我用geom_boxplot来比较发音中"预发"和"终发"Position中某些单词的d个发音.我还想给出图表底部(y==0)的观测次数，理想情况下旋转90°.但在我使用geom_text的方式中，每个数据点都被 case 编号覆盖了:
df %>%
ggplot(
aes(x = Position, y = d, group = Position, color = Position)
) +
geom_boxplot(notch = TRUE) +
facet_grid(. ~ w, scales = 'free_x') +
geom_text(aes(label = N), stat = "unique")
theme(axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank())
生效日期:
df <- structure(list(d = c(0.084, 0.254, 0.064, 0.122, 0.367, 0.142,
0.299, 0.294, 0.096, 0.091, 0.184, 0.184, 0.082, 0.081, 0.185,
0.113, 0.231, 0.159, 0.151, 0.155, 0.135, 0.213, 0.219, 0.158,
0.145, 0.183, 0.159, 0.078, 0.169, 0.086, 0.189, 0.097, 0.079,
0.077, 0.157, 0.218, 0.113, 0.255, 0.153, 0.049, 0.096, 0.062,
0.091, 0.035, 0.043, 0.224, 0.057, 0.186, 0.239, 0.062, 0.109,
0.131, 0.146, 0.144, 0.076, 0.094, 0.089, 0.049, 0.049, 0.053,
0.099, 0.131, 0.029, 0.303, 0.034, 0.103, 0.161, 0.028, 0.161,
0.161, 0.139, 0.236, 0.045, 0.233, 0.124, 0.158, 0.175, 0.113,
0.231, 0.126, 0.139, 0.173, 0.114, 0.321, 0.159, 0.142, 0.158,
0.187, 0.086, 0.369, 0.068, 0.104, 0.218, 0.336, 0.172, 0.098,
0.082, 0.039, 0.149, 0.148, 0.169, 0.116, 0.123, 0.099, 0.091,
0.051, 0.079, 0.188, 0.096, 0.167, 0.115, 0.138, 0.137, 0.281,
0.113, 0.217, 0.121, 0.126, 0.087, 0.154, 0.296, 0.081, 0.199,
0.064, 0.144, 0.105, 0.089, 0.117, 0.051, 0.048, 0.066, 0.089,
0.136, 0.315, 0.089, 0.066, 0.273, 0.099, 0.112, 0.117, 0.094,
0.132, 0.087, 0.058, 0.156, 0.121, 0.205, 0.052, 0.154, 0.278,
0.138, 0.181, 0.151, 0.063, 0.103, 0.111, 0.126, 0.312, 0.252,
0.095, 0.142, 0.181, 0.263, 0.153, 0.209, 0.059, 0.214, 0.097,
0.161, 0.143, 0.143, 0.127, 0.191, 0.209, 0.144, 0.068, 0.095,
0.074, 0.107, 0.187, 0.122, 0.265, 0.131, 0.111, 0.187, 0.069,
0.109, 0.149, 0.149, 0.093, 0.135, 0.145, 0.135, 0.073, 0.131,
0.168, 0.126, 0.111, 0.106, 0.146, 0.134, 0.127, 0.081, 0.096,
0.097, 0.067, 0.111, 0.202, 0.179, 0.257, 0.171, 0.232, 0.158,
0.292, 0.096, 0.139, 0.109, 0.102, 0.189, 0.097, 0.076, 0.071,
0.234, 0.081, 0.148, 0.211, 0.057, 0.162, 0.154, 0.089, 0.127,
0.098, 0.109, 0.153, 0.401, 0.074, 0.039, 0.083, 0.202, 0.096,
0.177, 0.094, 0.104, 0.099, 0.129, 0.115, 0.151, 0.132, 0.187,
0.132, 0.141, 0.195, 0.091, 0.103, 0.157, 0.142, 0.164, 0.151,
0.121, 0.067, 0.179, 0.146, 0.101, 0.115, 0.155, 0.075, 0.139,
0.139, 0.221, 0.446, 0.079, 0.103, 0.117, 0.098, 0.086, 0.064,
0.159, 0.089, 0.223, 0.131, 0.053, 0.107, 0.171, 0.205, 0.137,
0.125, 0.173, 0.114, 0.278, 0.121, 0.192, 0.112, 0.171, 0.086,
0.158, 0.204, 0.104, 0.064, 0.091, 0.167, 0.233, 0.055, 0.123,
0.134, 0.088, 0.171, 0.133, 0.138, 0.136, 0.092, 0.133, 0.122,
0.127, 0.115, 0.116, 0.161, 0.088, 0.095, 0.231, 0.117, 0.117,
0.141, 0.078, 0.084, 0.126, 0.164, 0.084, 0.096, 0.156, 0.112,
