Get summary statistics about rules
Value
A list with n_rules and summary statistics (min,
max, mean, median) for support,
confidence, and lift.
Examples
# \donttest{
if (requireNamespace("arules", quietly = TRUE)) {
data("Groceries", package = "arules")
res <- tidy_apriori(Groceries, support = 0.001, confidence = 0.5)
summarize_rules(res)
}
#> 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].
#> $n_rules
#> [1] 5668
#>
#> $support
#> $support$min
#> [1] 0.001016777
#>
#> $support$max
#> [1] 0.02226741
#>
#> $support$mean
#> [1] 0.001667797
#>
#> $support$median
#> [1] 0.00132181
#>
#>
#> $confidence
#> $confidence$min
#> [1] 0.5
#>
#> $confidence$max
#> [1] 1
#>
#> $confidence$mean
#> [1] 0.6249694
#>
#> $confidence$median
#> [1] 0.6
#>
#>
#> $lift
#> $lift$min
#> [1] 1.956825
#>
#> $lift$max
#> [1] 18.99565
#>
#> $lift$mean
#> [1] 3.262302
#>
#> $lift$median
#> [1] 2.898999
#>
#>
# }