Question

我正在尝试使用R中的ggplot2在每个条形的末尾构建一个带有误差条和值标签的极坐标条形图。我遇到的问题是误差条和值标签都堆叠在一起而不是在单独的酒吧。有谁知道如何解决这一问题？

以下是我使用的代码和数据：

structure(list(Feature_Set = c("All Features", "Depression Only", 
"Depression + schiz", "Depression + schiz + AD", "Depression + schiz + AD + Cog", 
"Depression + schiz + AD + Cog + BMI", "Cog_BMI_WHR", "cog_and_AD", 
"AD", "Depressive Symptoms", "All Features", "Depression Only", 
"Depression + schiz", "Depression + schiz + AD", "Depression + schiz + AD + Cog", 
"Depression + schiz + AD + Cog + BMI", "Cog_BMI_WHR", "cog_and_AD", 
"AD", "Depressive Symptoms", "All Features", "Depression Only", 
"Depression + schiz", "Depression + schiz + AD", "Depression + schiz + AD + Cog", 
"Depression + schiz + AD + Cog + BMI", "Cog_BMI_WHR", "cog_and_AD", 
"AD", "Depressive Symptoms", "All Features", "Depression Only", 
"Depression + schiz", "Depression + schiz + AD", "Depression + schiz + AD + Cog", 
"Depression + schiz + AD + Cog + BMI", "Cog_BMI_WHR", "cog_and_AD", 
"AD", "Depressive Symptoms"), Trajectory = structure(c(1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Resilient", "chronic", "emergent", 
"depressed improved"), class = "factor"), value = c(65.51, 61.42, 
62, 64.26, 64.99, 65.72, 60.26, 61.6, 59.98, 59.92, 85.13, 69.06, 
72.2, 77.18, 80.61, 83.6, 71.85, 69.72, 66.71, 65.74, 79.5, 66.79, 
70.22, 72.52, 74.87, 77.28, 69.72, 68.17, 63.15, 65.64, 77.39, 
67.97, 69.18, 70.51, 73.08, 75.33, 67.19, 67.82, 68, 65.12), 
    SD = c(3.23, 2.75, 4.01, 3.42, 3.88, 3.23, 3.31, 4.15, 3.34, 
    3.98, 1.57, 2.72, 3.51, 2.53, 2.36, 2.86, 2.51, 3.58, 2.88, 
    1.8, 2.09, 2.44, 2.75, 2.86, 1.98, 1.96, 2.15, 1.88, 2.82, 
    3.87, 1.78, 2.99, 2.71, 3.28, 2.96, 1.53, 2.92, 3.1, 2.76, 
    2.47)), row.names = c(NA, -40L), class = "data.frame", .Names = c("Feature_Set", 
"Trajectory", "value", "SD"))

数据框（以下数据用于演示目的）。

                           Feature_Set         class .    value   SD
1                         All Features          Resilient 65.51 3.23
2                      Depression Only          Resilient 61.42 2.75
3                   Depression + schiz          Resilient 62.00 4.01
4              Depression + schiz + AD          Resilient 64.26 3.42
5        Depression + schiz + AD + Cog          Resilient 64.99 3.88
6  Depression + schiz + AD + Cog + BMI          Resilient 65.72 3.23
7                          Cog_BMI_WHR          Resilient 60.26 3.31
8                           cog_and_AD          Resilient 61.60 4.15
9                                   AD          Resilient 59.98 3.34
10                 Depressive Symptoms          Resilient 59.92 3.98
11                        All Features            chronic 85.13 1.57
12                     Depression Only            chronic 69.06 2.72
13                  Depression + schiz            chronic 72.20 3.51
14             Depression + schiz + AD            chronic 77.18 2.53
15       Depression + schiz + AD + Cog            chronic 80.61 2.36
16 Depression + schiz + AD + Cog + BMI            chronic 83.60 2.86
17                         Cog_BMI_WHR            chronic 71.85 2.51
18                          cog_and_AD            chronic 69.72 3.58
19                                  AD            chronic 66.71 2.88
20                 Depressive Symptoms            chronic 65.74 1.80
21                        All Features           emergent 79.50 2.09
22                     Depression Only           emergent 66.79 2.44
23                  Depression + schiz           emergent 70.22 2.75
24             Depression + schiz + AD           emergent 72.52 2.86
25       Depression + schiz + AD + Cog           emergent 74.87 1.98
26 Depression + schiz + AD + Cog + BMI           emergent 77.28 1.96
27                         Cog_BMI_WHR           emergent 69.72 2.15
28                          cog_and_AD           emergent 68.17 1.88
29                                  AD           emergent 63.15 2.82
30                 Depressive Symptoms           emergent 65.64 3.87
31                        All Features depressed improved 77.39 1.78
32                     Depression Only depressed improved 67.97 2.99
33                  Depression + schiz depressed improved 69.18 2.71
34             Depression + schiz + AD depressed improved 70.51 3.28
35       Depression + schiz + AD + Cog depressed improved 73.08 2.96
36 Depression + schiz + AD + Cog + BMI depressed improved 75.33 1.53
37                         Cog_BMI_WHR depressed improved 67.19 2.92
38                          cog_and_AD depressed improved 67.82 3.10
39                                  AD depressed improved 68.00 2.76
40                 Depressive Symptoms depressed improved 65.12 2.47

代码：

ggplot(data,aes(x=Feature_Set,y=value,fill=Trajectory))+
  geom_bar(stat="identity",position="dodge")+
  coord_polar() +
  scale_y_continuous(breaks = 0:nlevels(data$Trajectory)) +
  geom_text(aes(y = value +20,label = value))+
  geom_errorbar(aes(ymin=value-SD, ymax=value+SD), width=.2, position="identity") +
  xlab("Feature Set")+ylab("Predictive Accuracy")

结果：

根据接受的答案，我已将代码更新为其他具有类似问题的示例：

ggplot(data,aes(x=Feature_Set,y=value,fill=Trajectory))+
  geom_bar(stat="identity",position="dodge")+
  coord_polar() +
  scale_y_continuous(breaks = 0:nlevels(data$Trajectory)) +
  geom_text(position = position_dodge(.9), aes(y = value +10,label = value))+
  geom_errorbar(aes(ymin=value-SD, ymax=value+SD), position=position_dodge(.9)) +
  #geom_point(position=position_dodge(.9), aes(y=value, colour=Trajectory)) +
  xlab("Feature Set")+ylab("Predictive Accuracy")

Answer 1

以下是部分解决方案：

我在width

geom_errorbar()

我更喜欢使用position = position_dodge()
在width中针对重叠文字position_dodge()尝试不同的geom_text值。

ggplot(data, aes(x = Feature_Set, y = value, fill = Trajectory)) +
  geom_bar(stat = "identity", position = position_dodge()) +
  coord_polar() +
  scale_y_continuous(breaks = 0:nlevels(data$Trajectory)) +
  geom_text(aes(y = value + 20, label = value), position = position_dodge(width = 0.8)) +
  geom_errorbar(aes(ymin = value - SD, ymax = value + SD), position = position_dodge()) +
  xlab("Feature Set") + ylab("Predictive Accuracy")

误差条和标签在极坐标条形图中错误定位ggplot2 R

1 个答案: