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nuggets

Extensible R framework for subgroup discovery (Atzmueller (2015)), contrast patterns (Chen (2022)), emerging patterns (Dong (1999)) and association rules (Agrawal (1994)). Both crisp (binary) and fuzzy data are supported. It generates conditions in the form of elementary conjunctions, evaluates them on a dataset and checks the induced sub-data for interesting statistical properties. Currently, the package searches for implicative association rules and conditional correlations (Hájek (1978)). A user-defined function may be defined to evaluate on each generated condition to search for custom patterns.

Installation

To install the stable version of nuggets from CRAN, type the following command within the R session:

install.packages("nuggets")

You can also install the development version of nuggets from GitHub with:

install.packages("devtools")
devtools::install_github("beerda/nuggets")

Examples

Search for Implicative Rules

We start with loading of the needed packages:

library(tidyverse)
library(nuggets)

We are going to use the CO2 dataset as an example:

head(CO2)
#>   Plant   Type  Treatment conc uptake
#> 1   Qn1 Quebec nonchilled   95   16.0
#> 2   Qn1 Quebec nonchilled  175   30.4
#> 3   Qn1 Quebec nonchilled  250   34.8
#> 4   Qn1 Quebec nonchilled  350   37.2
#> 5   Qn1 Quebec nonchilled  500   35.3
#> 6   Qn1 Quebec nonchilled  675   39.2

First, the numeric columns need to be transformed to factors:

d <- mutate(CO2,
            conc = cut(conc, c(-Inf, 175, 350, 675, Inf)),
            uptake = cut(uptake, c(-Inf, 17.9, 28.3, 37.12)))
head(d)
#>   Plant   Type  Treatment       conc      uptake
#> 1   Qn1 Quebec nonchilled (-Inf,175] (-Inf,17.9]
#> 2   Qn1 Quebec nonchilled (-Inf,175] (28.3,37.1]
#> 3   Qn1 Quebec nonchilled  (175,350] (28.3,37.1]
#> 4   Qn1 Quebec nonchilled  (175,350]        <NA>
#> 5   Qn1 Quebec nonchilled  (350,675] (28.3,37.1]
#> 6   Qn1 Quebec nonchilled  (350,675]        <NA>

Then every column can be dichotomized, i.e., dummy logical columns may be created for each factor level:

d <- dichotomize(d)
head(d)
#> # A tibble: 6 × 23
#>   `Plant=Qn1` `Plant=Qn2` `Plant=Qn3` `Plant=Qc1` `Plant=Qc3` `Plant=Qc2`
#>   <lgl>       <lgl>       <lgl>       <lgl>       <lgl>       <lgl>      
#> 1 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 2 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 3 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 4 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 5 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 6 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> # ℹ 17 more variables: `Plant=Mn3` <lgl>, `Plant=Mn2` <lgl>, `Plant=Mn1` <lgl>,
#> #   `Plant=Mc2` <lgl>, `Plant=Mc3` <lgl>, `Plant=Mc1` <lgl>,
#> #   `Type=Quebec` <lgl>, `Type=Mississippi` <lgl>,
#> #   `Treatment=nonchilled` <lgl>, `Treatment=chilled` <lgl>,
#> #   `conc=(-Inf,175]` <lgl>, `conc=(175,350]` <lgl>, `conc=(350,675]` <lgl>,
#> #   `conc=(675, Inf]` <lgl>, `uptake=(-Inf,17.9]` <lgl>,
#> #   `uptake=(17.9,28.3]` <lgl>, `uptake=(28.3,37.1]` <lgl>

Before starting to search for the rules, it is good idea to create the vector of disjoints. Columns with equal values in the disjoint vector will not be combined together. This will speed-up the search as it makes no sense, e.g., to combine Plant=Qn1 and Plant=Qn2 in a single condition.

