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R 使用geom_段连接geom_hline

r

R 使用geom_段连接geom_hline,r,ggplot2,tidyverse,R,Ggplot2,Tidyverse,问题： penguins <- structure(list(species = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,

问题：

penguins <- structure(list(species = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 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), .Label = c("Gentoo", "Chinstrap", "Adelie"), class = "factor"), 
    island = c("Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream"), bill_length_mm = c(39.1, 39.5, 40.3, NA, 
    36.7, 39.3, 38.9, 39.2, 34.1, 42, 37.8, 37.8, 41.1, 38.6, 
    34.6, 36.6, 38.7, 42.5, 34.4, 46, 37.8, 37.7, 35.9, 38.2, 
    38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 
    36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36, 44.1, 
    37, 39.6, 41.1, 37.5, 36, 42.3, 39.6, 40.1, 35, 42, 34.5, 
    41.4, 39, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 
    41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 
    42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 
    41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34, 39.6, 36.2, 
    40.8, 38.1, 40.3, 33.1, 43.2, 35, 41, 37.7, 37.8, 37.9, 39.7, 
    38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 
    38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 
    38.8, 41.5, 39, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 
    35.6, 40.2, 37, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39, 39.2, 
    36.6, 36, 37.8, 36, 41.5, 46.1, 50, 48.7, 50, 47.6, 46.5, 
    45.4, 46.7, 43.3, 46.8, 40.9, 49, 45.5, 48.4, 45.8, 49.3, 
    42, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 
    44.5, 47.8, 48.2, 50, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 
    42.6, 44.4, 44, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 
    45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45, 
    43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 
    46.2, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 
    47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50, 44.9, 50.8, 
    43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 
    49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 47.3, 46.8, 
    41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 
    44.5, 48.8, 47.2, NA, 46.8, 50.4, 45.2, 49.9, 46.5, 50, 51.3, 
    45.4, 52.7, 45.2, 46.1, 51.3, 46, 51.3, 46.6, 51.7, 47, 52, 
    45.9, 50.5, 50.3, 58, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 
    46.7, 52, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51, 49.7, 
    47.5, 47.6, 52, 46.9, 53.5, 49, 46.2, 50.9, 45.5, 50.9, 50.8, 
    50.1, 49, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 
    45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 
    50.8, 50.2), bill_depth_mm = c(18.7, 17.4, 18, NA, 19.3, 
    20.6, 17.8, 19.6, 18.1, 20.2, 17.1, 17.3, 17.6, 21.2, 21.1, 
    17.8, 19, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 
    18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17, 
    21.1, 20, 18.5, 19.3, 19.1, 18, 18.4, 18.5, 19.7, 16.9, 18.8, 
    19, 18.9, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 
    17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17, 18.2, 17.1, 18, 16.2, 
    19.1, 16.6, 19.4, 19, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 
    19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 
    18.6, 19.2, 18.8, 18, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 
    18.5, 16.1, 18.5, 17.9, 20, 16, 20, 18.6, 18.9, 17.2, 20, 
    17, 19, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17, 20.5, 17, 
    18.6, 17.2, 19.8, 17, 18.5, 15.9, 19, 17.6, 18.3, 17.1, 18, 
    17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 
    17.1, 17.2, 15.5, 17, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 
    17.1, 18.5, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 
    13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 
    14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 14.3, 15, 
    14.3, 15.3, 15.3, 14.2, 14.5, 17, 14.8, 16.3, 13.7, 17.3, 
    13.6, 15.7, 13.7, 16, 13.7, 15, 15.9, 13.9, 13.9, 15.9, 13.3, 
    15.8, 14.2, 14.1, 14.4, 15, 14.4, 15.4, 13.9, 15, 14.5, 15.3, 
    13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 14.4, 16.2, 14.2, 15, 
    15, 15.6, 15.6, 14.8, 15, 16, 14.2, 16.3, 13.8, 16.4, 14.5, 
    15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14, 17, 15, 17.1, 
    14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15, 17, 15.5, 
    15, 13.8, 16.1, 14.7, 15.8, 14, 15.1, 15.2, 15.9, 15.2, 16.3, 
    14.1, 16, 15.7, 16.2, 13.7, NA, 14.3, 15.7, 14.8, 16.1, 17.9, 
    19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 
    20.3, 17.3, 18.1, 17.1, 19.6, 20, 17.8, 18.6, 18.2, 17.3, 
    17.5, 16.6, 19.4, 17.9, 19, 18.4, 19, 17.8, 20, 16.6, 20.8, 
    16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 
    19.1, 17, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19, 17.3, 
    19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17, 
    19.8, 18.1, 18.2, 19, 18.7), flipper_length_mm = c(181, 186, 