0.095, 0.036, 0.224, 0.135, 0.092, 0.096, 0.131, 0.411, 0.187,
0.088, 0.171, 0.061, 0.064, 0.096, 0.101, 0.115, 0.197, 0.082,
0.089, 0.061, 0.211, 0.108, 0.115, 0.104, 0.118, 0.125, 0.309,
0.185, 0.151, 0.181, 0.036, 0.142, 0.161, 0.164, 0.135, 0.096,
0.089, 0.382, 0.085, 0.089, 0.116, 0.145, 0.185, 0.066, 0.113,
0.329, 0.218, 0.053, 0.112, 0.127, 0.137, 0.138, 0.232, 0.063,
0.093, 0.173, 0.104, 0.137, 0.163, 0.077, 0.103, 0.068, 0.306,
0.081, 0.109, 0.549, 0.257, 0.099, 0.078, 0.169, 0.103, 0.088,
0.292, 0.378, 0.317, 0.147, 0.142, 0.149, 0.104, 0.144, 0.131,
0.101, 0.103, 0.079, 0.169, 0.219, 0.071, 0.105, 0.107, 0.091,
0.111, 0.115, 0.066, 0.191, 0.061, 0.177, 0.048, 0.078, 0.119,
0.552, 0.179, 0.066, 0.221, 0.212, 0.041, 0.083, 0.069, 0.093,
0.174, 0.037, 0.115, 0.073, 0.173, 0.167, 0.052, 0.121, 0.076,
0.097, 0.159, 0.148, 0.106, 0.177, 0.065, 0.227, 0.196, 0.078,
0.175, 0.234, 0.208, 0.106, 0.122, 0.062, 0.085, 0.051, 0.171,
0.057, 0.104, 0.184, 0.071, 0.081, 0.147, 0.149, 0.145, 0.241,
0.258, 0.152, 0.246, 0.152, 0.087, 0.151, 0.084, 0.142, 0.106,
0.144, 0.318, 0.224, 0.232, 0.151, 0.194, 0.119, 0.111, 0.109,
0.153, 0.126, 0.134, 0.116, 0.061, 0.186, 0.106, 0.113, 0.112,
0.157, 0.148, 0.054, 0.146, 0.204, 0.082, 0.245, 0.075, 0.108,
0.084, 0.083, 0.099, 0.318, 0.186, 0.101, 0.093, 0.041, 0.168,
0.095, 0.089, 0.069, 0.149, 0.314, 0.173, 0.102, 0.056, 0.093,
0.119, 0.121, 0.226, 0.107, 0.225, 0.307, 0.242, 0.132, 0.105,
0.227, 0.094, 0.076, 0.083, 0.114, 0.136, 0.262, 0.094, 0.124,
0.106, 0.118, 0.085, 0.045, 0.193, 0.076, 0.124, 0.076, 0.148,
0.072, 0.231, 0.134, 0.126, 0.102, 0.086, 0.189, 0.145, 0.142,
0.252, 0.084, 0.116, 0.095, 0.065, 0.084, 0.132, 0.184, 0.197,
0.152, 0.106, 0.071, 0.095, 0.128, 0.093, 0.299, 0.061, 0.178,
0.285, 0.073, 0.132, 0.103, 0.099, 0.196, 0.061, 0.075, 0.143,
0.083, 0.131, 0.249, 0.092, 0.132, 0.152, 0.162, 0.133, 0.089,
0.131, 0.145, 0.156, 0.177, 0.114, 0.141, 0.073, 0.119, 0.103,
0.194, 0.076, 0.148, 0.123, 0.112, 0.125, 0.071, 0.083, 0.078,
0.141, 0.152, 0.128, 0.093, 0.112, 0.099, 0.181, 0.168, 0.115,
0.146, 0.167, 0.084, 0.161, 0.092, 0.057, 0.094, 0.095, 0.141,
0.115, 0.131, 0.111, 0.079, 0.175, 0.121, 0.359, 0.102, 0.121,
0.124, 0.133, 0.121, 0.204, 0.041, 0.246, 0.108, 0.146, 0.078,
0.135, 0.147, 0.074, 0.096, 0.271, 0.066, 0.103, 0.148, 0.125,
0.077, 0.145, 0.171, 0.397, 0.071, 0.066, 0.124, 0.058, 0.102,
0.031, 0.062, 0.088, 0.192, 0.285, 0.163, 0.144, 0.132, 0.203,
0.043, 0.118, 0.129, 0.057, 0.121, 0.142, 0.084, 0.172, 0.165,
0.056, 0.025, 0.122, 0.163, 0.169, 0.199, 0.165, 0.123, 0.147,
0.176, 0.071, 0.096, 0.122, 0.116, 0.111, 0.159, 0.114, 0.386,
