[datatable-help] Error that I don't think should be an error

[Chris Neff]

On 27 October 2011 12:59, Matthew Dowle <mdowle at mdowle.plus.com> wrote:
> But user intended to delete a column that isn't there. Why should it do
> nothing, silently?  A warning at least.  If I typed "y" by mistake and
> meant another column that was there, I'd want to know about it straight
> way. Otherwise it could cause problems later on, possibly silently. That's
> the most common situation we want to catch, I think.

When developing code I've never once had this problem.  When exactly
does not deleting a column you should have deleted run into errors?

> The use-case "if
> column is there, delete it, else don't" is much rarer I would have
> thought. The rarer cases are the ones the user can code explicitly. No?


Well the times I run into this are when I want to take a data table,
calculate something using a by aggregation, and remerge the aggregated
variables into the original data table.
If the original data.table may already have the aggregated variable
name as a column, I want to delete it before merging in order to not
conflict with names. So I do something like DT[, w:=NULL] just to
double check.

This is a slightly roundabout way of saying that once := with by gets
implemented, I would probably never run into this. But I've never had
a mistype so I don't know the rarity of the two cases.  I usually
thought though without serious compelling reasons that data.table
should behave like data.frame where at all possible, and I don't know
if it is a compelling enough reason to deviate.

>
>> Pretty simple. I think assigning NULL to a column that doesn't exist
>> should just work as a no-op instead of failing with an error. No-op is
>> consistent with data.frame:
>>
>>> dt=data.table(x=1:10)
>>> dt$y=NULL
>> Error in `[<-.data.table`(x, j = name, value = value) :
>>   RHS is NULL, meaning delete column(s). But, at least one column is
>> not present.
>>> dt[,y:=NULL]
>> Error in `[.data.table`(dt, , `:=`(y, NULL)) :
>>   RHS is NULL, meaning delete column(s). But, at least one column is
>> not present.
>>> df=data.frame(x=1:10)
>>> df$y=NULL
>>
>> If agreed I will file a bug report.
>>
>> -Chris
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