Methods for "[", i.e., extraction or subsetting for arules objects.
Usage
# S4 method for class 'itemMatrix,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]
# S4 method for class 'transactions,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]
# S4 method for class 'tidLists,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]
# S4 method for class 'rules,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]
# S4 method for class 'itemsets,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]Arguments
- x
an object of class itemMatrix, transactions or associations.
- i
select rows/sets using an integer vector containing row numbers or a logical vector.
- j
select columns/items using an integer vector containing column numbers (i.e., item IDs), a logical vector or a vector of strings containing parts of item labels.
- ...
further arguments are ignored.
- drop
ignored.
See also
Other associations functions:
abbreviate(),
associations-class,
c,
duplicated(),
inspect(),
is.closed(),
is.generator(),
is.maximal(),
is.redundant(),
is.significant(),
is.superset(),
itemsets-class,
match(),
rules-class,
sample(),
sets,
size(),
sort(),
unique()
Other itemMatrix and transactions functions:
abbreviate(),
c,
crossTable(),
duplicated(),
hierarchy,
image,
inspect(),
is.superset(),
itemFrequency(),
itemFrequencyPlot(),
itemMatrix-class,
itemwiseSetOps,
match(),
merge(),
random.transactions(),
sample(),
sets,
size(),
supportingTransactions(),
tidLists-class,
transactions-class,
unique()
Examples
data(Adult)
Adult
#> transactions in sparse format with
#> 48842 transactions (rows) and
#> 115 items (columns)
## select first 10 transactions
Adult[1:10]
#> transactions in sparse format with
#> 10 transactions (rows) and
#> 115 items (columns)
## select first 10 items for first 100 transactions
Adult[1:100, 1:10]
#> transactions in sparse format with
#> 100 transactions (rows) and
#> 10 items (columns)
## select the first 100 transactions for the items containing
## "income" or "age=Young" in their labels
Adult[1:100, c("income=small", "income=large", "age=Young")]
#> transactions in sparse format with
#> 100 transactions (rows) and
#> 3 items (columns)