L, 12L, 16L, 13L, 15L, 1L,
19L, 12L, 20L, 12L, 11L, 20L, 7L, 22L, 18L, 19L, 9L, 10L, 24L,
10L, 13L, 5L, 16L, 19L, 20L, 19L, 18L, 19L, 19L, 13L, 12L, 21L,
20L, 13L, 21L, 3L, 12L, 19L, 17L, 16L, 9L, 21L, 18L, 24L, 2L,
12L, 13L, 14L, 7L, 16L, 10L, 21L, 15L, 21L, 11L, 18L, 3L, 16L,
15L, 22L, 10L, 16L, 21L, 19L, 17L, 20L, 22L, 17L, 20L, 2L, 24L,
12L, 18L, 19L, 24L, 26L, 17L, 20L, 15L, 12L, 10L, 16L, 12L, 12L,
15L, 19L, 14L, 22L, 12L, 7L, 16L, 1L, 20L, 18L, 24L, 19L, 22L,
3L, 16L, 19L, 22L, 5L, 19L, 17L, 16L, 13L, 22L, 3L, 14L, 12L,
9L, 5L, 16L, 14L, 15L, 12L, 2L, 12L, 19L, 20L, 18L, 10L, 3L,
20L, 4L, 16L, 19L, 1L, 14L, 24L, 9L, 14L, 1L, 12L, 6L, 1L, 22L,
11L, 13L, 19L, 16L, 22L, 25L, 3L, 21L, 21L, 22L, 3L, 21L, 18L,
23L, 24L, 2L, 21L, 15L, 15L, 16L, 11L, 13L, 25L, 11L, 17L, 15L,
7L, 23L, 21L, 4L, 1L, 14L, 19L, 13L, 10L, 18L, 3L, 13L, 17L,
12L, 7L, 21L, 17L, 17L, 17L, 17L, 10L, 21L, 24L, 22L, 12L, 22L,
12L, 24L, 17L, 16L, 21L, 19L, 16L, 16L, 16L, 21L, 13L, 1L, 7L,
21L, 11L, 13L, 10L, 21L, 11L, 25L, 1L, 11L, 3L, 24L, 13L, 13L,
15L, 7L, 21L, 16L, 24L, 16L, 8L, 19L, 13L, 18L, 18L, 22L, 19L,
16L, 16L, 15L, 5L, 4L, 14L, 8L, 15L, 18L, 13L, 14L, 12L, 19L,
16L, 3L, 16L, 17L, 1L, 19L, 20L, 19L, 1L, 19L, 20L, 22L, 8L,
12L, 13L, 24L, 16L, 14L, 21L, 25L, 22L, 4L, 16L, 16L, 15L, 16L,
8L, 14L, 12L, 11L, 5L, 13L, 19L, 27L, 3L, 18L, 12L, 13L, 19L,
7L, 10L, 15L, 23L, 11L, 3L, 24L, 18L, 15L, 16L, 14L, 16L, 22L,
11L, 11L, 20L, 18L, 14L, 20L, 21L, 3L, 10L, 19L, 14L, 16L, 8L,
12L, 16L, 8L, 21L, 26L, 13L, 6L, 9L, 2L, 15L, 1L, 12L, 24L, 3L,
21L, 24L, 8L, 18L, 20L, 3L, 19L, 12L, 15L, 8L, 18L, 14L, 19L,
10L, 20L, 17L, 12L, 17L, 19L, 14L, 10L, 7L, 11L, 12L, 3L, 19L,
1L, 16L, 11L, 8L, 3L, 10L, 15L, 21L, 27L, 3L, 3L, 19L, 5L, 17L,
22L, 10L, 3L, 15L, 19L, 19L, 18L, 23L, 1L, 22L, 9L, 22L, 19L,
12L, 18L, 10L, 10L, 9L, 14L, 2L, 27L, 21L, 4L, 18L, 1L, 2L, 16L,
3L, 21L, 19L, 24L, 12L, 12L, 19L, 13L, 16L, 19L, 20L, 12L, 20L,
13L, 9L, 15L, 22L, 14L, 5L, 22L, 15L, 3L, 9L, 3L, 12L, 2L, 12L,
12L, 22L, 15L, 9L, 3L, 21L, 14L, 5L, 5L, 10L, 5L, 5L, 1L, 7L,
21L, 19L, 22L, 1L, 9L, 1L, 21L, 18L, 15L, 14L, 21L, 6L, 19L,
15L, 16L, 5L, 5L, 10L, 20L, 5L, 8L, 19L, 3L, 16L, 5L, 7L, 17L,
16L, 19L, 2L, 20L, 15L, 9L, 17L, 21L, 19L, 13L, 3L, 13L, 12L,
21L, 16L, 15L, 17L, 16L, 19L, 8L, 17L, 14L, 1L, 1L, 22L, 19L,
24L, 20L, 10L, 17L, 1L, 17L, 1L, 17L, 13L, 15L, 21L, 6L, 3L,
18L, 20L, 15L, 4L, 16L, 8L, 12L, 10L, 13L, 13L, 22L, 11L, 12L,
1L, 21L, 21L, 5L, 5L, 16L, 11L, 20L, 21L, 20L, 21L, 20L, 19L,
20L, 15L, 25L, 9L, 1L, 12L, 21L, 9L, 24L, 3L, 12L, 24L, 8L, 16L,
15L, 9L, 20L, 15L, 5L, 10L, 1L, 16L, 16L, 12L, 9L, 20L, 10L,
19L, 12L, 3L, 20L, 22L, 11L, 16L, 16L, 22L, 19L, 19L, 22L, 14L,
14L, 12L, 5L, 14L, 19L, 18L, 19L, 18L, 3L, 10L, 20L, 14L, 1L,
13L, 18L, 13L, 1L, 22L, 23L, 19L, 13L, 18L, 9L, 16L, 15L, 17L,