    195, NA, 193, 190, 181, 195, 193, 190, 186, 180, 182, 191, 
    198, 185, 195, 197, 184, 194, 174, 180, 189, 185, 180, 187, 
    183, 187, 172, 180, 178, 178, 188, 184, 195, 196, 190, 180, 
    181, 184, 182, 195, 186, 196, 185, 190, 182, 179, 190, 191, 
    186, 188, 190, 200, 187, 191, 186, 193, 181, 194, 185, 195, 
    185, 192, 184, 192, 195, 188, 190, 198, 190, 190, 196, 197, 
    190, 195, 191, 184, 187, 195, 189, 196, 187, 193, 191, 194, 
    190, 189, 189, 190, 202, 205, 185, 186, 187, 208, 190, 196, 
    178, 192, 192, 203, 183, 190, 193, 184, 199, 190, 181, 197, 
    198, 191, 193, 197, 191, 196, 188, 199, 189, 189, 187, 198, 
    176, 202, 186, 199, 191, 195, 191, 210, 190, 197, 193, 199, 
    187, 190, 191, 200, 185, 193, 193, 187, 188, 190, 192, 185, 
    190, 184, 195, 193, 187, 201, 211, 230, 210, 218, 215, 210, 
    211, 219, 209, 215, 214, 216, 214, 213, 210, 217, 210, 221, 
    209, 222, 218, 215, 213, 215, 215, 215, 216, 215, 210, 220, 
    222, 209, 207, 230, 220, 220, 213, 219, 208, 208, 208, 225, 
    210, 216, 222, 217, 210, 225, 213, 215, 210, 220, 210, 225, 
    217, 220, 208, 220, 208, 224, 208, 221, 214, 231, 219, 230, 
    214, 229, 220, 223, 216, 221, 221, 217, 216, 230, 209, 220, 
    215, 223, 212, 221, 212, 224, 212, 228, 218, 218, 212, 230, 
    218, 228, 212, 224, 214, 226, 216, 222, 203, 225, 219, 228, 
    215, 228, 216, 215, 210, 219, 208, 209, 216, 229, 213, 230, 
    217, 230, 217, 222, 214, NA, 215, 222, 212, 213, 192, 196, 
    193, 188, 197, 198, 178, 197, 195, 198, 193, 194, 185, 201, 
    190, 201, 197, 181, 190, 195, 181, 191, 187, 193, 195, 197, 
    200, 200, 191, 205, 187, 201, 187, 203, 195, 199, 195, 210, 
    192, 205, 210, 187, 196, 196, 196, 201, 190, 212, 187, 198, 
    199, 201, 193, 203, 187, 197, 191, 203, 202, 194, 206, 189, 
    195, 207, 202, 193, 210, 198), body_mass_g = c(3750, 3800, 
    3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, 3300, 3700, 
    3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 
    3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 
    3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 
    3100, 4400, 3000, 4600, 3425, 2975, 3450, 4150, 3500, 4300, 
    3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 
    3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 
    3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 
    3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 
    3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 
    3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 
    3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 
    3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 
    3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 
    3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000, 
    4500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 
    4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 
    5700, 5000, 4400, 5050, 5000, 5100, 4100, 5650, 4600, 5550, 
    5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 
    3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 
    4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 
    4200, 5300, 4400, 5650, 4700, 5700, 4650, 5800, 4700, 5550, 
    4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 
    4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 
    4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 
    4850, 5600, 4975, 5500, 4725, 5500, 4700, 5500, 4575, 5500, 
    5000, 5950, 4650, 5500, 4375, 5850, 4875, 6000, 4925, NA, 
    4850, 5750, 5200, 5400, 3500, 3900, 3650, 3525, 3725, 3950, 
    3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 
    3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 
    3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 
    3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 
    3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 
    3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 
    4100, 3775), sex = structure(c(2L, 1L, 1L, NA, 1L, 2L, 1L, 
    2L, NA, NA, NA, NA, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, NA, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, NA, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 
    1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L, 1L), .Label = c("female", "male"), class = "factor"), 
    year = c(2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009)), row.names = c(NA, -344L
), spec = structure(list(cols = list(species = structure(list(), class = c("collector_character", 
"collector")), island = structure(list(), class = c("collector_character", 
"collector")), bill_length_mm = structure(list(), class = c("collector_double", 
"collector")), bill_depth_mm = structure(list(), class = c("collector_double", 
"collector")), flipper_length_mm = structure(list(), class = c("collector_double", 
"collector")), body_mass_g = structure(list(), class = c("collector_double", 
"collector")), sex = structure(list(), class = c("collector_character", 
"collector")), year = structure(list(), class = c("collector_double", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"))
我想将
geom_hline
中的
geom_片段添加到3个
stat_摘要
点中，确定每个物种的平均值

