MultiFactor

Overview

MultiFactor aims to provide a consistent toolkit to incorporate relational data into data-analytical tools and methods. In practice, we expect MultiFactor to be used in combination with additional packages that extend it. For instance, see ariadne and anansi, two such packages that make use of MultiFactor to link and convert across biological database IDs and perform multi-omics integration analysis, respectively.

Get started using MultiFactor

Installation instructions

Get the latest stable R release from CRAN. Then install the released version of MultiFactor:

install.packages("MultiFactor")

Or install the development version from this repository:

install.packages("remotes")
remotes::install_github("minotau-R/MultiFactor")

Setup

library(MultiFactor)

# Wrangling
library(dplyr)

# Plotting
library(ggplot2)
library(systemfonts)
library(ragg)

# Load demo data
set.seed(2612)
tp <- trade_posts()

MultiFactor and LinkMap objects

LinkMap

The basic object in the MultiFactor package is the LinkMap, which contains relational information across two types of features. This format is sometimes referred to as an edge list. Under a thin layer of S7, the LinkMap object is a data.frame where the first two columns indicate which feature IDs that are considered linked. Optional additional columns are considered metadata.

Let’s use an example data set to take a closer look at LinkMap. The trade_posts() function generates data about fictional trade posts, where one type of good is traded for another. In this particular case, types of books are traded for types of furniture.

linkmap <- tp[[1]]
linkmap
#> A MultiFactor::LinkMap data.frame S7_object: 9 rows.
#>        books furniture
#> 1   red book     couch
#> 2 green book   cabinet
#> 3 plain book   cabinet
#> 4  blue book  wastebin
#> 5 plain book  wastebin
#> 6  blue book       box
#> 7 plain book       box
#> 8 fancy book      door
#> 9  blue book      door
#> 
#> @ levels:   2 variables: 
#>  $ books     : 6 Levels: fancy book ... plain book 
#>  $ furniture : 6 Levels: couch ... door 
#> 
#> @ metadata: 4 variables: 
#> List of 4
#>  $ books_emoji    : chr  "📕" "📗" "📓" "📘" ...
#>  $ books_runes    : chr  "1F4D5" "1F4D7" "1F4D3" "1F4D8" ...
#>  $ furniture_emoji: chr  "🛋" "🗄" "🗄" "🗑" ...
#>  $ furniture_runes: chr  "1F6CB" "1F5C4" "1F5C4" "1F5D1" ...

The two types of goods are captured by the two columns, with column names representing the types of good and rows representing which specific type-pairs are traded at that trade post, or, more generally, are linked in that LinkMap.

levels( linkmap )
#> $books
#> [1] "fancy book"  "green book"  "red book"    "blue book"   "orange book"
#> [6] "plain book" 
#> 
#> $furniture
#> [1] "couch"    "cabinet"  "wastebin" "bed"      "box"      "door"

Notice that LinkMap is two factor columns in a trench coat. We can inspect the levels as usual.

We can see that we have several of these types of trade posts in our data set. However, we also notice that the column names - the types of goods - can differ.

tp[[2]]
#> A MultiFactor::LinkMap data.frame S7_object: 9 rows.
#>   clothing furniture
#> 1    scarf     couch
#> 2    scarf   cabinet
#> 3   gloves  wastebin
#> 4      hat  wastebin
#> 5    scarf  wastebin
#> 6    dress       bed
#> 7  t-shirt       box
#> 8    scarf       box
#> 9      hat      door
#> 
#> @ levels:   2 variables: 
#>  $ clothing  : 6 Levels: t-shirt ... scarf 
#>  $ furniture : 6 Levels: couch ... door 
#> 
#> @ metadata: 4 variables: 
#> List of 4
#>  $ clothing_emoji : chr  "🧣" "🧣" "🧤" "🎩" ...
#>  $ clothing_runes : chr  "1F9E3" "1F9E3" "1F9E4" "1F3A9" ...
#>  $ furniture_emoji: chr  "🛋" "🗄" "🗑" "🗑" ...
#>  $ furniture_runes: chr  "1F6CB" "1F5C4" "1F5D1" "1F5D1" ...
tp[[3]]
#> A MultiFactor::LinkMap data.frame S7_object: 9 rows.
#>         books instruments
#> 1  green book     trumpet
#> 2    red book      guitar
#> 3   blue book      guitar
#> 4   blue book        drum
#> 5  green book    keyboard
#> 6 orange book    keyboard
#> 7    red book      fiddle
#> 8 orange book   saxophone
#> 9  plain book   saxophone
#> 
#> @ levels:   2 variables: 
#>  $ books       : 6 Levels: fancy book ... plain book 
#>  $ instruments : 6 Levels: trumpet ... saxophone 
#> 
#> @ metadata: 4 variables: 
#> List of 4
#>  $ books_emoji      : chr  "📗" "📕" "📘" "📘" ...
#>  $ books_runes      : chr  "1F4D7" "1F4D5" "1F4D8" "1F4D8" ...
#>  $ instruments_emoji: chr  "🎺" "🎸" "🎸" "🥁" ...
#>  $ instruments_runes: chr  "1F3BA" "1F3B8" "1F3B8" "1F941" ...

