How can I force lapply to bind the output as apply does for variables with identical values?

apply(mtcars[,c('vs','am')],2,table)

produces

  vs am
0 18 19
1 14 13

but

lapply(mtcars[,c('vs','am')],table)

produces

Can I force lapply to do produce one table as apply does?

In the end I would like to compute means using different dependent variables with identical values. I use lapply but do not want to have to perform a cbind in the end:

func.break <- function(indy){
  t(as.data.frame(mtcars 
                      %>% group_by(get(indy)) 
                      %>% summarise_at(depy, funs(mean))
                      )
   )
}

indy <- c('vs','am') 
depy <- c('mpg','qsec')

res.list <- lapply(indy,func.break)
res.list

[[1]]
              [,1]     [,2]
get(indy)  0.00000  1.00000
mpg       16.61667 24.55714
qsec      16.69389 19.33357

[[2]]
              [,1]     [,2]
get(indy)  0.00000  1.00000
mpg       17.14737 24.39231
qsec      18.18316 17.36000

cbind(as.data.frame(res.list[1]),as.data.frame(res.list[1]))

                X1       X2       X1       X2
get(indy)  0.00000  1.00000  0.00000  1.00000
mpg       16.61667 24.55714 16.61667 24.55714
qsec      16.69389 19.33357 16.69389 19.33357

I guess there is a more elegant way? How would apply work for this?

Solution

Try list2DF

list2DF(lapply(mtcars[, c("vs", "am")], table))

which gives

  vs am
1 18 19
2 14 13

Update

Or you can try as.data.frame

> as.data.frame(lapply(indy, func.break))
                X1       X2     X1.1     X2.1
get(indy)  0.00000  1.00000  0.00000  1.00000
mpg       16.61667 24.55714 17.14737 24.39231
qsec      16.69389 19.33357 18.18316 17.36000