问题
So I have this case currently of a data.table with one column of type list.
This list can contain different values, NULL among other possible values.
I tried to subset the data.table to keep only rows for which this column has the value NULL.
Behold... my attempts below (for the example I named the column "ColofTypeList"):
DT[is.null(ColofTypeList)]
It returns me an Empty data.table.
Then I tried:
DT[ColofTypeList == NULL]
It returns the following error (I expected an error):
Error in .prepareFastSubset(isub = isub, x = x, enclos = parent.frame(), :
RHS of == is length 0 which is not 1 or nrow (96). For robustness, no recycling is allowed (other than of length 1 RHS). Consider %in% instead.
(Just a precision my original data.table contains 96 rows, which is why the error message say such thing:
which is not 1 or nrow (96).
The number of rows is not the point).
Then I tried this:
DT[ColofTypeList == list(NULL)]
It returns the following error:
Error: comparison of these types is not implemented
I also tried to give a list of the same length than the length of the column, and got this same last error.
So my question is simple: What is the correct data.table way to subset the rows for which elements of this "ColofTypeList" are NULL ?
EDIT: here is a reproducible example
DT<-data.table(Random_stuff=c(1:9),ColofTypeList=rep(list(NULL,"hello",NULL),3))
Have fun!
回答1:
If it is a list, we can loop through the list and apply the is.null to return a logical vector
DT[unlist(lapply(ColofTypeList, is.null))]
# ColofTypeList anotherCol
#1: 3
Or another option is lengths
DT[lengths(ColofTypeList)==0]
data
DT <- data.table(ColofTypeList = list(0, 1:5, NULL, NA), anotherCol = 1:4)
回答2:
I have found another way that is also quite nice:
DT[lapply(ColofTypeList, is.null)==TRUE]
It is also important to mention that using isTRUE() doesn't work.
来源：https://stackoverflow.com/questions/59198204/subset-data-table-based-on-value-in-column-of-type-list