disj <- sub("=.*", "", colnames(d))
print(disj)
#>  [1] "Plant"     "Plant"     "Plant"     "Plant"     "Plant"     "Plant"    
#>  [7] "Plant"     "Plant"     "Plant"     "Plant"     "Plant"     "Plant"    
#> [13] "Type"      "Type"      "Treatment" "Treatment" "conc"      "conc"     
#> [19] "conc"      "conc"      "uptake"    "uptake"    "uptake"

Once the data are prepared, the dig_implications function may be invoked. It takes the dataset as its first parameter and a pair of “tidyselect” expressions to select the column names to appear in the left- and right-hand side of the rule (antecedent and consequent).

result <- dig_implications(d,
                           antecedent = !starts_with("Treatment"),
                           consequent = starts_with("Treatment"),
                           disjoint = disj,
                           min_support = 0.02,
                           min_confidence = 0.8)

result <- arrange(result, desc(support))
print(result)
#> # A tibble: 225 × 7
#>    antecedent                 consequent support confidence coverage  lift count
#>    <chr>                      <chr>        <dbl>      <dbl>    <dbl> <dbl> <dbl>
#>  1 {Type=Mississippi,uptake=… {Treatmen…  0.155       0.813   0.190   1.63    16
#>  2 {Type=Mississippi,uptake=… {Treatmen…  0.119       1       0.119   2       10
#>  3 {Plant=Mc3}                {Treatmen…  0.0833      1       0.0833  2        7
#>  4 {Plant=Mc1}                {Treatmen…  0.0833      1       0.0833  2        7
#>  5 {Plant=Qn1}                {Treatmen…  0.0833      1       0.0833  2        7
#>  6 {Plant=Mc2}                {Treatmen…  0.0833      1       0.0833  2        7
#>  7 {Plant=Mn1}                {Treatmen…  0.0833      1       0.0833  2        7
#>  8 {Plant=Mn2}                {Treatmen…  0.0833      1       0.0833  2        7
#>  9 {Plant=Mn3}                {Treatmen…  0.0833      1       0.0833  2        7
#> 10 {Plant=Qc2}                {Treatmen…  0.0833      1       0.0833  2        7
#> # ℹ 215 more rows

Custom Pattern Search

The nuggets package allows to execute a user-defined callback function on each generated frequent condition. That way a custom type of patterns may be searched. The following example replicates the search for implicative rules with the custom callback function. For that, a dataset has to be dichotomized and the disjoint vector created as in the previous example:

head(d)
#> # A tibble: 6 × 23
#>   `Plant=Qn1` `Plant=Qn2` `Plant=Qn3` `Plant=Qc1` `Plant=Qc3` `Plant=Qc2`
#>   <lgl>       <lgl>       <lgl>       <lgl>       <lgl>       <lgl>      
#> 1 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 2 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 3 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 4 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 5 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> 6 TRUE        FALSE       FALSE       FALSE       FALSE       FALSE      
#> # ℹ 17 more variables: `Plant=Mn3` <lgl>, `Plant=Mn2` <lgl>, `Plant=Mn1` <lgl>,
#> #   `Plant=Mc2` <lgl>, `Plant=Mc3` <lgl>, `Plant=Mc1` <lgl>,
#> #   `Type=Quebec` <lgl>, `Type=Mississippi` <lgl>,
#> #   `Treatment=nonchilled` <lgl>, `Treatment=chilled` <lgl>,
#> #   `conc=(-Inf,175]` <lgl>, `conc=(175,350]` <lgl>, `conc=(350,675]` <lgl>,
#> #   `conc=(675, Inf]` <lgl>, `uptake=(-Inf,17.9]` <lgl>,
#> #   `uptake=(17.9,28.3]` <lgl>, `uptake=(28.3,37.1]` <lgl>
print(disj)
#>  [1] "Plant"     "Plant"     "Plant"     "Plant"     "Plant"     "Plant"    
#>  [7] "Plant"     "Plant"     "Plant"     "Plant"     "Plant"     "Plant"    
#> [13] "Type"      "Type"      "Treatment" "Treatment" "conc"      "conc"     
#> [19] "conc"      "conc"      "uptake"    "uptake"    "uptake"

