可变变量组的ggplot平均值

Question

我有以下代码和数据：

library(dplyr)
library(ggplot2)

d %>% 
    ggplot() +
    coord_flip() +
    ggforce::geom_sina(aes(
      x = variable,
      y = value,
      color = stdfvalue),
      method = "counts", maxwidth = 0.7, alpha = 0.7)

如何通过各组变量绘制“垂直”平均线？

也就是说，从“风”的平均值开始，然后画一条线到“温度”的平均点，然后再画到“散云”的平均点，等等。因此垂直画一条线的平均点每个变量。

数据：

d <- structure(list(model = c("2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", 
"2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22", "2011-10-12 to 2017-10-10", 
"2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22", 
"2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22", 
"2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", "2011-10-12 to 2017-10-10", 
"2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22", "2011-10-12 to 2017-10-10", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2011-10-12 to 2017-10-10", "2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", 
"2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", 
"2013-07-24 to 2019-07-22", "2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", 
"2011-10-12 to 2017-10-10", "2011-10-12 to 2017-10-10", "2011-10-12 to 2017-10-10", 
"2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", 
"2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22", 
"2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", "2011-10-12 to 2017-10-10", 
"2013-07-24 to 2019-07-22", "2011-10-12 to 2017-10-10", "2011-10-12 to 2017-10-10", 
"2013-07-24 to 2019-07-22", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2011-10-12 to 2017-10-10", "2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", 
"2010-01-01 to 2015-12-31", "2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", 
"2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2010-01-01 to 2015-12-31", "2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22", 
"2011-10-12 to 2017-10-10", "2010-01-01 to 2015-12-31", "2011-10-12 to 2017-10-10", 
"2013-07-24 to 2019-07-22", "2013-07-24 to 2019-07-22"), variable = c("Temp", 
"Holiday", "Humidity", "Temp", "Humidity", "Temp", "Scattered Clouds", 
"Scattered Clouds", "Scattered Clouds", "Humidity", "Holiday", 
"Protocol Active", "Holiday", "Temp", "Wind", "Humidity", "Scattered Clouds", 
"Temp", "Holiday", "Holiday", "Humidity", "Wind", "Protocol Active", 
"Wind", "Protocol Active", "Temp", "Humidity", "Temp", "Temp", 
"Wind", "Holiday", "Wind", "Humidity", "Wind", "Holiday", "Scattered Clouds", 
"Wind", "Humidity", "Humidity", "Scattered Clouds", "Holiday", 
"Holiday", "Wind", "Wind", "Wind", "Holiday", "Scattered Clouds", 
"Wind", "Wind", "Scattered Clouds", "Wind", "Protocol Active", 
"Humidity", "Humidity", "Wind", "Protocol Active", "Protocol Active", 
"Holiday", "Wind", "Humidity", "Protocol Active", "Humidity", 
"Humidity", "Holiday", "Scattered Clouds", "Holiday", "Scattered Clouds", 
"Scattered Clouds", "Temp", "Temp", "Humidity", "Scattered Clouds", 
"Humidity", "Wind", "Holiday", "Temp", "Wind", "Humidity", "Holiday", 
"Scattered Clouds", "Holiday", "Wind", "Scattered Clouds", "Protocol Active", 
"Scattered Clouds", "Humidity", "Humidity", "Scattered Clouds", 
"Wind", "Scattered Clouds", "Protocol Active", "Humidity", "Wind", 
"Scattered Clouds", "Temp", "Temp", "Humidity", "Temp", "Wind", 
"Holiday"), value = c(0.000459374103229493, -0.253675699234009, 
-0.0786094292998314, -0.196785241365433, -0.0280073583126068, 
-0.1349718272686, 0.0337677411735058, -0.199854329228401, -0.151771619915962, 
