r 箱线图中的平均值和中位数

Question

我有两个站点和每个站点的浮游生物季节性样本。 我已经为每个季节和站点执行了多样性指数，并且我使用 ggplot2 和 geom_boxplot（我向您展示了情节）代表了同一个 plot 上的所有内容。

这些是我用于 plot 的命令：

level_order <- c("Win", "Spr","Sum","Aut") #serve per cambiare l'ordine dei gruppi sull'asse delle x
ggplot(div, aes(x = factor(season, level = level_order), y = shannon)) + geom_boxplot(aes(fill = site)) +  xlab("season") + ylab("Shannon index")

我现在想做的（但我没有做）是绘制箱线图，其中线是每个组的平均值（例如，第一个站点的冬季多样性和第二个站点的冬季多样性），一个点是中位数。

有什么建议么？ 先感谢您！！

我在这里留下我的 div dataframe 的示例：

    site    season  shannon
1   SG01    Win 1.55124832
2   SG01    Win 1.72057146
3   SG01    Spr 1.625478482
4   SG01    Spr 1.277293322
5   SG01    Sum 0.88550747
6   SG05    Sum 1.677666039
7   SG01    Sum 1.850984118
8   SG05    Sum 2.36108339
9   SG01    Aut 1.195804612
10  SG01    Aut 1.439432047
11  SG05    Aut 2.546555781
12  SG01    Win 0.284953317
13  SG05    Win 0.779162884
14  SG01    Spr 1.723890419
15  SG05    Spr 1.373792719
16  SG01    Sum 2.092365382
17  SG05    Sum 1.931014136
18  SG01    Sum 1.50502545
19  SG05    Sum 1.532379533
20  SG01    Aut 1.570949853
21  SG05    Aut 1.713710631
22  SG01    Aut 2.230091608
23  SG05    Aut 2.60573397
24  SG01    Win 0.876748429
25  SG05    Win 2.02200333
26  SG01    Win 2.352305681
27  SG01    Spr 1.891093419
28  SG05    Spr 1.394992271
29  SG01    Sum 1.946875957
30  SG05    Sum 1.599478879
31  SG01    Sum 2.124065518
32  SG05    Sum 1.515955871
33  SG01    Aut 1.158688215
34  SG05    Aut 1.748027849
35  SG01    Win 0.105111547
36  SG01    Spr 0.87617449
37  SG05    Spr 2.162793046
38  SG01    Spr 2.188259123
39  SG05    Spr 1.477570463
40  SG01    Spr 2.403560297
41  SG05    Spr 1.377893122
42  SG01    Sum 2.134173167
43  SG05    Sum 1.858323438
44  SG01    Sum 1.372338798
45  SG05    Sum 1.850782293
46  SG01    Sum 2.042722743
47  SG05    Sum 1.765405181
48  SG01    Sum 2.069671278
49  SG05    Sum 2.61192074
50  SG01    Aut 2.070530751
51  SG05    Aut 1.906772829
52  SG01    Aut 1.631107479
53  SG05    Aut 2.426254572
54  SG01    Win 1.987217164
55  SG05    Win 0.799496294
56  SG01    Spr 1.015641148
57  SG05    Spr 1.406142227
58  SG01    Spr 1.475127955
59  SG05    Spr 1.64170242
60  SG01    Sum 2.18855532
61  SG05    Sum 2.055605308
62  SG01    Sum 1.843388552
63  SG05    Sum 2.143056015
64  SG01    Aut 1.390632003
65  SG05    Aut 1.177005155
66  SG01    Win 0.436994857
67  SG05    Win 0.922177895
68  SG01    Win 0.111486445
69  SG05    Win 1.013003209
70  SG01    Spr 2.038485906
71  SG05    Spr 1.699342757
72  SG01    Spr 2.197461132
73  SG05    Spr 1.818752081
74  SG01    Spr 1.593323983
75  SG05    Spr 1.74058146
76  SG01    Sum 1.828585725
77  SG05    Sum 2.134304048
78  SG01    Sum 0.682908105
79  SG05    Sum 1.779730889
80  SG01    Sum 1.736418975
81  SG05    Sum 2.122669488
82  SG05    Aut 0.739529655
83  SG01    Aut 1.477379963
84  SG05    Aut 1.910292757
85  SG01    Aut 1.297295831
86  SG05    Aut 1.340215584
87  SG01    Win 0.607693424
88  SG05    Win 1.288681476
89  SG01    Win 1.123201233
90  SG05    Win 2.133970441
91  SG01    Win 2.087194385
92  SG05    Win 2.267827588
93  SG01    Spr 2.178855657
94  SG05    Spr 2.475019718
95  SG01    Spr 1.211745507
96  SG05    Spr 1.466358065
97  SG01    Spr 1.760959558
98  SG05    Spr 1.701252873
99  SG01    Sum 0.332361517
100 SG05    Sum 0.588153241
101 SG01    Sum 0.867165813
102 SG05    Sum 1.105468261
103 SG01    Sum 1.609437912
104 SG05    Sum 0.831497572
105 SG01    Aut 2.019695282
106 SG05    Aut 1.78876299
107 SG01    Aut 2.111590479
108 SG05    Aut 2.371876837
109 SG01    Aut 2.055512217
110 SG05    Aut 2.055472931
111 SG01    Aut 1.88461724
112 SG05    Aut 1.857836914
113 SG01    Win 0.849886275
114 SG05    Win 0.79030057
115 SG01    Sum 1.861445785
116 SG05    Sum 1.481311163
117 SG01    Sum 2.388759303
118 SG05    Sum 1.912778218
119 SG01    Aut 1.780059004
120 SG01    Aut 1.46783794
121 SG01    Win 0.162111238
122 SG01    Win 0.115561428
123 SG01    Win 0.063567551
124 SG01    Win 0.294800212
125 SG05    Win 0.831952782
126 SG01    Win 0.21439167
127 SG01    Win 1.411562768
128 SG01    Win 1.896814356
129 SG01    Win 1.038566269
130 SG01    Win 0.714502942
131 SG01    Spr 0.466288947
132 SG01    Spr 0.684086537
133 SG01    Spr 1.629302597
134 SG01    Sum 1.766008844
135 SG01    Sum 0.512330502
136 SG01    Sum 0.855249384
137 SG01    Sum 1.738085497
138 SG01    Sum 1.670846137
139 SG01    Sum 1.959151756
140 SG01    Sum 2.659931022
141 SG05    Sum 2.239514768
142 SG01    Aut 1.765273458
143 SG05    Aut 1.809746076
144 SG01    Aut 1.814669577
145 SG01    Aut 1.693459272
146 SG01    Aut 0.880029422
147 SG01    Aut 0.030424902
148 SG01    Aut 0.190036382
149 SG01    Win 0.028064827
150 SG01    Win 0.410753432
151 SG01    Win 1.196355197
152 SG01    Win 0.640028814
153 SG05    Win 2.172842158
154 SG01    Spr 0.310729618
155 SG01    Spr 0.431023204
156 SG01    Spr 1.957663797
157 SG05    Spr 1.819830757
158 SG01    Spr 0.399347092
159 SG01    Spr 1.298327832
160 SG05    Spr 2.011736101
161 SG01    Spr 0.76557657
162 SG01    Spr 2.127680798
163 SG01    Sum 1.990586223
164 SG01    Sum 1.176712496
165 SG01    Sum 1.163299687
166 SG01    Sum 1.342327775
167 SG05    Sum 1.45696041
168 SG01    Sum 1.425284821
169 SG01    Sum 0.603490683
170 SG05    Sum 0.8933049
171 SG01    Sum 0.832441299
172 SG01    Sum 0.203173153
173 SG01    Aut 0.432802137
174 SG01    Aut 0.689899451
175 SG01    Aut 0.633663257
176 SG01    Win 0.353839326
177 SG01    Win 0.060482006
178 SG01    Spr 0.212576264
179 SG01    Spr 1.593671964
180 SG05    Spr 1.17170529
181 SG01    Spr 2.37898595
182 SG01    Sum 1.557439793
183 SG05    Sum 1.468759607
184 SG01    Sum 0.723432071
185 SG05    Sum 1.24189285
186 SG01    Sum 1.633885941
187 SG01    Sum 1.970553561
188 SG05    Sum 2.568060749
189 SG01    Sum 1.390455469
190 SG01    Sum 1.489030655
191 SG01    Aut 1.877639964
192 SG05    Aut 2.17632569
193 SG01    Aut 1.805251144
194 SG01    Aut 2.398210416
195 SG05    Aut 1.52789825
196 SG01    Aut 1.781342289
   