Visual representation of the LinkMaps in our data set.

The MultiFactor is in essence a collection of LinkMaps. Where LinkMaps contain relational information across two data types, MultiFactors are a representation of the relational graph formed by combining those LinkMaps. The full object tp, which contains the LinkMaps we just investigated, is in fact a MultiFactor object:

tp
#> A MultiFactor::MultiFactor list S7_object,
#>     6 feature types across 6 LinkMaps.
#> 
#>                     books furniture clothing instruments fruit marbles
#> books2furniture         5         5        .           .     .       .
#> clothing2furniture      .         6        5           .     .       .
#> books2instruments       5         .        .           6     .       .
#> fruit2instruments       .         .        .           6     5       .
#> furniture2marbles       .         5        .           .     .       6
#> instruments2marbles     .         .        .           5     .       4
#> 
#> Values represent unique feature names in that LinkMap.
#> 
#> @ levels:
#>  $ books       : 6 Levels: fancy book ... plain book 
#>  $ furniture   : 6 Levels: couch ... door 
#>  $ clothing    : 6 Levels: t-shirt ... scarf 
#>  $ instruments : 6 Levels: trumpet ... saxophone 
#>  $ fruit       : 6 Levels: apples ... grapes 
#>  $ marbles     : 6 Levels: red marble ... sparkly marble

Notice that a MultiFactor summarizes information across the component LinkMaps in several ways. First, The matrix shows the types of goods as columns and the component LinkMaps that contain this information as rows. The numbers in the matrix then show the number of unique types of that type of good in that particular LinkMap - and that are therefore linked to the second feature in the LinkMap.

For instance, in the top-left corner of the matrix, we can see that the first LinkMap links books and furniture and is aptly named books2furniture. Notice that five unique types of books as well as five types of furniture are mentioned in books2furniture.

Though MultiFactor is a list of LinkMaps under the hood, we can also interact with its matrix representation, as well as access the component levels:

dim(tp)
#> [1] 6 6
dimnames(tp)
#> [[1]]
#> [1] "books2furniture"     "clothing2furniture"  "books2instruments"  
#> [4] "fruit2instruments"   "furniture2marbles"   "instruments2marbles"
#> 
#> [[2]]
#> [1] "books"       "furniture"   "clothing"    "instruments" "fruit"      
#> [6] "marbles"
levels(tp)
#> $books
#> [1] "fancy book"  "green book"  "red book"    "blue book"   "orange book"
#> [6] "plain book" 
#> 
#> $clothing
#> [1] "t-shirt" "dress"   "socks"   "gloves"  "hat"     "scarf"  
#> 
#> $fruit
#> [1] "apples"   "pears"    "cherries" "oranges"  "melons"   "grapes"  
#> 
#> $furniture
#> [1] "couch"    "cabinet"  "wastebin" "bed"      "box"      "door"    
#> 
#> $instruments
#> [1] "trumpet"   "guitar"    "drum"      "keyboard"  "fiddle"    "saxophone"
#> 
#> $marbles
#> [1] "red marble"     "white marble"   "black marble"   "blue marble"   
#> [5] "8 marble"       "sparkly marble"
nlevels(tp)
#>       books    clothing       fruit   furniture instruments     marbles 
#>           6           6           6           6           6           6