-0.033388290554285, -0.197295814752579, -0.0150858527049422, 
-0.158438324928284, -0.0510015822947025, -0.256258130073547, 
-0.0824626013636589, -0.0339591130614281, -0.163890063762665, 
-0.0855430290102959, -0.119788505136967, 0.0866491049528122, 
-0.119025081396103, 0.188910558819771, 0.366333365440369, 0.199591532349586, 
0.149334877729416, 0.0670526176691055, 0.0176683943718672, -0.0365814939141273, 
-0.154049754142761, -0.228669106960297, -0.196197375655174, -0.0200027860701084, 
-0.12459571659565, -0.296628773212433, -0.08418869972229, -0.0166760385036469, 
-0.0674063041806221, -0.0115637453272939, -0.0849187523126602, 
-0.309920370578766, -0.255993634462357, -0.122082889080048, -0.100378297269344, 
-0.10309936106205, -0.282891929149628, -0.017831839621067, 0.995135962963104, 
-0.0860754400491714, -0.0530533157289028, 0.690496385097504, 
-0.00516092125326395, 0.122894659638405, -0.0707448422908783, 
-0.207068875432014, 0.00719616562128067, -0.0254188310354948, 
-0.152992576360703, -0.532763719558716, -0.0513307563960552, 
0.000162361699040048, -0.042680311948061, -0.00825674924999475, 
-0.31216362118721, -0.0360889285802841, -0.257501393556595, -0.0178098864853382, 
-0.248683765530586, 0.0639149844646454, 0.16026109457016, 0.0928365215659142, 
-0.198139742016792, 0.10240363329649, 0.0677793100476265, -0.183281898498535, 
-0.158823877573013, 0.795968234539032, 0.057108536362648, -0.227293521165848, 
-0.101821482181549, -0.139844477176666, 0.466279178857803, -0.140976637601852, 
-0.0496001504361629, -0.117273397743702, 0.0588472411036491, 
0.0477366708219051, -0.13063570857048, -0.0677084028720856, -0.124116607010365, 
-0.0330690667033195, 0.13056093454361, -0.0848147794604301, -0.0219597667455673, 
-0.305300086736679, -0.149264678359032, 0.113734841346741, 0.00306061143055558, 
0.376322239637375, -0.139279842376709), rfvalue = c(86.258064516129, 
0, 49.7096774193548, 70.75, 35.3225806451613, 60.1034482758621, 
1, 1, 1, 71.0357142857143, 0, 0, 0, 44.3571428571429, 6.42857142857143, 
61.4838709677419, 0, 62.7407407407407, 0, 0, 34, 4.29032258064516, 
0, 3.06451612903226, 0, 45.3846153846154, 33.5806451612903, 77.9032258064516, 
78.7741935483871, 5.35714285714286, 0, 11.3225806451613, 57.2692307692308, 
6.83870967741935, 0, 1, 3.89473684210526, 59.4838709677419, 55.5714285714286, 
0, 0, 0, 4.88461538461539, 6.14285714285714, 4.52941176470588, 
0, 1, 0.730769230769231, 5.45161290322581, 1, 1.3, 0, 26.1935483870968, 
31.1612903225806, 9.46875, 0, 0, 0, 21.5555555555556, 56.625, 
0, 49.1481481481481, 59.1111111111111, 0, 1, 0, 1, 0, 79.0967741935484, 
44.8518518518519, 34.1290322580645, 1, 25.6774193548387, 3.53571428571429, 
1, 56.6, 1.22222222222222, 72.1481481481482, 0, 1, 0, 0.935483870967742, 
1, 0, 1, 56.258064516129, 64.258064516129, 0, 4.14814814814815, 
0, 0, 15.5666666666667, 4.51612903225806, 1, 77.625, 56.8, 23.6896551724138, 
86.1034482758621, 2.70967741935484, 0), stdfvalue = c(0.864929959091151, 
0, 0.42152133580705, 0.628319713993871, 0.256029684601113, 0.443066319122509, 
1, 1, 1, 0.666830108666843, 0, 0, 0, 0.195424862735886, 0.257195445075949, 
0.5569573283859, 0, 0.501513259940226, 0, 0, 0.240816326530612, 
0.172937293729373, 0, 0.120975734518367, 0, 0.214191815233131, 
0.235992578849722, 0.741573033707865, 0.755362614913177, 0.216595945308817, 
0, 0.455640614800309, 0.508477237048666, 0.27380305362338, 0, 
1, 0.156748306409588, 0.533951762523191, 0.488947786906971, 0, 
0, 0, 0.197258187357197, 0.246004900090711, 0.180293326448518, 