当前代码：


penguins <- structure(list(species = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 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), .Label = c("Gentoo", "Chinstrap", "Adelie"), class = "factor"), 
    island = c("Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream"), bill_length_mm = c(39.1, 39.5, 40.3, NA, 
    36.7, 39.3, 38.9, 39.2, 34.1, 42, 37.8, 37.8, 41.1, 38.6, 
    34.6, 36.6, 38.7, 42.5, 34.4, 46, 37.8, 37.7, 35.9, 38.2, 
    38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 
    36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36, 44.1, 
    37, 39.6, 41.1, 37.5, 36, 42.3, 39.6, 40.1, 35, 42, 34.5, 
    41.4, 39, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 
    41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 
    42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 
    41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34, 39.6, 36.2, 
    40.8, 38.1, 40.3, 33.1, 43.2, 35, 41, 37.7, 37.8, 37.9, 39.7, 
    38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 
    38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 
    38.8, 41.5, 39, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 
    35.6, 40.2, 37, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39, 39.2, 
    36.6, 36, 37.8, 36, 41.5, 46.1, 50, 48.7, 50, 47.6, 46.5, 
    45.4, 46.7, 43.3, 46.8, 40.9, 49, 45.5, 48.4, 45.8, 49.3, 
    42, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 
    44.5, 47.8, 48.2, 50, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 
    42.6, 44.4, 44, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 
    45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45, 
    43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 
    46.2, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 
    47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50, 44.9, 50.8, 
    43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 
    49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 47.3, 46.8, 
    41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 
    44.5, 48.8, 47.2, NA, 46.8, 50.4, 45.2, 49.9, 46.5, 50, 51.3, 
    45.4, 52.7, 45.2, 46.1, 51.3, 46, 51.3, 46.6, 51.7, 47, 52, 
    45.9, 50.5, 50.3, 58, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 
    46.7, 52, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51, 49.7, 
    47.5, 47.6, 52, 46.9, 53.5, 49, 46.2, 50.9, 45.5, 50.9, 50.8, 
    50.1, 49, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 
    45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 
    50.8, 50.2), bill_depth_mm = c(18.7, 17.4, 18, NA, 19.3, 
    20.6, 17.8, 19.6, 18.1, 20.2, 17.1, 17.3, 17.6, 21.2, 21.1, 
    17.8, 19, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 
    18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17, 
    21.1, 20, 18.5, 19.3, 19.1, 18, 18.4, 18.5, 19.7, 16.9, 18.8, 
    19, 18.9, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 
    17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17, 18.2, 17.1, 18, 16.2, 
    19.1, 16.6, 19.4, 19, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 
    19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 
    18.6, 19.2, 18.8, 18, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 
    18.5, 16.1, 18.5, 17.9, 20, 16, 20, 18.6, 18.9, 17.2, 20, 
    17, 19, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17, 20.5, 17, 
    18.6, 17.2, 19.8, 17, 18.5, 15.9, 19, 17.6, 18.3, 17.1, 18, 
    17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 
    17.1, 17.2, 15.5, 17, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 
    17.1, 18.5, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 
    13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 
    14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 14.3, 15, 
    14.3, 15.3, 15.3, 14.2, 14.5, 17, 14.8, 16.3, 13.7, 17.3, 
    13.6, 15.7, 13.7, 16, 13.7, 15, 15.9, 13.9, 13.9, 15.9, 13.3, 
    15.8, 14.2, 14.1, 14.4, 15, 14.4, 15.4, 13.9, 15, 14.5, 15.3, 
    13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 14.4, 16.2, 14.2, 15, 
    15, 15.6, 15.6, 14.8, 15, 16, 14.2, 16.3, 13.8, 16.4, 14.5, 
    15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14, 17, 15, 17.1, 
    14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15, 17, 15.5, 
    15, 13.8, 16.1, 14.7, 15.8, 14, 15.1, 15.2, 15.9, 15.2, 16.3, 
    14.1, 16, 15.7, 16.2, 13.7, NA, 14.3, 15.7, 14.8, 16.1, 17.9, 
    19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 
    20.3, 17.3, 18.1, 17.1, 19.6, 20, 17.8, 18.6, 18.2, 17.3, 
    17.5, 16.6, 19.4, 17.9, 19, 18.4, 19, 17.8, 20, 16.6, 20.8, 
    16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 
    19.1, 17, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19, 17.3, 
    19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17, 
    19.8, 18.1, 18.2, 19, 18.7), flipper_length_mm = c(181, 186, 
    195, NA, 193, 190, 181, 195, 193, 190, 186, 180, 182, 191, 
    198, 185, 195, 197, 184, 194, 174, 180, 189, 185, 180, 187, 
