Data Manipulation with fcaR and dplyr

In fcaR: Formal Concept Analysis

knitr::opts_chunk$set(
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Introduction

Formal Concept Analysis (FCA) typically involves a workflow of cleaning data, extracting concepts, and analyzing implications. The fcaR package integrates seamlessly with dplyr, allowing you to use the "grammar of data manipulation" directly on FormalContext and ImplicationSet objects.

This integration provides S3 methods for:

• FormalContext: select, filter, mutate, arrange, rename.
• ImplicationSet: filter, arrange, slice.

Setup

First, load the necessary packages and the example dataset planets.

library(fcaR)
library(dplyr)

data("planets")
# Create the initial Formal Context
fc <- FormalContext$new(planets)

Part 1: Context Manipulation

Real-world data is rarely ready for FCA out of the box. You might need to derive new attributes, remove noise, or rename variables.

1.1 Renaming and Feature Engineering

We can use rename() to standardize attribute names and mutate() to create new attributes based on logic applied to existing ones. This is particularly powerful for conceptual scaling or creating higher-level abstractions.

# Let's clean up the context
fc_clean <- fc %>% 
  rename(
    has_moon = moon,
    no_moon  = no_moon,
    is_large = large,
    is_small = small
  ) %>%
  mutate(
    # Create a new binary attribute 'giant_loner'
    # (A planet that is large but has no moon)
    giant_loner = is_large == 1 & no_moon == 1,

    # Create 'extreme_size' (either small or large)
    extreme_size = is_small == 1 | is_large == 1
  )

# Check the new attributes
print(fc_clean$attributes)

1.2 Filtering and Selecting

We can filter the objects (rows) and select attributes (columns) to focus our analysis on a specific subset of the domain.

# Focus only on 'extreme' sized planets and keep specific attributes
fc_focused <- fc_clean %>%
  filter(extreme_size == 1) %>% 
  select(has_moon, giant_loner, is_large)

fc_focused$print()

Part 2: Mining and Filtering Implications

Once the context is clean, we extract the implications (association rules).

# We use the original context for more results
fc$find_implications()
rules <- fc$implications

cat("Total rules found:", rules$cardinality(), "\n")

2.1 Filtering by Metrics

You can use standard dplyr verbs to filter rules based on their quality measures: support, lhs_size (number of attributes in the premise), rhs_size, and size.

# Get strong rules (support > 0.2) that are not trivial (size > 2)
strong_rules <- rules %>% 
  filter(support > 0.2, size > 2) %>% 
  arrange(desc(support))

strong_rules$print()

2.2 Semantic Filtering

Often, you are looking for rules that involve specific attributes (e.g., "What implies having a moon?"). fcaR provides special helper functions available only inside filter():

• lhs("A") / lhs_has("A"): The Left-Hand Side MUST contain "A".
• rhs("B") / rhs_has("B"): The Right-Hand Side MUST contain "B".
• not_lhs("C"): The LHS must NOT contain "C".
• lhs_any("A", "B"): The LHS must contain either "A" or "B".

# Find rules that imply 'moon'
moon_rules <- rules %>% 
  filter(rhs("moon"))

cat("Rules implying 'moon':\n")
moon_rules$print()

2.3 Complex Pipelines

You can combine metrics, semantic logic, sorting, and slicing in a single pipeline. This allows for very specific queries like:

*"Find me the top 3 most supported rules about large planets that do not concern distance."

specific_rules <- rules %>% 
  filter(
    lhs("large"),     # Must be about large planets
    not_lhs("far"),    # Ignore far planets
    support >= 0.2    # Minimum support threshold
  ) %>% 
  arrange(desc(support)) %>% 
  slice(1:3)          # Take the top 3

specific_rules$print()

Conclusion

The integration of dplyr into fcaR allows for a fluid, readable, and powerful workflow. You can clean your contexts and query your rule sets using the same tidy syntax you use for standard data frames.

fcaR documentation built on July 27, 2026, 5:06 p.m.