Answer 1

您可以使用stat_summary创建一个包含您喜欢的统计信息的新图表。 因为我认为用一个代表“均值”的“方框”会有点混乱（因为图上的方框通常代表四分位数）并且因为我相信代表实际数据点，所以我的建议如下：

library(ggbeeswarm) # To add the data-points

ggplot(div, aes(x = factor(season, level = level_order), 
                y = shannon, color = site)) +
  stat_summary(geom = "pointrange",   # To add mean +/- se
               position = position_dodge(0.8)) + 
  stat_summary(geom = "point",        # To add the median
               fun = median, 
               position = position_dodge(0.8), 
               shape = 2, size = 5) + 
  geom_beeswarm(dodge.width = 0.8,    # To add the actual data points
                alpha = 0.5, shape = 3) +
  labs(x = "season", y = "Shannon Index") + 
  theme_bw()

结果

对不起，我偏离了这个问题。 如果您真的想要均值框，请将“pointrange”替换为“crossbar”，如果您认为数据点分散注意力，只需删除geom_beeswarm几何图形。

此外，您可以将用于中位数的形状更改为您认为更漂亮的形状（来源： Data visualization with ggplot2 ）

Answer 2

您可以预先创建汇总统计数据，然后使用stat = 'identity'将它们传递给geom_boxplot

library(tidyverse)

div %>%
  mutate(season = factor(season, level_order)) %>%
  group_by(season, site) %>%
  summarize(ymin = quantile(shannon, 0),
            lower = quantile(shannon, 0.25),
            median = median(shannon),
            mean = mean(shannon),
            upper = quantile(shannon, 0.75),
            ymax = quantile(shannon, 1)) %>%
  ggplot(aes(x = season, fill = site)) + 
  geom_boxplot(stat = 'identity', 
               aes(ymin = ymin, lower = lower, middle = mean, upper = upper,
                   ymax = ymax)) +  
  geom_point(aes(y = median, group = site), 
             position = position_dodge(width = 0.9)) +
  xlab("season") + 
  ylab("Shannon index")