    183, 187, 172, 180, 178, 178, 188, 184, 195, 196, 190, 180, 
    181, 184, 182, 195, 186, 196, 185, 190, 182, 179, 190, 191, 
    186, 188, 190, 200, 187, 191, 186, 193, 181, 194, 185, 195, 
    185, 192, 184, 192, 195, 188, 190, 198, 190, 190, 196, 197, 
    190, 195, 191, 184, 187, 195, 189, 196, 187, 193, 191, 194, 
    190, 189, 189, 190, 202, 205, 185, 186, 187, 208, 190, 196, 
    178, 192, 192, 203, 183, 190, 193, 184, 199, 190, 181, 197, 
    198, 191, 193, 197, 191, 196, 188, 199, 189, 189, 187, 198, 
    176, 202, 186, 199, 191, 195, 191, 210, 190, 197, 193, 199, 
    187, 190, 191, 200, 185, 193, 193, 187, 188, 190, 192, 185, 
    190, 184, 195, 193, 187, 201, 211, 230, 210, 218, 215, 210, 
    211, 219, 209, 215, 214, 216, 214, 213, 210, 217, 210, 221, 
    209, 222, 218, 215, 213, 215, 215, 215, 216, 215, 210, 220, 
    222, 209, 207, 230, 220, 220, 213, 219, 208, 208, 208, 225, 
    210, 216, 222, 217, 210, 225, 213, 215, 210, 220, 210, 225, 
    217, 220, 208, 220, 208, 224, 208, 221, 214, 231, 219, 230, 
    214, 229, 220, 223, 216, 221, 221, 217, 216, 230, 209, 220, 
    215, 223, 212, 221, 212, 224, 212, 228, 218, 218, 212, 230, 
    218, 228, 212, 224, 214, 226, 216, 222, 203, 225, 219, 228, 
    215, 228, 216, 215, 210, 219, 208, 209, 216, 229, 213, 230, 
    217, 230, 217, 222, 214, NA, 215, 222, 212, 213, 192, 196, 
    193, 188, 197, 198, 178, 197, 195, 198, 193, 194, 185, 201, 
    190, 201, 197, 181, 190, 195, 181, 191, 187, 193, 195, 197, 
    200, 200, 191, 205, 187, 201, 187, 203, 195, 199, 195, 210, 
    192, 205, 210, 187, 196, 196, 196, 201, 190, 212, 187, 198, 
    199, 201, 193, 203, 187, 197, 191, 203, 202, 194, 206, 189, 
    195, 207, 202, 193, 210, 198), body_mass_g = c(3750, 3800, 
    3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, 3300, 3700, 
    3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 
    3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 
    3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 
    3100, 4400, 3000, 4600, 3425, 2975, 3450, 4150, 3500, 4300, 
    3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 
    3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 
    3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 
    3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 
    3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 
    3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 
    3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 
    3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 
    3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 
    3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000, 
    4500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 
    4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 
    5700, 5000, 4400, 5050, 5000, 5100, 4100, 5650, 4600, 5550, 
    5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 
    3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 
    4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 
    4200, 5300, 4400, 5650, 4700, 5700, 4650, 5800, 4700, 5550, 
    4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 
    4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 
    4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 
    4850, 5600, 4975, 5500, 4725, 5500, 4700, 5500, 4575, 5500, 
    5000, 5950, 4650, 5500, 4375, 5850, 4875, 6000, 4925, NA, 
    4850, 5750, 5200, 5400, 3500, 3900, 3650, 3525, 3725, 3950, 
    3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 
    3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 
    3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 
    3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 
    3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 
    3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 
    4100, 3775), sex = structure(c(2L, 1L, 1L, NA, 1L, 2L, 1L, 
    2L, NA, NA, NA, NA, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, NA, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, NA, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 
    1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L, 1L), .Label = c("female", "male"), class = "factor"), 
    year = c(2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009)), row.names = c(NA, -344L
), spec = structure(list(cols = list(species = structure(list(), class = c("collector_character", 
"collector")), island = structure(list(), class = c("collector_character", 
"collector")), bill_length_mm = structure(list(), class = c("collector_double", 
"collector")), bill_depth_mm = structure(list(), class = c("collector_double", 
"collector")), flipper_length_mm = structure(list(), class = c("collector_double", 
"collector")), body_mass_g = structure(list(), class = c("collector_double", 
"collector")), sex = structure(list(), class = c("collector_character", 
"collector")), year = structure(list(), class = c("collector_double", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"))



库（tidyverse）
图书馆（比例尺）
图书馆（showtext）
谷歌（“明天”、“明天”）
谷歌（“明天”、“明天”）
showtext_auto（）
主题套装（主题灯（基本尺寸=24，基本系列=“明天”））
cbbPalette%
拉力（车身重量平均值）
企鹅%>%
na.省略（）%>%
分组依据（物种、岛屿、性别）%>%
ggplot（aes（x=重新排序（物种，-身体质量，y=身体质量，颜色=物种））+
几何抖动（位置=位置抖动（种子=2020，宽度=0.2），α=0.25，大小=2）+
统计汇总（fun=mean，geom=“point”，size=5，alpha=1）+
geom_hline（aes（yintercept=身体质量平均值），color=“gray70”，size=0.6）+
coord_flip（）+
比例\颜色\手册（值=cbbPalette）+
比例y连续（标签=比例：：逗号）+
实验室（x=NULL，y=“体重（g）”）+
主题（legend.position=“无”，
axis.title=元素\文本（family=“明天”，大小=13），
axis.text.y=元素\文本（family=“明天”，大小=15），
axis.text.x=元素\文本（family=“明天”，大小=15），
panel.grid=element\u blank（）


电流输出：


所需输出：


数据：


penguins <- structure(list(species = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 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), .Label = c("Gentoo", "Chinstrap", "Adelie"), class = "factor"), 
    island = c("Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Torgersen", "Torgersen", "Torgersen", "Torgersen", "Torgersen", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", "Biscoe", 
    "Biscoe", "Biscoe", "Biscoe", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", "Dream", 
    "Dream", "Dream"), bill_length_mm = c(39.1, 39.5, 40.3, NA, 
    36.7, 39.3, 38.9, 39.2, 34.1, 42, 37.8, 37.8, 41.1, 38.6, 
    34.6, 36.6, 38.7, 42.5, 34.4, 46, 37.8, 37.7, 35.9, 38.2, 
    38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 
    36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36, 44.1, 
    37, 39.6, 41.1, 37.5, 36, 42.3, 39.6, 40.1, 35, 42, 34.5, 
    41.4, 39, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 
    41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 
    42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 
    41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34, 39.6, 36.2, 
    40.8, 38.1, 40.3, 33.1, 43.2, 35, 41, 37.7, 37.8, 37.9, 39.7, 
    38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 
    38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 
    38.8, 41.5, 39, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 
    35.6, 40.2, 37, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39, 39.2, 
    36.6, 36, 37.8, 36, 41.5, 46.1, 50, 48.7, 50, 47.6, 46.5, 
    45.4, 46.7, 43.3, 46.8, 40.9, 49, 45.5, 48.4, 45.8, 49.3, 
    42, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 
    44.5, 47.8, 48.2, 50, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 
    42.6, 44.4, 44, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 
    45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45, 
    43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 
    46.2, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 
    47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50, 44.9, 50.8, 
    43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 
    49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 47.3, 46.8, 
    41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 
    44.5, 48.8, 47.2, NA, 46.8, 50.4, 45.2, 49.9, 46.5, 50, 51.3, 
    45.4, 52.7, 45.2, 46.1, 51.3, 46, 51.3, 46.6, 51.7, 47, 52, 
    45.9, 50.5, 50.3, 58, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 
    46.7, 52, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51, 49.7, 
    47.5, 47.6, 52, 46.9, 53.5, 49, 46.2, 50.9, 45.5, 50.9, 50.8, 
    50.1, 49, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 
    45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 
    50.8, 50.2), bill_depth_mm = c(18.7, 17.4, 18, NA, 19.3, 
    20.6, 17.8, 19.6, 18.1, 20.2, 17.1, 17.3, 17.6, 21.2, 21.1, 
    17.8, 19, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 
    18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17, 
    21.1, 20, 18.5, 19.3, 19.1, 18, 18.4, 18.5, 19.7, 16.9, 18.8, 
    19, 18.9, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 
    17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17, 18.2, 17.1, 18, 16.2, 
    19.1, 16.6, 19.4, 19, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 
    19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 
    18.6, 19.2, 18.8, 18, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 
    18.5, 16.1, 18.5, 17.9, 20, 16, 20, 18.6, 18.9, 17.2, 20, 
    17, 19, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17, 20.5, 17, 
    18.6, 17.2, 19.8, 17, 18.5, 15.9, 19, 17.6, 18.3, 17.1, 18, 
    17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 
    17.1, 17.2, 15.5, 17, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 
    17.1, 18.5, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 
    13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 
    14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 14.3, 15, 
    14.3, 15.3, 15.3, 14.2, 14.5, 17, 14.8, 16.3, 13.7, 17.3, 
    13.6, 15.7, 13.7, 16, 13.7, 15, 15.9, 13.9, 13.9, 15.9, 13.3, 
    15.8, 14.2, 14.1, 14.4, 15, 14.4, 15.4, 13.9, 15, 14.5, 15.3, 
    13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 14.4, 16.2, 14.2, 15, 
    15, 15.6, 15.6, 14.8, 15, 16, 14.2, 16.3, 13.8, 16.4, 14.5, 
    15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14, 17, 15, 17.1, 
    14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15, 17, 15.5, 
    15, 13.8, 16.1, 14.7, 15.8, 14, 15.1, 15.2, 15.9, 15.2, 16.3, 
    14.1, 16, 15.7, 16.2, 13.7, NA, 14.3, 15.7, 14.8, 16.1, 17.9, 
    19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 
    20.3, 17.3, 18.1, 17.1, 19.6, 20, 17.8, 18.6, 18.2, 17.3, 
    17.5, 16.6, 19.4, 17.9, 19, 18.4, 19, 17.8, 20, 16.6, 20.8, 
    16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 
    19.1, 17, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19, 17.3, 