Note that levels of the same type are automatically unified across all component LinkMaps with that type of level.

weave() a path

Going back to our trading post example, let’s say we’d want to get some new hats, but we only have fruit. While both fruit and clothing are available in our data set, there aren’t any trade post that deals in that particular pair of goods. However, we could imagine trading our fruit for clothing in steps: We can trade our fruit for a different good, which we trade for another one, until we reach a good that we can trade for clothing. The weave() function does exactly this:

fruit2clothing <- weave(tp, fruit ~ clothing)
fruit2clothing
#> A MultiFactor::LinkMap data.frame S7_object: 18 rows.
#>      fruit clothing
#> 1   apples  t-shirt
#> 2    pears  t-shirt
#> 3   apples   gloves
#> 4    pears   gloves
#> 5   apples      hat
#> 6    pears      hat
#> 7   apples    scarf
#> 8    pears    scarf
#> 9  oranges    scarf
#> 10  melons    scarf
#>  + 8 more rows. Use `print(n = ...)` to see more rows.
#> 
#> @ levels:   2 variables: 
#>  $ fruit    : 6 Levels: apples ... grapes 
#>  $ clothing : 6 Levels: t-shirt ... scarf

We receive a new LinkMap containing all fruits that could be traded for clothing.

Selecting a path

We can use select_path() to find the types of traded goods in order, or more generally, the paths that were traversed. If several paths exist, all will be traversed and included into one LinkMap.

select_path(tp, fruit ~ clothing)
#> A MultiFactor::factor_path S7_object from `fruit` to `clothing` with 2 sub-paths:
#> [[1]]
#> [1] "fruit"       "instruments" "books"       "furniture"   "clothing"   
#> 
#> [[2]]
#> [1] "fruit"       "instruments" "marbles"     "furniture"   "clothing"

Compatibility with `igraph` and `Matrix`

Convert `MultiFactor` and `LinkMap` objects to `igraph` representation

MultiFactor relies heavily on the excellent igraph and Matrix packages, particularly for path finding and sparse matrix representation.

Convert `MultiFactor` main graph representation to `igraph` object

library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:dplyr':
#> 
#>     as_data_frame, groups, union
#> The following objects are masked from 'package:stats':
#> 
#>     decompose, spectrum
#> The following object is masked from 'package:base':
#> 
#>     union
# Convert to an igraph object
g <- as.igraph(tp)
g
#> IGRAPH 280c1f0 UN-- 6 6 -- 
#> + attr: name (v/c), name (e/c), instruments_emoji (e/n),
#> | instruments_runes (e/n), marbles_emoji (e/n), marbles_runes (e/n),
#> | furniture_emoji (e/n), furniture_runes (e/n), books_emoji (e/n),
#> | books_runes (e/n), clothing_emoji (e/n), clothing_runes (e/n),
#> | fruit_emoji (e/n), fruit_runes (e/n)
#> + edges from 280c1f0 (vertex names):
#> [1] books      --furniture   clothing   --furniture   books      --instruments
#> [4] fruit      --instruments furniture  --marbles     instruments--marbles
# Plot graph across data types
plot(g)

Convert `LinkMap` relational information to `igraph` object

# Convert to an igraph object
lg <- as.igraph(fruit2clothing)
lg
#> IGRAPH e39809f UN-B 12 18 -- 
#> + attr: type (v/l), name (v/c)
#> + edges from e39809f (vertex names):
#>  [1] apples --t-shirt pears  --t-shirt apples --dress   oranges--dress  
#>  [5] melons --dress   grapes --dress   apples --gloves  pears  --gloves 
#>  [9] apples --hat     pears  --hat     oranges--hat     melons --hat    
#> [13] grapes --hat     apples --scarf   pears  --scarf   oranges--scarf  
#> [17] melons --scarf   grapes --scarf
# Same information as the LinkMap: 
plot(lg)

Convert `LinkMap` linkage information to sparse adjacency `Matrix`

# Convert to an igraph object
m <- as.matrix(fruit2clothing)
m
#> 6 x 6 sparse Matrix of class "ngCMatrix"
#>           clothing
#> fruit      t-shirt dress socks gloves hat scarf
#>   apples         |     |     .      |   |     |
#>   pears          |     .     .      |   |     |
#>   cherries       .     .     .      .   .     .
#>   oranges        .     |     .      .   |     |
#>   melons         .     |     .      .   |     |
#>   grapes         .     |     .      .   |     |

Utilities

MultiFactor also provides some utility functions for data wrangling on tables with features (rows) of the appropriate type.

