Display Associations and Transactions in Readable Form

Provides the generic function inspect() and methods to display associations and transactions plus additional information formatted for online inspection.

Usage

inspect(x, ...)

# S4 method for class 'itemsets'
inspect(x, itemSep = ", ", setStart = "{", setEnd = "}", linebreak = NULL, ...)

# S4 method for class 'rules'
inspect(
  x,
  itemSep = ", ",
  setStart = "{",
  setEnd = "}",
  ruleSep = "=>",
  linebreak = NULL,
  ...
)

# S4 method for class 'transactions'
inspect(x, itemSep = ", ", setStart = "{", setEnd = "}", linebreak = NULL, ...)

# S4 method for class 'itemMatrix'
inspect(x, itemSep = ", ", setStart = "{", setEnd = "}", linebreak = NULL, ...)

# S4 method for class 'tidLists'
inspect(x, ...)

Arguments

x

a set of associations or transactions or an itemMatrix.

...

additional arguments. can be used to customize the output:

itemSep

item separator

setStart

set start symbol

setEnd

set end symbol

linebreak

print only one element per line in case the output lines get very long?

ruleSep

rule separator

Value

Nothing is returned (see the Details Section).

Details

inspect() prints the results directly. If you need to create a data.frame with a human readable version, then you can use DATAFRAME().

See also

Other associations functions: abbreviate(), associations-class, c, duplicated(), extract, 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(), extract, hierarchy, image, is.superset(), itemFrequency(), itemFrequencyPlot(), itemMatrix-class, itemwiseSetOps, match(), merge(), random.transactions(), sample(), sets, size(), supportingTransactions(), tidLists-class, transactions-class, unique()

Author

Michael Hahsler and Kurt Hornik

Examples

data("Adult")
rules <- apriori(Adult)
#> Apriori
#> 
#> Parameter specification:
#>  confidence minval smax arem  aval originalSupport maxtime support minlen
#>         0.8    0.1    1 none FALSE            TRUE       5     0.1      1
#>  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: 4884 
#> 
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[115 item(s), 48842 transaction(s)] done [0.03s].
#> sorting and recoding items ... [31 item(s)] done [0.00s].
#> creating transaction tree ... done [0.02s].
#> checking subsets of size 1 2 3 4 5 6 7 8 9 done [0.08s].
#> writing ... [6137 rule(s)] done [0.00s].
#> creating S4 object  ... done [0.01s].

## display some rules
inspect(rules[1000:1001])
#>     lhs                          rhs                              support confidence  coverage     lift count
#> [1] {education=Some-college,                                                                                 
#>      sex=Male,                                                                                               
#>      capital-loss=None}       => {native-country=United-States} 0.1208181  0.9256471 0.1305229 1.031449  5901
#> [2] {education=Some-college,                                                                                 
#>      sex=Male,                                                                                               
#>      capital-gain=None}       => {capital-loss=None}            0.1199992  0.9474620 0.1266533 0.993899  5861
inspect(rules[1000:1001],
  ruleSep = "~~>", itemSep = " + ", setStart = "", setEnd = "",
  linebreak = FALSE
)
#>     lhs                                                      
#> [1] education=Some-college + sex=Male + capital-loss=None ~~>
#> [2] education=Some-college + sex=Male + capital-gain=None ~~>
#>     rhs                          support   confidence coverage  lift     count
#> [1] native-country=United-States 0.1208181 0.9256471  0.1305229 1.031449 5901 
#> [2] capital-loss=None            0.1199992 0.9474620  0.1266533 0.993899 5861 

## to get rules in readable format, use coercion or DATAFRAME with additional parameters.
as(rules[1000:1001], "data.frame")
#>                                                                                      rules
#> 1000 {education=Some-college,sex=Male,capital-loss=None} => {native-country=United-States}
#> 1001            {education=Some-college,sex=Male,capital-gain=None} => {capital-loss=None}
#>        support confidence  coverage     lift count
#> 1000 0.1208181  0.9256471 0.1305229 1.031449  5901
#> 1001 0.1199992  0.9474620 0.1266533 0.993899  5861
DATAFRAME(rules[1000:1001])
#>                                                      LHS
#> 1000 {education=Some-college,sex=Male,capital-loss=None}
#> 1001 {education=Some-college,sex=Male,capital-gain=None}
#>                                 RHS   support confidence  coverage     lift
#> 1000 {native-country=United-States} 0.1208181  0.9256471 0.1305229 1.031449
#> 1001            {capital-loss=None} 0.1199992  0.9474620 0.1266533 0.993899
#>      count
#> 1000  5901
#> 1001  5861
DATAFRAME(rules[1000:1001], separate = TRUE, setStart = "", setEnd = "")
#>                                                    LHS
#> 1000 education=Some-college,sex=Male,capital-loss=None
#> 1001 education=Some-college,sex=Male,capital-gain=None
#>                               RHS   support confidence  coverage     lift count
#> 1000 native-country=United-States 0.1208181  0.9256471 0.1305229 1.031449  5901
#> 1001            capital-loss=None 0.1199992  0.9474620 0.1266533 0.993899  5861