0.207, 0.127, 0.185, 0.139, 0.107, 0.289, 0.129, 0.282, 0.194,
0.139, 0.099, 0.127, 0.113, 0.334, 0.097, 0.251, 0.258, 0.152,
0.077, 0.194, 0.153, 0.358, 0.079, 0.294, 0.291, 0.169, 0.215,
0.134, 0.097, 0.114, 0.175, 0.068, 0.084, 0.218, 0.128, 0.124,
0.084, 0.139, 0.131, 0.079, 0.157, 0.259, 0.128, 0.225, 0.165,
0.059, 0.138, 0.103, 0.123, 0.247, 0.126, 0.174, 0.137, 0.255,
0.091, 0.177), w = c("you", "there", "it", "it", "it", "you",
"know", "now", "there", "they", "they", "they", "you", "they",
"they", "you", "now", "it", "it", "it", "you", "up", "it", "you",
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"you", "you", "now", "there", "it", "it", "it", "you", "you",
"you", "you", "they", "you", "you"), Position = structure(c(1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 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,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
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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, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L), levels = c("prefinal",
"final"), class = "factor"), N = c(218L, 55L, 187L, 187L, 42L,
218L, 11L, 17L, 55L, 90L, 90L, 10L, 218L, 90L, 10L, 218L, 10L,
187L, 42L, 187L, 218L, 21L, 187L, 218L, 21L, 49L, 218L, 31L,
218L, 218L, 187L, 49L, 42L, 90L, 218L, 11L, 90L, 10L, 42L, 187L,
218L, 218L, 187L, 218L, 218L, 55L, 42L, 218L, 49L, 90L, 218L,
218L, 187L, 218L, 49L, 187L, 218L, 187L, 218L, 187L, 187L, 187L,
187L, 55L, 187L, 31L, 218L, 187L, 49L, 218L, 218L, 11L, 49L,
11L, 49L, 49L, 55L, 218L, 10L, 187L, 187L, 55L, 187L, 49L, 31L,
49L, 49L, 42L, 187L, 218L, 187L, 90L, 55L, 10L, 90L, 90L, 218L,
218L, 187L, 42L, 187L, 187L, 187L, 187L, 187L, 187L, 187L, 187L,
187L, 218L, 187L, 31L, 218L, 12L, 55L, 55L, 218L, 11L, 218L,
31L, 218L, 218L, 12L, 218L, 187L, 55L, 218L, 218L, 55L, 187L,
42L, 187L, 187L, 17L, 49L, 187L, 17L, 218L, 218L, 218L, 218L,
218L, 187L, 187L, 187L, 218L, 12L, 218L, 31L, 55L, 218L, 55L,
31L, 187L, 90L, 187L, 90L, 42L, 11L, 42L, 187L, 11L, 21L, 218L,
10L, 187L, 42L, 218L, 218L, 218L, 49L, 31L, 55L, 12L, 55L, 187L,
90L, 90L, 90L, 10L, 218L, 49L, 90L, 218L, 11L, 187L, 218L, 49L,
218L, 218L, 49L, 31L, 218L, 187L, 187L, 187L, 218L, 49L, 218L,
90L, 187L, 90L, 187L, 187L, 90L, 218L, 187L, 31L, 218L, 55L,
31L, 55L, 55L, 55L, 218L, 21L, 187L, 218L, 218L, 42L, 218L, 187L,
12L, 218L, 55L, 12L, 218L, 49L, 90L, 90L, 187L, 90L, 90L, 21L,
10L, 218L, 218L, 218L, 31L, 218L, 31L, 218L, 42L, 218L, 218L,
218L, 187L, 218L, 31L, 218L, 218L, 31L, 218L, 218L, 49L, 218L,
218L, 218L, 218L, 187L, 218L, 218L, 218L, 218L, 49L, 218L, 42L,
218L, 49L, 11L, 55L, 218L, 218L, 218L, 218L, 187L, 218L, 218L,
49L, 90L, 187L, 218L, 187L, 21L, 90L, 218L, 49L, 21L, 10L, 21L,