# Generate small example table
generate_sample <- function(n) rbinom(n, rbinom(n, 100, runif(n)), runif(n))
n <- nlevels(tp)[["clothing"]]

clothing_table <- as.data.frame( replicate( 10,  generate_sample(n)) )
rownames(clothing_table) <- levels(tp)$clothing

clothing_table
#>         V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> t-shirt 14  8 34 72 47  4 32 50  0   5
#> dress   16 11 34 40 35 63  2 28  0  42
#> socks   20 24 20 50  2  0 19  5 17  46
#> gloves   0 34 10  2 21 30  0  3 13  16
#> hat      0 61  0  4  3  0  7 20 38  21
#> scarf    4 80 47 72 29 55  4  4 16  25

weave_apply

weave_apply allows us to run arbitrary code on subsets of a table, based on groupings on the left hand of the formula:

weave_apply(
  .x = tp, .path = fruit ~ clothing, 
  .data = clothing_table, .fun = as.data.frame
  )
#> $apples
#>         V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> dress   16 11 34 40 35 63  2 28  0  42
#> hat      0 61  0  4  3  0  7 20 38  21
#> scarf    4 80 47 72 29 55  4  4 16  25
#> t-shirt 14  8 34 72 47  4 32 50  0   5
#> gloves   0 34 10  2 21 30  0  3 13  16
#> 
#> $pears
#>         V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> t-shirt 14  8 34 72 47  4 32 50  0   5
#> gloves   0 34 10  2 21 30  0  3 13  16
#> hat      0 61  0  4  3  0  7 20 38  21
#> scarf    4 80 47 72 29 55  4  4 16  25
#> 
#> $oranges
#>       V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> dress 16 11 34 40 35 63  2 28  0  42
#> hat    0 61  0  4  3  0  7 20 38  21
#> scarf  4 80 47 72 29 55  4  4 16  25
#> 
#> $melons
#>       V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> dress 16 11 34 40 35 63  2 28  0  42
#> hat    0 61  0  4  3  0  7 20 38  21
#> scarf  4 80 47 72 29 55  4  4 16  25
#> 
#> $grapes
#>       V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> dress 16 11 34 40 35 63  2 28  0  42
#> hat    0 61  0  4  3  0  7 20 38  21
#> scarf  4 80 47 72 29 55  4  4 16  25

