英文:
Bar and point plot with repeat data
问题
这是您要翻译的内容：
我有以下形式的数据：data =|组 |条件 |数值 |来源 ||------|---------|-----------|------------|| A |健康 |1.0000000 |模拟 || B |健康 |1.0000000 |模拟 || C |健康 |1.0000000 |模拟 || A |疾病 |0.4589925 |模拟 || B |疾病 |1.4905422 |模拟 || C |疾病 |1.3422035 |模拟 || A |健康 |1.0000000 |实验 || B |健康 |1.0000000 |实验 || C |健康 |1.0000000 |实验 || A |疾病 |0.5158371 |实验 || A |疾病 |0.7559055 |实验 || B |疾病 |1.4153005 |实验 || B |疾病 |1.4000000 |实验 || C |疾病 |1.3300971 |实验 || C |疾病 |1.0000000 |实验 |我想制作一个堆叠图。沿X轴是组（3个），沿Y轴是值。每个组应该有两个条（一个用于健康，一个用于疾病）。此外，我尝试在上面叠加一个点图，显示我的实验值。当每个条件只有一个实验值时，我成功地使用以下方法做到了：`pivoted <- data %>%pivot_wider(names_from = Source)``ggplot(pivoted, aes(x = group, fill = condition)) +geom_col(aes(y = Simulated), position = position_dodge()) +labs(x = "组", y = "数值") +geom_point(aes(y = Experimental), position = position_dodge(width = 0.9), size = 3)`然而，现在有重复的情况，这种方法不起作用。非常感谢您的帮助！
请注意，上述内容已经被翻译成中文，只包括代码部分的英文内容。
英文:
So I have data of the form:
data =
| group | condition | value | Source |
|---|---|---|---|
| A | Health | 1.0000000 | Simulated |
| B | Health | 1.0000000 | Simulated |
| C | Health | 1.0000000 | Simulated |
| A | Disease | 0.4589925 | Simulated |
| B | Disease | 1.4905422 | Simulated |
| C | Disease | 1.3422035 | Simulated |
| A | Health | 1.0000000 | Experimental |
| B | Health | 1.0000000 | Experimental |
| C | Health | 1.0000000 | Experimental |
| A | Disease | 0.5158371 | Experimental |
| A | Disease | 0.7559055 | Experimental |
| B | Disease | 1.4153005 | Experimental |
| B | Disease | 1.4000000 | Experimental |
| C | Disease | 1.3300971 | Experimental |
| C | Disease | 1.0000000 | Experimental |
I wish to make a stacked plot. Along the X axis is Group (3) and along the Y axis is the value. Each group should have two bars (one for health, and one for disease). On top of this, I am trying to overlay a point plot with my experimental values.
I have managed to do this when there is only one Experimental value for each condition using:
pivoted<-data %>% pivot_wider(names_from =Source).
ggplot(pivoted, aes(x=group,fill=condition)) + geom_col(aes(y=Simulated), position=position_dodge()) +
labs(x="Group", y="Value") + geom_point(aes(y=Experimental), position = position_dodge(width=0.9), size=3)
However, now there are repeats this method is not working.
Thank you in advance for any help!
答案1
得分: 0
在这种情况下，我建议对您的数据进行子集选择，而不是进行数据透视：
ggplot(data, aes(x = group, y = value, fill = condition)) +geom_col(data = filter(data, Source == "Simulated"),position = position_dodge()) +geom_point(data = filter(data, Source == "Experimental"),aes(group = condition),position = position_dodge(width = 1))
使用此数据：
data = read.table(text = 'group condition value SourceA Health 1.0000000 SimulatedB Health 1.0000000 SimulatedC Health 1.0000000 SimulatedA Disease 0.4589925 SimulatedB Disease 1.4905422 SimulatedC Disease 1.3422035 SimulatedA Health 1.0000000 ExperimentalB Health 1.0000000 ExperimentalC Health 1.0000000 ExperimentalA Disease 0.5158371 ExperimentalA Disease 0.7559055 ExperimentalB Disease 1.4153005 ExperimentalB Disease 1.4000000 ExperimentalC Disease 1.3300971 ExperimentalC Disease 1.0000000 Experimental', header = T)
英文:
In this case I would suggest subsetting your data rather than pivoting:
ggplot(data, aes(x = group, y = value, fill = condition)) +geom_col(data = filter(data, Source == "Simulated"),position = position_dodge()) +geom_point(data = filter(data, Source == "Experimental"),aes(group = condition),position = position_dodge(width = 1))
Using this data
data = read.table(text = 'group condition value SourceA Health 1.0000000 SimulatedB Health 1.0000000 SimulatedC Health 1.0000000 SimulatedA Disease 0.4589925 SimulatedB Disease 1.4905422 SimulatedC Disease 1.3422035 SimulatedA Health 1.0000000 ExperimentalB Health 1.0000000 ExperimentalC Health 1.0000000 ExperimentalA Disease 0.5158371 ExperimentalA Disease 0.7559055 ExperimentalB Disease 1.4153005 ExperimentalB Disease 1.4000000 ExperimentalC Disease 1.3300971 ExperimentalC Disease 1.0000000 Experimental', header = T)
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论