    19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17, 
    19.8, 18.1, 18.2, 19, 18.7), flipper_length_mm = c(181, 186, 
    195, NA, 193, 190, 181, 195, 193, 190, 186, 180, 182, 191, 
    198, 185, 195, 197, 184, 194, 174, 180, 189, 185, 180, 187, 
    183, 187, 172, 180, 178, 178, 188, 184, 195, 196, 190, 180, 
    181, 184, 182, 195, 186, 196, 185, 190, 182, 179, 190, 191, 
    186, 188, 190, 200, 187, 191, 186, 193, 181, 194, 185, 195, 
    185, 192, 184, 192, 195, 188, 190, 198, 190, 190, 196, 197, 
    190, 195, 191, 184, 187, 195, 189, 196, 187, 193, 191, 194, 
    190, 189, 189, 190, 202, 205, 185, 186, 187, 208, 190, 196, 
    178, 192, 192, 203, 183, 190, 193, 184, 199, 190, 181, 197, 
    198, 191, 193, 197, 191, 196, 188, 199, 189, 189, 187, 198, 
    176, 202, 186, 199, 191, 195, 191, 210, 190, 197, 193, 199, 
    187, 190, 191, 200, 185, 193, 193, 187, 188, 190, 192, 185, 
    190, 184, 195, 193, 187, 201, 211, 230, 210, 218, 215, 210, 
    211, 219, 209, 215, 214, 216, 214, 213, 210, 217, 210, 221, 
    209, 222, 218, 215, 213, 215, 215, 215, 216, 215, 210, 220, 
    222, 209, 207, 230, 220, 220, 213, 219, 208, 208, 208, 225, 
    210, 216, 222, 217, 210, 225, 213, 215, 210, 220, 210, 225, 
    217, 220, 208, 220, 208, 224, 208, 221, 214, 231, 219, 230, 
    214, 229, 220, 223, 216, 221, 221, 217, 216, 230, 209, 220, 
    215, 223, 212, 221, 212, 224, 212, 228, 218, 218, 212, 230, 
    218, 228, 212, 224, 214, 226, 216, 222, 203, 225, 219, 228, 
    215, 228, 216, 215, 210, 219, 208, 209, 216, 229, 213, 230, 
    217, 230, 217, 222, 214, NA, 215, 222, 212, 213, 192, 196, 
    193, 188, 197, 198, 178, 197, 195, 198, 193, 194, 185, 201, 
    190, 201, 197, 181, 190, 195, 181, 191, 187, 193, 195, 197, 
    200, 200, 191, 205, 187, 201, 187, 203, 195, 199, 195, 210, 
    192, 205, 210, 187, 196, 196, 196, 201, 190, 212, 187, 198, 
    199, 201, 193, 203, 187, 197, 191, 203, 202, 194, 206, 189, 
    195, 207, 202, 193, 210, 198), body_mass_g = c(3750, 3800, 
    3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, 3300, 3700, 
    3200, 3800, 4400, 3700, 3450, 4500, 3325, 4200, 3400, 3600, 
    3800, 3950, 3800, 3800, 3550, 3200, 3150, 3950, 3250, 3900, 
    3300, 3900, 3325, 4150, 3950, 3550, 3300, 4650, 3150, 3900, 
    3100, 4400, 3000, 4600, 3425, 2975, 3450, 4150, 3500, 4300, 
    3450, 4050, 2900, 3700, 3550, 3800, 2850, 3750, 3150, 4400, 
    3600, 4050, 2850, 3950, 3350, 4100, 3050, 4450, 3600, 3900, 
    3550, 4150, 3700, 4250, 3700, 3900, 3550, 4000, 3200, 4700, 
    3800, 4200, 3350, 3550, 3800, 3500, 3950, 3600, 3550, 4300, 
    3400, 4450, 3300, 4300, 3700, 4350, 2900, 4100, 3725, 4725, 
    3075, 4250, 2925, 3550, 3750, 3900, 3175, 4775, 3825, 4600, 
    3200, 4275, 3900, 4075, 2900, 3775, 3350, 3325, 3150, 3500, 
    3450, 3875, 3050, 4000, 3275, 4300, 3050, 4000, 3325, 3500, 
    3500, 4475, 3425, 3900, 3175, 3975, 3400, 4250, 3400, 3475, 
    3050, 3725, 3000, 3650, 4250, 3475, 3450, 3750, 3700, 4000, 
    4500, 5700, 4450, 5700, 5400, 4550, 4800, 5200, 4400, 5150, 
    4650, 5550, 4650, 5850, 4200, 5850, 4150, 6300, 4800, 5350, 
    5700, 5000, 4400, 5050, 5000, 5100, 4100, 5650, 4600, 5550, 
    5250, 4700, 5050, 6050, 5150, 5400, 4950, 5250, 4350, 5350, 
    3950, 5700, 4300, 4750, 5550, 4900, 4200, 5400, 5100, 5300, 
    4850, 5300, 4400, 5000, 4900, 5050, 4300, 5000, 4450, 5550, 
    4200, 5300, 4400, 5650, 4700, 5700, 4650, 5800, 4700, 5550, 
    4750, 5000, 5100, 5200, 4700, 5800, 4600, 6000, 4750, 5950, 
    4625, 5450, 4725, 5350, 4750, 5600, 4600, 5300, 4875, 5550, 
    4950, 5400, 4750, 5650, 4850, 5200, 4925, 4875, 4625, 5250, 
    4850, 5600, 4975, 5500, 4725, 5500, 4700, 5500, 4575, 5500, 
    5000, 5950, 4650, 5500, 4375, 5850, 4875, 6000, 4925, NA, 
    4850, 5750, 5200, 5400, 3500, 3900, 3650, 3525, 3725, 3950, 
    3250, 3750, 4150, 3700, 3800, 3775, 3700, 4050, 3575, 4050, 
    3300, 3700, 3450, 4400, 3600, 3400, 2900, 3800, 3300, 4150, 
    3400, 3800, 3700, 4550, 3200, 4300, 3350, 4100, 3600, 3900, 
    3850, 4800, 2700, 4500, 3950, 3650, 3550, 3500, 3675, 4450, 
    3400, 4300, 3250, 3675, 3325, 3950, 3600, 4050, 3350, 3450, 
    3250, 4050, 3800, 3525, 3950, 3650, 3650, 4000, 3400, 3775, 
    4100, 3775), sex = structure(c(2L, 1L, 1L, NA, 1L, 2L, 1L, 
    2L, NA, NA, NA, NA, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, NA, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, NA, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 
    1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L, 1L), .Label = c("female", "male"), class = "factor"), 
    year = c(2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
    2007, 2007, 2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
    2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
    2009, 2009, 2009, 2009, 2009, 2009)), row.names = c(NA, -344L
), spec = structure(list(cols = list(species = structure(list(), class = c("collector_character", 
"collector")), island = structure(list(), class = c("collector_character", 
"collector")), bill_length_mm = structure(list(), class = c("collector_double", 
"collector")), bill_depth_mm = structure(list(), class = c("collector_double", 
"collector")), flipper_length_mm = structure(list(), class = c("collector_double", 
"collector")), body_mass_g = structure(list(), class = c("collector_double", 
"collector")), sex = structure(list(), class = c("collector_character", 
"collector")), year = structure(list(), class = c("collector_double", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"))



