[英]box-plot for multiple columns with normalized x-axis values
I have the following data (in csv file) 我有以下数据(在csv文件中)
product release_after_issue release_before_issue
P1 40
P1 100
P1 10
P2 50
P2 300
P2 200
P3 10
P3 20
P3 300
I would like use the box-plot to show the distribution of days for each product release (P1, P2, etc.) based on release_after_issue
and release_before_issue
. 我想使用箱形图显示基于
release_after_issue
和release_before_issue
每个产品版本(P1,P2等)的天数分布。 The x-axis is the products names and y-axis is days. x轴为产品名称,y轴为天。
The issues that I am facing now are:the empty values in each column, and the big number for the days. 我现在面临的问题是:每列中的空值,以及几天中的大数字。
How could I normalize the days in y-axis to be in month (easy to read)? 如何将y轴上的天标准化为月(易于阅读)? And I wold like to have each product (Ps) has its own box plot based on the column's data (
release_after_issue
or release_before_issue
) 而且我希望每个产品(Ps)都有基于列数据(
release_after_issue
或release_before_issue
)的自己的箱形图。
I tried to omit NA values and plot test example, but it did not work 我试图省略NA值并绘制测试示例,但是没有用
data <- read.csv("commons-fileupload.csv")
ggplot(data[!is.na(data$release_after_issue),],aes(x=product,y=release_after_issue))
+ geom_point()
Any help ! 任何帮助!
Not sure what fails in your code, the dummy data below works fine for me. 不知道您的代码中有什么失败,下面的虚拟数据对我来说很好。 Also, ggplot removes the NAs for you.
另外,ggplot会为您删除NA。
data <- data.frame(product=c("P1","P2","P1","P1","P2"),release_after_issue=c(100,NA,50,10,30))
ggplot(data,aes(x=product,y=release_after_issue))+ geom_boxplot()
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