knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5, warning = FALSE )

In this vignette, we present the main functionalities and data structures of the `fcaR`

package when working with formal contexts and concepts, in FCA.

We load the `fcaR`

package by:

```
library(fcaR)
```

We are going to work with two datasets, a crisp one and a fuzzy one.

The classical (binary) dataset is the well-known `planets`

formal context, presented in

Wille R (1982). “Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts.” In Ordered Sets, pp. 445–470. Springer.

knitr::kable(planets, format = "html", booktabs = TRUE)

The other formal context is fuzzy and is defined by the following matrix I:

objects <- paste0("O", 1:6) n_objects <- length(objects) attributes <- paste0("P", 1:6) n_attributes <- length(attributes) I <- matrix(data = c(0, 1, 0.5, 0, 0, 0.5, 0, 1, 0.5, 0, 0, 0.5, 0.5, 1, 0, 0, 1, 0, 0.5, 0, 0, 1, 0.5, 0, 1, 0, 0, 0.5, 0, 0, 0, 0, 1, 0, 0, 1), nrow = n_objects, byrow = FALSE) colnames(I) <- attributes rownames(I) <- objects

knitr::kable(I, format = "html", booktabs = TRUE)

The first step when using the `fcaR`

package to analyze a formal context is to create an object of class `FormalContext`

which will store all the information related to the context.

In our examples, we create two objects:

fc_planets <- FormalContext$new(planets) fc_I <- FormalContext$new(I)

Internally, the object stores information about whether the context is binary or the names of objects and attributes, which are taken from the rownames and colnames of the provided matrix.

Once created the `FormalContext`

objects, we can print them or plot them as heatmaps (with functions `print()`

and `plot()`

):

print(fc_planets) print(fc_I)

fc_planets$plot() fc_I$plot()

Also, we can export the formal context as a LaTeX table:

fc_planets$to_latex()

One can also create `FormalContext`

s by importing RDS, CSV or CXT files directly:

# Read CSV filename <- system.file("contexts", "airlines.csv", package = "fcaR") fc1 <- FormalContext$new(filename) fc1 # Read CXT filename <- system.file("contexts", "lives_in_water.cxt", package = "fcaR") fc2 <- FormalContext$new(filename) fc2

We can compute the dual formal context of a given one by using the `dual()`

method:

fc_dual <- fc_planets$dual() fc_dual

The result is a `FormalContext`

where attributes are now the objects of the previous formal context and viceversa.

The basic operation in FCA is the computation of closures given an attribute set, by using the two derivation operators, extent and intent.

The intent of a (probably fuzzy) set of objects is the set of their common attributes:

# Define a set of objects S <- Set$new(attributes = fc_planets$objects) S$assign(Earth = 1, Mars = 1) S # Compute the intent of S fc_planets$intent(S)

Analogously, the extent of a set of attributes is the set of objects which possess all the attributes in the given set:

# Define a set of objects S <- Set$new(attributes = fc_planets$attributes) S$assign(moon = 1, large = 1) S # Compute the extent of S fc_planets$extent(S)

The composition of intent and extent is the closure of a set of attributes:

# Compute the closure of S Sc <- fc_planets$closure(S) Sc

This means that all planets which have the attributes `moon`

and `large`

also have `far`

in common.

We can check whether a set is closed (that is, it is equal to its closure), using `is_closed()`

:

fc_planets$is_closed(S) fc_planets$is_closed(Sc)

An interesting point when managing formal contexts is the ability to reduce the context, removing redundancies, while retaining all the knowledge. This is accomplished by two functions: `clarify()`

, which removes duplicated attributes and objects (columns and rows in the original matrix); and `reduce()`

, which uses closures to remove dependent attributes, but only on binary formal contexts. The resulting `FormalContext`

is equivalent to the original one in both cases.

fc_planets$reduce(TRUE) fc_I$clarify(TRUE)

Note that merged attributes or objects are stored in the new formal context by using squared brackets to unify them, e.g. `[Mercury, Venus]`

.

The function to extract the canonical basis of implications and the concept lattice is `find_implications()`

. Its use is to store a `ConceptLattice`

and an `ImplicationSet`

objects internally in the `FormalContext`

object after running the NextClosure algorithm.

It can be used both for binary and fuzzy formal contexts, resulting in binary or fuzzy concepts and implications:

fc_planets$find_implications() fc_I$find_implications()

We can inspect the results as:

# Concepts fc_planets$concepts # Implications fc_planets$implications

Once we have computed the concepts, we can build the *standard context* (J, M, $\le$), where J is the set of join-irreducible concepts and M are the meet-irreducible ones. Join and meet are another name for supremum and infimum operations in the concept lattice.

The function `standardize()`

works for all FormalContext where the concept lattice has been found, and it produces a new `FormalContext`

object:

fc_planets$standardize() fc_I$standardize()

Note that now objects are named J1, J2... and attributes are M1, M2..., from join and meet.

A `FormalContext`

is saved in RDS format using its own `save()`

method, which is more efficient than the base `saveRDS()`

and `readRDS()`

.

fc$save(filename = "./fc.rds")

In order to load a previously saved `FormalContext`

, it suffices to do:

fc2 <- FormalContext$new("./fc.rds")

In this case, `fc`

and `fc2`

contain the same information.

We are going to use the previously computed concept lattices for the two `FormalContext`

objects.

The concept lattice can be plotted using a Hasse diagram and the function `plot()`

inside the `ConceptLattice`

component:

fc_planets$concepts$plot() fc_I$concepts$plot()

If one desires to get the list of concepts printed, or in $\LaTeX$ format, just:

# Printing fc_planets$concepts # LaTeX fc_planets$concepts$to_latex()

For a `ConceptLattice`

, one may want to retrieve particular concepts, using a subsetting as in `R`

:

fc_planets$concepts[2:3]

Or get all the extents and all the intents of all concepts, as sparse matrices:

fc_planets$concepts$extents() fc_planets$concepts$intents()

The support of concepts can be computed using the function `support()`

:

fc_planets$concepts$support()

When the concept lattice is too large, it can be useful in certain occasions to just work with a sublattice of the complete lattice. To this end, we use the `sublattice()`

function.

For instance, to build the sublattice of those concepts with support greater than 0.5, we can do:

# Get the index of those concepts with support # greater than the threshold idx <- which(fc_I$concepts$support() > 0.2) # Build the sublattice sublattice <- fc_I$concepts$sublattice(idx) sublattice

And we can plot just the sublattice:

sublattice$plot()

It may be interesting to use the notions of subconcept and superconcept. Given a concept, we can compute all its subconcepts and all its superconcepts:

# The fifth concept C <- fc_planets$concepts$sub(5) C # Its subconcepts: fc_planets$concepts$subconcepts(C) # And its superconcepts: fc_planets$concepts$superconcepts(C)

Also, we can define infimum and supremum of a set of concepts as the greatest common subconcept of all the given concepts, and the lowest common superconcept of them, and can be computed by:

# A list of concepts C <- fc_planets$concepts[5:7] C # Supremum of the concepts in C fc_planets$concepts$supremum(C) # Infimum of the concepts in C fc_planets$concepts$infimum(C)

Also irreducible elements with respect to join (supremum) and meet (infimum) can be computed for a given concept lattice:

fc_planets$concepts$join_irreducibles() fc_planets$concepts$meet_irreducibles()

This are the concepts used to build the *standard context*, mentioned above.

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.