企鹅你可以试试这个。可能不是最优雅的解决方案，但您可以添加特定数据的子集，并测试每个新的
统计摘要的限值
：


set.seed(2020)
cbbPalette <- c("#27a8f7", "#88c0d0", "#B9C8D7")

body_mass_avg <- penguins %>% 
  summarize(body_mass_avg = mean(body_mass_g, na.rm = TRUE)) %>% 
  pull(body_mass_avg)


penguins %>% 
  na.omit() %>% 
  group_by(species, island, sex) %>%
  ggplot(aes(x=reorder(species, -body_mass_g), y = body_mass_g, color = species))+
  geom_jitter(position = position_jitter(seed = 2020, width = 0.2), alpha = 0.25, size = 2)+
  stat_summary(fun = mean, geom = "point", size = 5, alpha = 1)+
  stat_summary(data = penguins %>% filter(species %in% c('Adelie','Chinstrap')),fun="mean", geom="segment", mapping=aes(xend=..x.., yend=..y.. + 500))+
  stat_summary(data = penguins %>% filter(species %in% c('Gentoo')),fun="mean", geom="segment", mapping=aes(xend=..x.., yend=..y.. - 900))+
  geom_hline(aes(yintercept = body_mass_avg), color = "gray70", size = 0.6)+
  coord_flip() +
  scale_color_manual(values=cbbPalette)+
  scale_y_continuous(labels = scales::comma)+
  labs(x = NULL, y = "Body Mass (g)")+
  theme(legend.position = "NONE",
        axis.title = element_text(size = 13),
        axis.text.y = element_text(size = 15),
        axis.text.x = element_text(size = 15),
        panel.grid = element_blank())