# More complex example: 
# For all subgroups of clothing corresponding to one particular fruit, if that 
# group has more than two rows (types of clothing), fit a statistical model. 
# 
weave_apply(
  tp, fruit ~ clothing, clothing_table, function(x) {
    if( NROW(x) <= 2 ) return( NULL )
    # else: 
    summary( lm(V1 ~ V2, data = x) )
    }
)
#> $apples
#> 
#> Call:
#> lm(formula = V1 ~ V2, data = x)
#> 
#> Residuals:
#>   dress     hat   scarf t-shirt  gloves 
#>   4.175  -2.787   4.647   1.633  -7.668 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)  
#> (Intercept) 13.81295    4.58505   3.013   0.0571 .
#> V2          -0.18075    0.09576  -1.887   0.1555  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 6.007 on 3 degrees of freedom
#> Multiple R-squared:  0.5428, Adjusted R-squared:  0.3905 
#> F-statistic: 3.562 on 1 and 3 DF,  p-value: 0.1555
#> 
#> 
#> $pears
#> 
#> Call:
#> lm(formula = V1 ~ V2, data = x)
#> 
#> Residuals:
#> t-shirt  gloves     hat   scarf 
#>   4.522  -6.050  -2.489   4.017 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  10.5332     6.1564   1.711    0.229
#> V2           -0.1319     0.1156  -1.141    0.372
#> 
#> Residual standard error: 6.3 on 2 degrees of freedom
#> Multiple R-squared:  0.3941, Adjusted R-squared:  0.09117 
#> F-statistic: 1.301 on 1 and 2 DF,  p-value: 0.3722
#> 
#> 
#> $oranges
#> 
#> Call:
#> lm(formula = V1 ~ V2, data = x)
#> 
#> Residuals:
#>  dress    hat  scarf 
#>  1.256 -4.563  3.306 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  16.9835     6.6920   2.538    0.239
#> V2           -0.2036     0.1145  -1.778    0.326
#> 
#> Residual standard error: 5.773 on 1 degrees of freedom
#> Multiple R-squared:  0.7597, Adjusted R-squared:  0.5193 
#> F-statistic: 3.161 on 1 and 1 DF,  p-value: 0.3262
#> 
#> 
#> $melons
#> 
#> Call:
#> lm(formula = V1 ~ V2, data = x)
#> 
#> Residuals:
#>  dress    hat  scarf 
#>  1.256 -4.563  3.306 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  16.9835     6.6920   2.538    0.239
#> V2           -0.2036     0.1145  -1.778    0.326
#> 
#> Residual standard error: 5.773 on 1 degrees of freedom
#> Multiple R-squared:  0.7597, Adjusted R-squared:  0.5193 
#> F-statistic: 3.161 on 1 and 1 DF,  p-value: 0.3262
#> 
#> 
#> $grapes
#> 
#> Call:
#> lm(formula = V1 ~ V2, data = x)
#> 
#> Residuals:
#>  dress    hat  scarf 
#>  1.256 -4.563  3.306 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  16.9835     6.6920   2.538    0.239
#> V2           -0.2036     0.1145  -1.778    0.326
#> 
#> Residual standard error: 5.773 on 1 degrees of freedom
#> Multiple R-squared:  0.7597, Adjusted R-squared:  0.5193 
#> F-statistic: 3.161 on 1 and 1 DF,  p-value: 0.3262

Compatibility with the tidyverse: `weave_to_tbl`

For those who prefer to use tidyverse, weave_to_tbl() returns a tidy wide-format table, ready to be grouped based on the first two columns.

weave_to_tbl(tp, .path = fruit ~ clothing, .data = clothing_table)
#>      fruit clothing V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> 1   apples    dress 16 11 34 40 35 63  2 28  0  42
#> 2  oranges    dress 16 11 34 40 35 63  2 28  0  42
#> 3   melons    dress 16 11 34 40 35 63  2 28  0  42
#> 4   grapes    dress 16 11 34 40 35 63  2 28  0  42
#> 5   apples      hat  0 61  0  4  3  0  7 20 38  21
#> 6  oranges      hat  0 61  0  4  3  0  7 20 38  21
#> 7   melons      hat  0 61  0  4  3  0  7 20 38  21
#> 8   grapes      hat  0 61  0  4  3  0  7 20 38  21
#> 9   apples    scarf  4 80 47 72 29 55  4  4 16  25
#> 10 oranges    scarf  4 80 47 72 29 55  4  4 16  25
#> 11  grapes    scarf  4 80 47 72 29 55  4  4 16  25
#> 12  apples  t-shirt 14  8 34 72 47  4 32 50  0   5
#> 13   pears  t-shirt 14  8 34 72 47  4 32 50  0   5
#> 14  apples   gloves  0 34 10  2 21 30  0  3 13  16
#> 15   pears   gloves  0 34 10  2 21 30  0  3 13  16
#> 16   pears      hat  0 61  0  4  3  0  7 20 38  21
#> 17   pears    scarf  4 80 47 72 29 55  4  4 16  25
#> 18  melons    scarf  4 80 47 72 29 55  4  4 16  25

Reading and parsing adjaceny list-formatted files

Relational data between two types (bipartite) is sometimes expressed as a text file to be read row-wise. The first element of each row specifies a level of the first type of data, whereas all following elements all from the second type, indicate which levels it is connected to. These files can be tricky to load and properly parse into R. The function read_adjacency_list() allows these files to be read from file (or URL).

Example of adjacency list formatted data

We’ll format the LinkMap from fruit to furniture that we generated above.