0, 1, 0.0272658035034273, 0.22046204620462, 1, 0.0505610561056106, 
0, 0.151020408163265, 0.208163265306122, 0.384859735973597, 0, 
0, 0, 0.87950128346168, 0.501066790352505, 0, 0.415062186490758, 
0.529663986806844, 0, 1, 0, 1, 0, 0.76046986721144, 0.203205129451128, 
0.242300556586271, 1, 0.145083487940631, 0.140487635324737, 1, 
0.392749747640215, 0.046855115753104, 0.679626193911908, 0, 1, 
0, 0.035250133523233, 1, 0, 1, 0.496846011131725, 0.58886827458256, 
0, 0.164854929968904, 0, 0, 0.0287816944959802, 0.182178217821782, 
1, 0.71862699648374, 0.395933908300423, 0.122218668031476, 0.862468344342713, 
0.10660735408969, 0), mean_value = c(0.0849713237573971, 0.258623607625513, 
0.087893476006846, 0.116846546852014, 0.0762156088812836, 0.108219837388825, 
0.0428427606529557, 0.1136661705191, 0.130059023540346, 0.087893476006846, 
0.174296025264175, 0.0261154186427083, 0.174296025264175, 0.108219837388825, 
0.325793222173917, 0.0778882477085785, 0.0428427606529557, 0.116846546852014, 
0.15248118933341, 0.15248118933341, 0.087893476006846, 0.287538362248933, 
0.21894815838024, 0.325793222173917, 0.21894815838024, 0.0849713237573971, 
0.0778882477085785, 0.116846546852014, 0.116846546852014, 0.287538362248933, 
0.258623607625513, 0.245334523414615, 0.0762156088812836, 0.325793222173917, 
0.258623607625513, 0.1136661705191, 0.287538362248933, 0.0778882477085785, 
0.087893476006846, 0.1136661705191, 0.258623607625513, 0.258623607625513, 
0.287538362248933, 0.245334523414615, 0.325793222173917, 0.258623607625513, 
0.0428427606529557, 0.287538362248933, 0.287538362248933, 0.0428427606529557, 
0.287538362248933, 0.0261154186427083, 0.087893476006846, 0.0762156088812836, 
0.287538362248933, 0.00663847356772084, 0.0261154186427083, 0.258623607625513, 
0.287538362248933, 0.0762156088812836, 0.00663847356772084, 0.0762156088812836, 
0.0762156088812836, 0.258623607625513, 0.0428427606529557, 0.258623607625513, 
0.0428427606529557, 0.130059023540346, 0.116846546852014, 0.108219837388825, 
0.087893476006846, 0.130059023540346, 0.0762156088812836, 0.245334523414615, 
0.174296025264175, 0.0849713237573971, 0.245334523414615, 0.087893476006846, 
0.258623607625513, 0.1136661705191, 0.15248118933341, 0.245334523414615, 
0.130059023540346, 0.0261154186427083, 0.130059023540346, 0.0778882477085785, 
0.0778882477085785, 0.130059023540346, 0.325793222173917, 0.1136661705191, 
0.0261154186427083, 0.0762156088812836, 0.287538362248933, 0.0428427606529557, 
0.108219837388825, 0.0849713237573971, 0.0778882477085785, 0.0849713237573971, 
0.325793222173917, 0.174296025264175)), row.names = c(NA, -100L
), class = "data.frame")

Answer 1

您可以首先使用dplyr计算每个条件的平均值：

library(dplyr)
D_mean <- d %>% group_by(variable) %>% summarise(Mean = mean(value, na.rm = TRUE))

# A tibble: 6 x 2
  variable            Mean
  <chr>              <dbl>
1 Holiday          -0.212 
2 Humidity          0.0184
3 Protocol Active   0.0297
4 Scattered Clouds -0.0961
5 Temp             -0.0573
6 Wind              0.0756

然后，您可以使用geom_errorbar在每个变量的均值点处画一条线（基本上，它会画出误差线，但是当您使ymin和ymax确实接近均值时，您会产生错觉行）：

library(dplyr)
library(ggplot2)
d %>% 
  ggplot() +
  coord_flip() +
  ggforce::geom_sina(aes(
    x = variable,
    y = value,
    color = stdfvalue),
    method = "counts", maxwidth = 0.7, alpha = 0.7)+
  geom_errorbar(inherit.aes = FALSE, data = D_mean, 
                aes(x = variable, y = Mean, 
                    ymin = Mean-0.0001, ymax  = Mean+0.0001), 
                size = 1)