As we want to search for implicative rules with some minimum support and confidence, we define the variables to hold that thresholds. We also need to define a callback function that will be called for each found frequent condition. Its purpose is to generate the rules with the obtained condition as an antecedent:

min_support <- 0.02
min_confidence <- 0.8

f <- function(condition, support, foci_supports) {
    conf <- foci_supports / support
    sel <- !is.na(conf) & conf >= min_confidence & !is.na(foci_supports) & foci_supports >= min_support
    conf <- conf[sel]
    supp <- foci_supports[sel]
    
    lapply(seq_along(conf), function(i) { 
      list(antecedent = format_condition(names(condition)),
           consequent = format_condition(names(conf)[[i]]),
           support = supp[[i]],
           confidence = conf[[i]])
    })
}

The callback function f() defines three arguments: condition, support and foci_supports. The names of the arguments are not random. Based on the argument names of the callback function, the searching algorithm provides information to the function. Here condition is a vector of indices representing the conjunction of predicates in a condition. By the predicate we mean the column in the source dataset. The support argument gets the relative frequency of the condition in the dataset. foci_supports is a vector of supports of special predicates, which we call “foci” (plural of “focus”), within the rows satisfying the condition. For implicative rules, foci are potential rule consequents.

Now we can run the digging for rules:

result <- dig(d,
              f = f,
              condition = !starts_with("Treatment"),
              focus = starts_with("Treatment"),
              disjoint = disj,
              min_length = 1,
              min_support = min_support)

As we return a list of lists in the callback function, we have to flatten the first level of lists in the result and binding it into a data frame:

result <- result %>%
  unlist(recursive = FALSE) %>%
  map(as_tibble) %>%
  do.call(rbind, .) %>%
  arrange(desc(support))

print(result)
#> # A tibble: 225 × 4
#>    antecedent                            consequent           support confidence
#>    <chr>                                 <chr>                  <dbl>      <dbl>
#>  1 {Type=Mississippi,uptake=(-Inf,17.9]} {Treatment=chilled}   0.155       0.813
#>  2 {Type=Mississippi,uptake=(28.3,37.1]} {Treatment=nonchill…  0.119       1    
#>  3 {Plant=Mc3}                           {Treatment=chilled}   0.0833      1    
#>  4 {Plant=Mc1}                           {Treatment=chilled}   0.0833      1    
#>  5 {Plant=Qn1}                           {Treatment=nonchill…  0.0833      1    
#>  6 {Plant=Mc2}                           {Treatment=chilled}   0.0833      1    
#>  7 {Plant=Mn1}                           {Treatment=nonchill…  0.0833      1    
#>  8 {Plant=Mn2}                           {Treatment=nonchill…  0.0833      1    
#>  9 {Plant=Mn3}                           {Treatment=nonchill…  0.0833      1    
#> 10 {Plant=Qc2}                           {Treatment=chilled}   0.0833      1    
#> # ℹ 215 more rows

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Version

Install

install.packages('nuggets')

Monthly Downloads

342

Version

1.2.0

License

GPL (>= 3)

Maintainer

Michal Burda

Last Published

October 11th, 2024

Functions in nuggets (1.2.0)

is_subset

Determine whether the first vector is a subset of the second vector
dig_correlations

Search for conditional correlations
var_grid

Create a tibble of combinations of xvar/yvar variable pairs.
dichotomize

Create dummy columns from logicals or factors in a data frame
which_antichain

Return indices of first elements of the list, which are incomparable with preceding elements.
dig

Search for rules
format_condition

Format condition - convert a character vector to character scalar
dig_implications

Search for implicative rules
nuggets-package

nuggets: Extensible Data Pattern Searching Framework
dig_grid

Search for grid-based rules