# Use igraph to deconstruct a graph into an adjacency list, coerce to characters
adj <- lapply( as_adj_list( as.igraph(fruit2clothing) ), as_ids )
# Only keep the 'fruit' nodes that have more than one link for now
adj <- adj[ levels(fruit2clothing)$fruit ]
adj <- adj[ lengths(adj) > 0 ]
# collapse names to first elements of character vectors
adj <- mapply(c, names(adj), adj, USE.NAMES = FALSE)
adj <- vapply(adj, paste, collapse = "\t", "")

If stored in a file in adjacency list format, it may look like this:

pear\tdesk
cherry\tchair\ttable\tdesk
melon\tchair\ttable\tdesk 
blueberry\tchair\tdesk

# Create some temporary file to read from:
temp <- tempfile()
# fill the temp file with the adjacency list we just constructed 
t.con <- file(temp, "w")
cat(adj, file = t.con, sep = "\n")
close(t.con)

adj_data <- read_adjacency_list(temp)
adj_data
#> A MultiFactor::LinkMap data.frame S7_object: 18 rows.
#>       id.x    id.y
#> 1   apples t-shirt
#> 2   apples   dress
#> 3   apples  gloves
#> 4   apples     hat
#> 5   apples   scarf
#> 6    pears t-shirt
#> 7    pears  gloves
#> 8    pears     hat
#> 9    pears   scarf
#> 10 oranges   dress
#>  + 8 more rows. Use `print(n = ...)` to see more rows.
#> 
#> @ levels:   2 variables: 
#>  $ id.x : 5 Levels: apples grapes ... pears 
#>  $ id.y : 5 Levels: dress gloves ... t-shirt

# Notice that the unlinked data types are no longer in the graph.  
plot(as.igraph(adj_data))

Session Info

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] igraph_2.3.3      ragg_1.5.2        systemfonts_1.3.2 ggplot2_4.0.3    
#> [5] dplyr_1.2.1       MultiFactor_0.1.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.7-5       gtable_0.3.6       jsonlite_2.0.0     compiler_4.6.1    
#>  [5] tidyselect_1.2.1   jquerylib_0.1.4    scales_1.4.0       textshaping_1.0.5 
#>  [9] yaml_2.3.12        fastmap_1.2.0      lattice_0.22-9     R6_2.6.1          
#> [13] generics_0.1.4     knitr_1.51         htmlwidgets_1.6.4  forcats_1.0.1     
#> [17] tibble_3.3.1       desc_1.4.3         RColorBrewer_1.1-3 bslib_0.11.0      
#> [21] pillar_1.11.1      rlang_1.3.0        cachem_1.1.0       xfun_0.60         
#> [25] fs_2.1.0           sass_0.4.10        S7_0.2.2           otel_0.2.0        
#> [29] cli_3.6.6          withr_3.0.3        pkgdown_2.2.1      magrittr_2.0.5    
#> [33] digest_0.6.39      grid_4.6.1         lifecycle_1.0.5    vctrs_0.7.3       
#> [37] evaluate_1.0.5     glue_1.8.1         farver_2.1.2       rmarkdown_2.31    
#> [41] tools_4.6.1        pkgconfig_2.0.3    htmltools_0.5.9

MultiFactor

Overview

Get started using MultiFactor

Installation instructions

Setup

MultiFactor and LinkMap objects

LinkMap

Visual representation of the LinkMaps in our data set.

MultiFactor

weave() a path

Selecting a path

Compatibility with igraph and Matrix

Convert MultiFactor and LinkMap objects to igraph representation

Convert MultiFactor main graph representation to igraph object

Convert LinkMap relational information to igraph object

Convert LinkMap linkage information to sparse adjacency Matrix

Utilities

weave_apply

Compatibility with the tidyverse: weave_to_tbl

Reading and parsing adjaceny list-formatted files

Example of adjacency list formatted data

Session Info

Compatibility with `igraph` and `Matrix`

Convert `MultiFactor` and `LinkMap` objects to `igraph` representation

Convert `MultiFactor` main graph representation to `igraph` object

Convert `LinkMap` relational information to `igraph` object

Convert `LinkMap` linkage information to sparse adjacency `Matrix`

Compatibility with the tidyverse: `weave_to_tbl`