下面是我使用ggplot2绘制的图（问题末尾的数据dput）。

这里我用不同的方法计算了一些均值，我在不同的方面展示了这些方法。

data %>%
  ggplot() +
  aes(
    x = grad,
    y = mean
  ) +
  geom_errorbar(
    aes(
      ymin = lb,
      ymax = ub
    ),
    width = 0.2,
    alpha = 0.4
  ) +
  geom_point(
    size = 2
  ) +
  geom_line(
    linewidth = 0.8
  ) +
  geom_hline(
    yintercept = 50,
    linetype = "dashed",
    colour = "#FF0000"
  ) +
  facet_wrap(
    ~ method,
    ncol = 2,
    scales = "free"
  ) +
  theme_bw()

对我来说，既要看到误差线，也要比较不同方法之间的点的值，这很重要。有没有一种方法可以在y轴上做某种轴变换（如scale_y_log10()），这将基本上“放大”的y轴约50？（使得50和51之间差距大于51和52之间的间隙，等等。并且这围绕50 °对称，使得49和50之间的间隙与50和51之间的间隙相同，并且48和49之间的间隙与51和52之间的间隙相同，等等）。
DPUT：

structure(list(method = structure(c(1L, 2L, 3L, 4L, 6L, 5L, 1L, 
2L, 3L, 4L, 6L, 5L, 1L, 2L, 3L, 4L, 6L, 5L, 1L, 2L, 3L, 4L, 6L, 
5L, 1L, 2L, 3L, 4L, 6L, 5L), .Label = c("raw", "norm", "bank", 
"rand", "floor", "ceil"), class = "factor"), grad = c(2, 2, 2, 
2, 2, 2, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 
10, 10, 10, 10, 10, 10), mean = c(49.99667915, 50.2454844, 49.9967503, 
49.996759, 50.2454844, 49.7478739, 49.994064725, 50.1187028, 
49.9940469, 49.9940169, 50.3682549, 49.6200129, 50.00164035, 
50.0847906, 50.0015683, 50.0016874, 50.4176783, 49.5857625, 50.00176225, 
50.0643196, 50.0019263, 50.0018275, 50.4386578, 49.5647442, 50.00713535, 
50.0572266, 50.0072163, 50.007149, 50.456595, 49.5574979), sd = c(0.91473404644074, 
0.91478017995276, 0.9151151931487, 0.914942290015281, 0.91478017995276, 
0.914757425175375, 0.912161337763246, 0.912320011976382, 0.912328572194664, 
0.912293149537724, 0.912251012998649, 0.912065855377804, 0.909061418603362, 
0.909071357608941, 0.909021915808113, 0.909025127237655, 0.909130605608961, 
0.909030928525895, 0.910690862745318, 0.910911812026153, 0.91094121042864, 
0.910847754625854, 0.910551266903806, 0.910647546563305, 0.906676002734588, 
0.906779575074459, 0.906664057457677, 0.906712448654729, 0.906661779570118, 
0.906787505291932), ub = c(51.7895578810238, 52.0384535527074, 
51.7903760785715, 51.7900458884299, 52.0384535527074, 51.5407984533437, 
51.781900947016, 51.9068500234737, 51.7822109015015, 51.7821114730939, 
52.1562668854774, 51.4076619765405, 51.7834007304626, 51.8665704609135, 
51.7832512549839, 51.7833766493858, 52.1995742869936, 51.3674631199108, 
51.7867163409808, 51.8497067515713, 51.7873710724401, 51.7870890990667, 
52.2233382831315, 51.3496133912641, 51.7842203153598, 51.8345145671459, 
51.7842778526171, 51.7843053993633, 52.2336520879574, 51.3348014103722
), lb = c(48.2038004189762, 48.4525152472926, 48.2031245214285, 
48.2034721115701, 48.4525152472926, 47.9549493466563, 48.206228502984, 
48.3305555765263, 48.2058828984985, 48.2059223269061, 48.5802429145226, 
47.8323638234595, 48.2198799695374, 48.3030107390865, 48.2198853450161, 
48.2199981506142, 48.6357823130064, 47.8040618800892, 48.2168081590192, 
48.2789324484287, 48.2164815275599, 48.2165659009333, 48.6539773168685, 
47.7798750087359, 48.2300503846402, 48.2799386328541, 48.230154747383, 
48.2299926006367, 48.6795379120426, 47.7801943896278)), row.names = c(NA, 
-30L), class = c("tbl_df", "tbl", "data.frame"))