我有时会使用ftable
其功能纯粹是为了表示层次类别。然而,有时,当表很大时,我想在使用它之前进一步对表进行子集化。
假设我们从以下开始:
mytable <- ftable(Titanic, row.vars = 1:3)
mytable
## Survived No Yes
## Class Sex Age
## 1st Male Child 0 5
## Adult 118 57
## Female Child 0 1
## Adult 4 140
## 2nd Male Child 0 11
## Adult 154 14
## Female Child 0 13
## Adult 13 80
## 3rd Male Child 35 13
## Adult 387 75
## Female Child 17 14
## Adult 89 76
## Crew Male Child 0 0
## Adult 670 192
## Female Child 0 0
## Adult 3 20
str(mytable)
## ftable [1:16, 1:2] 0 118 0 4 0 154 0 13 35 387 ...
## - attr(*, "row.vars")=List of 3
## ..$ Class: chr [1:4] "1st" "2nd" "3rd" "Crew"
## ..$ Sex : chr [1:2] "Male" "Female"
## ..$ Age : chr [1:2] "Child" "Adult"
## - attr(*, "col.vars")=List of 1
## ..$ Survived: chr [1:2] "No" "Yes"
## NULL
因为没有dimnames
,我无法以与具有对象的方式相同的方式提取数据dimnames
。例如,我无法直接从“1st”和“3rd”类中提取所有“Child”值。
我目前的方法是将其转换为table
,进行提取,然后将其转换回ftable
.
Example:
mytable[c("1st", "3rd"), , "Child", ]
## Error: incorrect number of dimensions
## Only the underlying data are seen as having dims
dim(mytable)
## [1] 16 2
## I'm OK with the "Age" column being dropped in this case....
ftable(as.table(mytable)[c("1st", "3rd"), , "Child", ])
## Survived No Yes
## Class Sex
## 1st Male 0 5
## Female 0 1
## 3rd Male 35 13
## Female 17 14
但是,我不喜欢这种方法,因为如果您不小心,整体布局有时会发生变化。将其与以下内容进行比较,后者删除了仅对子项进行子集化的要求,并添加了仅对未生存的子集进行子集化的要求:
ftable(as.table(mytable)[c("1st", "3rd"), , , "No"])
## Age Child Adult
## Class Sex
## 1st Male 0 118
## Female 0 4
## 3rd Male 35 387
## Female 17 89
我不喜欢行和列的整体布局发生了变化。这是必须记住使用的经典案例drop = FALSE
在提取单个列时保持维度:
ftable(as.table(mytable)[c("1st", "3rd"), , , "No", drop = FALSE])
## Survived No
## Class Sex Age
## 1st Male Child 0
## Adult 118
## Female Child 0
## Adult 4
## 3rd Male Child 35
## Adult 387
## Female Child 17
## Adult 89
我知道有many获取我想要的数据的方法,从原始数据的子集开始,然后使我的ftable
,但对于这个问题,我们假设这是不可能的。
最终目标是有一种方法可以让我从ftable
保留嵌套“行”层次结构的显示格式。
还有其他解决方案吗?我们可以利用row.vars
and col.vars
从属性中提取数据ftable
并保留其格式?
我当前的方法也不适用于分层列,因此我希望提议的解决方案也可以处理这些情况。
Example:
tab2 <- ftable(Titanic, row.vars = 1:2, col.vars = 3:4)
tab2
## Age Child Adult
## Survived No Yes No Yes
## Class Sex
## 1st Male 0 5 118 57
## Female 0 1 4 140
## 2nd Male 0 11 154 14
## Female 0 13 13 80
## 3rd Male 35 13 387 75
## Female 17 14 89 76
## Crew Male 0 0 670 192
## Female 0 0 3 20
请注意“Age”和“Survived”的嵌套。
尝试我目前的方法:
ftable(as.table(tab2)[c("1st", "3rd"), , , , drop = FALSE])
## Survived No Yes
## Class Sex Age
## 1st Male Child 0 5
## Adult 118 57
## Female Child 0 1
## Adult 4 140
## 3rd Male Child 35 13
## Adult 387 75
## Female Child 17 14
## Adult 89 76
我可以通过以下方式回到我想要的:
ftable(as.table(tab2)[c("1st", "3rd"), , , , drop = FALSE], row.vars = 1:2, col.vars = 3:4)
但我希望有更直接的东西。