# subset-methods: Subset Objects In arulesSequences: Mining Frequent Sequences

## Description

`subset` extracts a subset of a collection of sequences or sequence rules which meet conditions specified with respect to their associated (or derived) quality measures, additional information, or patterns of items or itemsets.

`[` extracts subsets from a collection of (timed) sequences or sequence rules.

`unique` extracts the unique set of sequences or sequence rules from a collection of sequences or sequence rules.

`lhs, rhs` extract the left-hand (antecedent) or right-hand side (consequent) sequences from a collection of sequence rules.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## S4 method for signature 'sequences' subset(x, subset) ## S4 method for signature 'sequencerules' subset(x, subset) ## S4 method for signature 'sequences' x[i, j, ..., reduce = FALSE, drop = FALSE] ## S4 method for signature 'timedsequences' x[i, j, k, ..., reduce = FALSE, drop = FALSE] ## S4 method for signature 'sequencerules' x[i, j, ..., drop = FALSE] ## S4 method for signature 'sequences' unique(x, incomparables = FALSE) ## S4 method for signature 'sequencerules' unique(x, incomparables = FALSE) ## S4 method for signature 'sequencerules' lhs(x) ## S4 method for signature 'sequencerules' rhs(x) ```

## Arguments

 `x` an object. `subset` an expression specifying the conditions where the columns in quality and info must be referenced by their names, and the object itself as `x`. `i` a vector specifying the subset of elements to be extracted. `k` a vector specifying the subset of event times to be extracted. `reduce` a logical value specifying if the reference set of distinct itemsets should be reduced if possible. `j, ..., drop` unused arguments (for compatibility with package Matrix only). `incomparables` not used.

## Value

For `subset`, `[`, and `unique` returns an object of the same class as `x`.

For `lhs` and `rhs` returns an object of class `sequences`.

## Note

In package arules, somewhat confusingly, the object itself has to be referenced as `items`. We do not provide this, as well as any of the references `items`, `lhs`, or `rhs`.

After extraction the reference set of distinct itemsets may be larger than the set actually referred to unless reduction to this set is explicitly requested. However, this may increase memory consumption.

Event time indexes of mode character are matched against the time labels. Any duplicate indexes are ignored and their order does not matter, i.e. reordering of a sequence is not possible.

The accessors `lhs` and `rhs` impute the support of a sequence from the support and confidence of a rule. This may lead to numerically inaccuracies over back-to-back derivations.

## Author(s)

Christian Buchta

Class `sequences`, `timedsequences`, `sequencerules`, method `lhs`, `rhs`, `match`, `nitems`, `c`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```## continue example example(ruleInduction, package = "arulesSequences") ## matching a pattern as(subset(s2, size(x) > 1), "data.frame") as(subset(s2, x %ain% c("B", "F")), "data.frame") ## as well as a measure as(subset(s2, x %ain% c("B", "F") & support == 1), "data.frame") ## matching a pattern in the left-hand side as(subset(r2, lhs(x) %ain% c("B", "F")), "data.frame") ## matching a derived measure as(subset(r2, coverage(x) == 1), "data.frame") ## reduce s <- s2[11, reduce = TRUE] itemLabels(s) itemLabels(s2) ## drop initial events z <- as(zaki, "timedsequences") summary(z[1,,-1]) ```