Using attributes of `ftable` for extracting data

Question

I sometimes use the ftable function purely for its presentation of hierarchical categories. However, sometimes, when the table is large, I would like to further subset the table before using it.

Let's say we're starting with:

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

Because there are no dimnames, I can't extract data in the same way that I would with an object that has dimnames. For instance, there's no way for me to directly extract all the "Child" values from the "1st" and "3rd" classes.

My current approach is to convert it to a table, do the extraction, and then convert it back to an 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

However, I don't like this approach because the overall layout sometimes changes if you're not careful. Compare it with the following, which removes the requirement of subsetting only children and adds the requirement of subsetting only those who did not survive:

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

I don't like that the overall layout of rows and columns has changed. That's a classic case of having to remember to use drop = FALSE to maintain dimensions when a single column is extracted:

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

I know there are many ways to get the data that I want, starting with subsetting from the raw data and then making my ftable, but for this question, let's assume that's not possible.

The end goal is to have an approach that lets me extract from an ftable preserving the display format of the nested "row" hierarchy.

Are there other solutions to this? Can we make use of the row.vars and col.vars attributes to extract data from an ftable and retain its formatting?

---

My current approach also doesn't work for hierarchical columns, so I'm hoping that the proposed solution can also handle those cases.

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

Note the nesting of "Age" and "Survived".

Try my current approach:

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

I can get back to what I want with:

ftable(as.table(tab2)[c("1st", "3rd"), , , , drop = FALSE], row.vars = 1:2, col.vars = 3:4)

But I'm hoping for something more direct.

Answer 1

Once the data is aggregated to frequencies by combination of factors as is the case with the Titanic data set, it is arguably easier to subset the raw data and tabulate it for display rather than manipulating the output object.

I recognize that the OP asks for solutions using ftable, but with the back and forth in the comments section soliciting other ideas, I thought I'd post a different take on this question because it illustrates a way to simultaneously subset the data and generate the hierarchical structure of the contingency tables without custom functions.

Here is an approach using the tables package that preserves the hierarchical structure of the Titanic data, as well as eliminating cells that are empty when we subset the data frame.

First we cast the incoming table as a data frame so we can subset it during the tabular() function.

 library(titanic)
 df <- as.data.frame(Titanic)

Then we use tables::tabular() while subsetting the data in the data= argument with the extract operator [, and use DropEmpty() to avoid printing rows and columns where Freq == 0. We also use Heading() to suppress unwanted headings for Freq and sum.

tabular((Class * Sex) ~ (Age)*Survived*Heading()*Freq*Heading()*sum*DropEmpty(empty=0),
        data=df[df$Class %in% c("1st","3rd") & df$Age=="Child",])

...and the output:

> tabular((Class * Sex) ~ (Age)*Survived*Heading()*Freq*Heading()*sum*DropEmpty(empty=0),
+         data=df[df$Class %in% c("1st","3rd") & df$Age=="Child",])

              Age         
              Child       
              Survived    
 Class Sex    No       Yes
 1st   Male    0        5 
       Female  0        1 
 3rd   Male   35       13 
       Female 17       14

If we remove DropEmpty(), we replicate the entire tabular structure based on the factor variables in the table.

> # remove DropEmpty() to replicate entire factor structure
> tabular((Class * Sex) ~ (Age)*Survived*Heading()*Freq*Heading()*sum,
+         data=df[df$Class %in% c("1st","3rd") & df$Age=="Child",])

              Age                      
              Child        Adult       
              Survived     Survived    
 Class Sex    No       Yes No       Yes
 1st   Male    0        5  0        0  
       Female  0        1  0        0  
 2nd   Male    0        0  0        0  
       Female  0        0  0        0  
 3rd   Male   35       13  0        0  
       Female 17       14  0        0  
 Crew  Male    0        0  0        0  
       Female  0        0  0        0  
> 

Replicating the second and third examples from the OP is also straightforward.

> # second example from question
> tabular((Class * Sex * Age) ~ Survived*Heading()*Freq*Heading()*sum*DropEmpty(empty=0),
+         data=df[df$Class %in% c("1st","3rd") & df$Survived=="No",])

                    Survived
 Class Sex    Age   No      
 1st   Male   Child   0     
              Adult 118     
       Female Child   0     
              Adult   4     
 3rd   Male   Child  35     
              Adult 387     
       Female Child  17     
              Adult  89     
> # third example from question 
> tabular((Class * Sex) ~ (Age)*Survived*Heading()*Freq*Heading()*sum*DropEmpty(empty=0),
+         data=df[df$Class %in% c("1st","3rd"),])

              Age                      
              Child        Adult       
              Survived     Survived    
 Class Sex    No       Yes No       Yes
 1st   Male    0        5  118       57
       Female  0        1    4      140
 3rd   Male   35       13  387       75
       Female 17       14   89       76
> 

Answer 2

Here's what I was able to sort of hack together, with some help from Axeman:

replace_empty_arguments <- function(a) {
  empty_symbols <- vapply(a, function(x) {
    is.symbol(x) && identical("", as.character(x)), 0)
  } 
  a[!!empty_symbols] <- 0
  lapply(a, eval)
}

`[.ftable` <- function (inftable, ...) {
  if (!class(inftable) %in% "ftable") stop("input is not an ftable")
  tblatr <- attributes(inftable)[c("row.vars", "col.vars")]
  valslist <- replace_empty_arguments(as.list(match.call()[-(1:2)]))
  x <- sapply(valslist, function(x) identical(x, 0))
  TAB <- as.table(inftable)
  valslist[x] <- dimnames(TAB)[x]
  temp <- as.matrix(expand.grid(valslist))
  out <- ftable(
    `dimnames<-`(`dim<-`(TAB[temp], lengths(valslist)), valslist),
    row.vars = seq_along(tblatr[["row.vars"]]),
    col.vars = seq_along(tblatr[["col.vars"]]) + length(tblatr[["row.vars"]]))
  names(attributes(out)[["row.vars"]]) <- names(tblatr[["row.vars"]])
  names(attributes(out)[["col.vars"]]) <- names(tblatr[["col.vars"]])
  out
}

Try it out with the examples from the question:

mytable[c("1st", "3rd"), , "Child", ]
##                    Survived No Yes
## Class Sex    Age                  
## 1st   Male   Child           0   5
##       Female Child           0   1
## 3rd   Male   Child          35  13
##       Female Child          17  14

mytable[c("1st", "3rd"), , , "No"]
##                    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

tab2[c("1st", "3rd"), , , ]
##              Age      Child     Adult    
##              Survived    No Yes    No Yes
## Class Sex                                
## 1st   Male                0   5   118  57
##       Female              0   1     4 140
## 3rd   Male               35  13   387  75
##       Female             17  14    89  76