55L, 218L, 218L, 187L, 218L, 218L, 187L, 187L, 187L, 218L, 11L,
55L, 218L, 17L, 187L, 218L, 55L, 55L, 218L, 187L, 218L, 218L,
21L, 90L, 218L, 10L, 31L, 218L, 49L, 218L, 187L, 42L, 90L, 90L,
42L, 218L, 187L, 218L, 218L, 187L, 187L, 218L, 49L, 218L, 42L,
218L, 55L, 11L, 187L, 187L, 21L, 218L, 21L, 90L, 218L, 31L, 17L,
55L, 218L, 42L, 187L, 187L, 42L, 55L, 55L, 90L, 49L, 218L, 90L,
90L, 90L, 90L, 90L, 187L, 90L, 187L, 187L, 11L, 218L, 55L, 218L,
218L, 49L, 90L, 55L, 90L, 90L, 187L, 187L, 90L, 55L, 218L, 49L,
187L, 218L, 90L, 90L, 90L, 10L, 55L, 55L, 187L, 11L, 187L, 187L,
42L, 49L, 42L, 218L, 49L, 187L, 187L, 11L, 49L, 49L, 90L, 55L,
218L, 90L, 49L, 21L, 17L, 90L, 90L, 90L, 218L, 187L, 90L, 90L,
187L, 187L, 187L, 90L, 17L, 218L, 49L, 187L, 90L, 90L, 10L, 49L,
218L, 49L, 31L, 187L, 187L, 187L, 187L, 12L, 218L, 21L, 218L,
90L, 90L, 187L, 187L, 187L, 90L, 42L, 90L, 90L, 55L, 55L, 12L,
49L, 187L, 218L, 187L, 17L, 187L, 218L, 218L, 187L, 218L, 17L,
42L, 90L, 49L, 187L, 187L, 42L, 90L, 90L, 10L, 49L, 218L, 12L,
218L, 187L, 187L, 187L, 187L, 187L, 218L, 11L, 90L, 90L, 90L,
17L, 42L, 187L, 187L, 187L, 187L, 187L, 90L, 90L, 218L, 90L,
90L, 218L, 218L, 90L, 49L, 218L, 42L, 218L, 11L, 218L, 55L, 187L,
187L, 218L, 49L, 90L, 42L, 218L, 218L, 49L, 218L, 218L, 187L,
218L, 12L, 10L, 218L, 218L, 218L, 218L, 187L, 218L, 218L, 12L,
17L, 17L, 90L, 218L, 31L, 218L, 42L, 187L, 90L, 55L, 17L, 187L,
31L, 187L, 218L, 49L, 187L, 55L, 187L, 49L, 218L, 187L, 187L,
42L, 42L, 187L, 42L, 187L, 218L, 55L, 218L, 10L, 187L, 55L, 218L,
218L, 218L, 187L, 10L, 55L, 218L, 21L, 90L, 90L, 10L, 187L, 55L,
187L, 187L, 11L, 187L, 42L, 187L, 187L, 90L, 187L, 218L, 55L,
55L, 218L, 11L, 90L, 218L, 218L, 31L, 90L, 187L, 218L, 187L,
42L, 55L, 55L, 187L, 187L, 21L, 187L, 55L, 55L, 11L, 187L, 187L,
218L, 187L, 187L, 218L, 49L, 218L, 187L, 218L, 31L, 187L, 55L,
42L, 17L, 187L, 90L, 187L, 42L, 218L, 187L, 31L, 187L, 55L, 187L,
218L, 90L, 90L, 218L, 218L, 187L, 218L, 90L, 218L, 42L, 218L,
12L, 187L, 11L, 218L, 187L, 187L, 218L, 187L, 187L, 187L, 17L,
218L, 218L, 218L, 218L, 218L, 218L, 218L, 55L, 187L, 218L, 11L,
90L, 187L, 218L, 187L, 218L, 21L, 21L, 218L, 218L, 21L, 42L,
187L, 218L, 218L, 187L, 90L, 90L, 187L, 187L, 187L, 187L, 187L,
49L, 218L, 218L, 49L, 187L, 218L, 187L, 49L, 187L, 187L, 218L,
90L, 187L, 21L, 187L, 55L, 17L, 90L, 10L, 187L, 42L, 42L, 218L,
49L, 90L, 90L, 90L, 218L, 90L, 218L, 218L, 11L, 31L, 187L, 218L,
31L, 21L, 10L, 187L, 42L, 187L, 218L, 31L, 218L, 218L, 218L,
218L, 31L, 218L, 21L, 187L, 187L, 218L, 187L, 218L, 187L, 187L,
42L, 218L, 31L, 17L, 55L, 42L, 187L, 187L, 31L, 218L, 218L, 31L,
90L, 218L, 31L)), row.names = c(NA, -764L), class = c("tbl_df",
"tbl", "data.frame"))