set.seed（2020年）
cbbPalette%
拉力（车身重量平均值）
企鹅%>%
na.省略（）%>%
分组依据（物种、岛屿、性别）%>%
ggplot（aes（x=重新排序（物种，-身体质量，y=身体质量，颜色=物种））+
几何抖动（位置=位置抖动（种子=2020，宽度=0.2），α=0.25，大小=2）+
统计汇总（fun=mean，geom=“point”，size=5，alpha=1）+
统计汇总（数据=企鹅%>%过滤器（物种百分比在%c（'Adelie'，'Chinstrap'）），fun=“mean”，geom=“segment”，mapping=aes（xend=…x…，yend=…y…+500））+
统计汇总（数据=企鹅%>%过滤器（物种百分比在%c（'Gentoo'））、fun=“平均”、geom=“分段”、mapping=aes（xend=…x…，yend=…y…-900））+
geom_hline（aes（yintercept=身体质量平均值），color=“gray70”，size=0.6）+
coord_flip（）+
比例\颜色\手册（值=cbbPalette）+
比例y连续（标签=比例：：逗号）+
实验室（x=NULL，y=“体重（g）”）+
主题（legend.position=“无”，
axis.title=元素\文本（大小=13），
axis.text.y=元素\文本（大小=15），
axis.text.x=元素\文本（大小=15），
panel.grid=element\u blank（）



您只需一次调用
geom_段
，即可通过转换
geom_段
中的数据来完成此操作：


theme_set(theme_light(base_size = 24))

cbbPalette <- c("#27a8f7", "#88c0d0", "#B9C8D7")

set.seed(2019)

body_mass_avg = mean(penguins$body_mass_g, na.rm=TRUE)

penguins %>% 
  na.omit() %>% 
  group_by(species, island) %>% 
  mutate(m = mean(body_mass_g)) %>% 
  arrange(-m) %>% 
  ungroup %>% 
  mutate(species=factor(species, levels=unique(species))) %>% 
  ggplot(aes(x=species, y = body_mass_g, color = species))+
    geom_jitter(position = position_jitter(seed = 2020, width = 0.2, height=0), 
                alpha = 0.25, size = 2)+
    stat_summary(fun = mean, geom = "point", size = 4, alpha = 1)+
    geom_hline(aes(yintercept = body_mass_avg), color = "gray70", size = 0.6) +
    geom_segment(data=. %>% 
                   group_by(species, island) %>% 
                   summarise(body_mass_g=mean(body_mass_g)) %>% 
                   mutate(body_mass_avg=body_mass_avg),
                 aes(y=body_mass_avg, yend=body_mass_g, xend=species), size=1) +
    coord_flip() +
    scale_color_manual(values=cbbPalette) +
    scale_y_continuous(labels = scales::comma)+
    labs(x = NULL, y = "Body Mass (g)")


主题集（主题灯（基本尺寸=24））
cbbPalette%
na.省略（）%>%
分组依据（物种、岛屿）%>%
突变（m=平均值（体重））%>%
排列（-m）%>%
解组%>%
突变（物种=因子（物种，等级=唯一（物种）））%>%
ggplot（aes（x=物种，y=身体质量，颜色=物种））+
几何抖动（位置=位置抖动（种子=2020，宽度=0.2，高度=0），
α=0.25，尺寸=2）+
统计汇总（fun=mean，geom=“point”，size=4，alpha=1）+
geom_hline（aes（yintercept=身体质量平均值），color=“gray70”，size=0.6）+
geom_段（数据=.%>%
分组依据（物种、岛屿）%>%
总结（身体质量=平均（身体质量））%>%
变异（体质量平均值=体质量平均值），
aes（y=身体质量平均值，yend=身体质量，xend=物种），尺寸=1）+
coord_flip（）+
比例\颜色\手册（值=cbbPalette）+
比例y连续（标签=比例：：逗号）+
实验室（x=NULL，y=“体重（g）”）

我通常发现在绘图之前创建一个小摘要数据框比较容易。这使得绘图本身变得微不足道

mean_企鹅%
突变（物种=重新排序（物种，-体质量），
总平均值=平均值（体重，na.rm=真实值））%>%
分组依据（物种，总平均值）%>%
汇总（平均值=平均值（体重，na.rm=真实值））
企鹅%>%
na.省略（）%>%
分组依据（物种、岛屿、性别）%>%
ggplot（aes（x=重新排序（物种，-身体质量，y=身体质量，颜色=物种））+
几何抖动（位置=位置抖动（种子=2020，宽度=0.2），α=0.25，大小=2）+
统计汇总（fun=mean，geom=“point”，size=5，alpha=1）+
geom_hline（aes（yintercept=身体质量平均值），color=“gray70”，size=0.6）+
geom_段（数据=平均值），
aes（x=物种，xend=物种，y=平均值，yend=总体平均值））+
coord_flip（）+
比例\颜色\手册（值=cbbPalette）+
比例y连续（标签=比例：：逗号）+
实验室（x=NULL，y=“体重（g）”）+
主题（legend.position=“无”，
axis.title=元素\文本（family=“明天”，大小=13），
axis.text.y=元素\文本（family=“明天”，大小=15），
axis.text.x=元素\文本（family=“明天”，大小=15），
panel.grid=element\u blank（）