21L, 15L, 18L, 1L, 14L, 14L, 1L, 14L, 9L, 16L, 12L, 22L, 14L,
2L, 22L, 19L, 21L, 16L, 16L, 11L, 19L, 13L, 3L, 16L, 16L, 20L,
18L, 1L, 19L, 11L, 17L, 19L, 12L, 15L, 10L, 11L, 13L, 7L, 14L,
14L, 14L, 15L, 15L, 16L, 14L, 22L, 20L, 17L, 19L, 19L, 13L, 16L,
12L, 15L, 20L, 22L, 17L, 20L, 16L, 10L, 15L, 15L, 12L, 12L, 14L,
20L, 5L, 19L, 2L, 13L, 15L, 17L, 9L, 14L, 18L, 2L, 10L, 14L,
12L, 14L, 12L, 18L, 17L, 13L, 8L, 22L, 12L, 21L, 12L, 13L, 3L,
14L, 26L, 4L, 3L, 1L, 7L, 10L, 19L, 16L, 16L, 15L, 13L, 15L,
16L, 11L, 21L, 12L, 11L, 15L, 1L, 16L, 1L, 17L, 6L, 1L, 16L,
7L, 11L, 2L, 5L, 16L, 5L, 12L, 13L, 12L, 13L, 13L, 12L, 20L,
21L, 21L, 12L, 19L, 21L, 18L, 12L, 15L, 22L, 19L, 16L, 16L, 3L,
14L, 1L, 7L, 13L, 16L, 11L, 7L, 12L, 16L, 16L, 12L, 22L, 1L,
13L, 4L, 8L, 16L, 5L, 11L, 10L, 1L, 21L, 10L, 19L, 12L, 13L,
16L, 12L, 15L, 19L, 13L, 1L, 1L, 2L, 6L, 16L, 14L, 15L, 15L,
16L, 4L, 12L, 16L, 10L, 19L, 12L, 5L, 6L, 10L, 3L, 14L, 1L, 12L,
4L, 11L, 16L, 10L, 20L, 4L, 13L, 10L, 1L, 9L, 2L, 7L, 9L, 18L,
10L, 26L, 14L, 2L, 14L, 10L, 11L, 13L, 1L, 21L, 16L, 9L, 22L,
12L, 12L, 16L, 15L, 12L, 8L, 15L, 20L, 11L, 16L, 15L, 12L, 12L,
16L, 2L, 9L, 12L, 14L, 20L, 1L, 10L, 7L, 10L, 18L, 16L, 12L,
15L, 12L, 14L, 3L, 14L, 6L, 10L, 1L, 11L, 9L, 5L, 12L, 12L, 1L,
8L, 20L, 7L, 21L, 20L, 22L, 20L, 7L, 12L, 9L, 7L, 13L, 19L, 15L,
15L, 18L, 16L, 1L, 10L, 19L, 2L, 13L, 6L, 24L, 1L, 22L, 16L,
11L, 7L, 5L, 19L, 15L, 14L, 12L, 19L, 14L, 12L, 15L, 24L, 15L,
10L, 4L, 14L, 16L, 3L, 21L, 1L, 19L, 14L, 17L, 12L, 21L, 3L,
12L, 16L, 18L, 14L, 15L, 15L, 14L, 1L, 2L, 17L, 1L, 14L, 16L,
15L, 14L, 10L, 14L, 17L, 17L, 12L, 17L, 11L, 14L, 16L, 1L, 1L,
19L, 12L, 24L, 15L, 19L, 14L, 8L, 3L, 22L, 1L, 16L, 15L, 19L,
8L, 15L, 12L, 8L, 14L, 8L, 12L, 7L, 13L, 2L, 13L, 10L, 15L, 15L,
17L, 1L, 26L, 24L, 21L, 25L, 14L, 10L, 13L, 9L, 13L, 18L, 19L,
16L, 21L, 16L, 17L, 14L, 14L, 11L, 17L, 16L, 12L, 17L, 14L, 6L,
24L, 11L, 11L, 11L, 12L, 15L, 13L, 22L, 11L, 17L, 3L, 12L, 17L,
14L, 10L, 11L, 9L, 21L, 18L, 19L, 20L, 24L, 7L, 12L, 22L, 3L,
17L, 10L, 1L, 20L, 1L, 1L, 12L, 2L, 14L, 2L, 17L, 19L, 1L, 10L,
12L, 16L, 15L, 3L, 12L, 16L, 12L, 15L, 17L, 24L, 15L, 16L, 8L,
12L, 14L, 21L, 9L, 23L, 3L, 19L, 16L, 19L, 16L, 16L, 13L, 13L,
3L, 9L, 17L, 1L, 1L, 16L, 11L, 15L, 7L, 7L, 14L, 8L, 14L, 20L,
15L, 16L, 1L, 12L, 9L, 16L), .Label = c("<A", "A", "B", "C",
"D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P",
"Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"), class = "factor")), .Names = c("shortname",
"value"), row.names = c(NA, 1156L), class = "data.frame")
Ngắn và ngọt. Cảm ơn! –
Bạn có thể giải thích lý do của ggplot2 đằng sau vấn đề này không? Ánh xạ 'nhóm' có thay đổi cách' stat_density' tính toán bằng cách nào đó không? Tại sao có nhiều đường mật độ khi 'nhóm' không được chỉ định? – Heisenberg