Subset rules containing specific items
Examples
# \donttest{
if (requireNamespace("arules", quietly = TRUE)) {
data("Groceries", package = "arules")
res <- tidy_apriori(Groceries, support = 0.001, confidence = 0.5)
filter_rules_by_item(res, "whole milk", where = "rhs")
}
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.5 0.1 1 none FALSE TRUE 5 0.001 2
#> maxlen target ext
#> 10 rules TRUE
#>
#> Algorithmic control:
#> filter tree heap memopt load sort verbose
#> 0.1 TRUE TRUE FALSE TRUE 2 TRUE
#>
#> Absolute minimum support count: 9
#>
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
#> sorting and recoding items ... [157 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 4 5 6 done [0.01s].
#> writing ... [5668 rule(s)] done [0.00s].
#> creating S4 object ... done [0.00s].
#> # A tibble: 2,679 × 8
#> rule_id lhs rhs support confidence coverage lift count
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 {honey} {who… 0.00112 0.733 0.00153 2.87 11
#> 2 3 {cocoa drinks} {who… 0.00132 0.591 0.00224 2.31 13
#> 3 4 {pudding powder} {who… 0.00132 0.565 0.00234 2.21 13
#> 4 5 {cooking chocolate} {who… 0.00132 0.52 0.00254 2.04 13
#> 5 6 {cereals} {who… 0.00366 0.643 0.00569 2.52 36
#> 6 7 {jam} {who… 0.00295 0.547 0.00539 2.14 29
#> 7 10 {rice} {who… 0.00468 0.613 0.00763 2.40 46
#> 8 11 {baking powder} {who… 0.00925 0.523 0.0177 2.05 91
#> 9 12 {liver loaf,yogurt} {who… 0.00102 0.667 0.00153 2.61 10
#> 10 14 {curd cheese,rolls/bun… {who… 0.00102 0.625 0.00163 2.45 10
#> # ℹ 2,669 more rows
# }
