Package 'cooccure'

IMDB actor-genre long table (1970-2024)

Description

Long-format table mapping each of the 624 actors in actors to every genre of every movie they appeared in. Use this to build an actor co-occurrence network grouped by genre: which actors share the same genres? Pass field = "actor" and by = "genre" to cooccurrence.

Usage

actor_genres

Format

A data frame with 2,502 rows and 2 variables:

actor

Actor name.

genre

Genre label (one row per actor-genre combination).

Source

https://developer.imdb.com/non-commercial-datasets/

Examples

head(actor_genres)
cooccurrence(actor_genres, field = "actor", by = "genre", similarity = "jaccard")

---

IMDB actor-movie long table (1970-2024)

Description

Long-format bipartite table linking actors to movies in movies. Pre-filtered to the 624 actors who appear in at least two movies, so all similarity measures compute instantly. Pass field = "actor" and by = "tconst" to cooccurrence to build an actor co-appearance network.

Usage

actors

Format

A data frame with 1,267 rows and 7 variables:

actor

Actor name.

tconst

IMDB title identifier linking to movies.

primaryTitle

Movie title.

startYear

Release year (integer).

decade

Release decade as a character string.

genres

Comma-separated genre labels for the linked movie.

averageRating

IMDB average user rating for the linked movie.

Source

https://developer.imdb.com/non-commercial-datasets/

Examples

head(actors)
cooccurrence(actors, field = "actor", by = "tconst", similarity = "jaccard")

---

Convert to cograph network

Description

Creates a cograph_network object from a cooccurrence edge list, compatible with cograph::splot() and other cograph functions.

Usage

as_cograph(x, ...)

## S3 method for class 'cooccurrence'
as_cograph(x, ...)

Arguments

x	A cooccurrence data frame.
...	Ignored.

Value

A cograph_network object.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
if (requireNamespace("cograph", quietly = TRUE)) {
  net <- as_cograph(res)
  net$n_nodes
}

---

Convert to igraph

Description

Creates an undirected, weighted igraph graph from a cooccurrence edge list.

Usage

as_igraph(x, ...)

## S3 method for class 'cooccurrence'
as_igraph(x, ...)

Arguments

x	A cooccurrence data frame.
...	Passed to igraph::graph_from_data_frame.

Value

An igraph object.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- as_igraph(res)
  igraph::vcount(g)
}

---

Extract the co-occurrence matrix

Description

Returns the full square co-occurrence matrix (normalized + scaled). Use type = "raw" for the raw count matrix.

Usage

as_matrix(x, ...)

## S3 method for class 'cooccurrence'
as_matrix(x, type = c("normalized", "raw"), ...)

Arguments

x	A cooccurrence data frame.
...	Ignored.
type	Character. "normalized" (default) or "raw".

Value

A numeric matrix.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
as_matrix(res)
as_matrix(res, type = "raw")

---

Convert to Nestimate netobject

Description

Creates a netobject from a cooccurrence edge list, compatible with Nestimate::centrality(), Nestimate::bootstrap_network(), etc.

Usage

as_netobject(x, ...)

## S3 method for class 'cooccurrence'
as_netobject(x, ...)

Arguments

x	A cooccurrence data frame.
...	Ignored.

Value

A netobject with class c("netobject", "cograph_network").

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
if (requireNamespace("Nestimate", quietly = TRUE)) {
  net <- as_netobject(res)
  net$n_nodes
}

---

Convert to tidygraph

Description

Creates a tbl_graph from a cooccurrence edge list.

Usage

as_tidygraph(x, ...)

## S3 method for class 'cooccurrence'
as_tidygraph(x, ...)

Arguments

x	A cooccurrence data frame.
...	Ignored.

Value

A tbl_graph object.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
if (requireNamespace("tidygraph", quietly = TRUE) &&
    requireNamespace("igraph",    quietly = TRUE)) {
  as_tidygraph(res)
}

---

Tidy node or group table from a cooccurrence summary

Description

Returns the summary's node-level metrics (one row per node) or its group-level metrics (one row per group) as a plain data.frame, so callers never have to reach into the summary object's internals.

Usage

## S3 method for class 'summary.cooccurrence'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  what = c("nodes", "groups"),
  ...
)

Arguments

x	A summary.cooccurrence object.
row.names	Passed to as.data.frame; unused.
optional	Passed to as.data.frame; unused.
what	Character. "nodes" (default) for one row per node with degree, strength, count strength, and frequency; "groups" for one row per group when the network was built with split_by.
...	Ignored.

Value

A base data.frame. For what = "nodes", one row per node; for what = "groups", one row per group (zero rows when the network was not split).

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
as.data.frame(summary(res))

---

Build a co-occurrence network

Description

Constructs an undirected co-occurrence network from various input formats and returns a tidy edge data frame. Argument names follow the citenets convention.

Usage

cooccurrence(
  data,
  field = NULL,
  by = NULL,
  sep = NULL,
  weight_by = NULL,
  split_by = NULL,
  similarity = c("none", "jaccard", "cosine", "inclusion", "association", "dice",
    "equivalence", "relative"),
  counting = c("full", "fractional", "attention"),
  scale = NULL,
  threshold = 0,
  min_occur = 1L,
  top_n = NULL,
  output = c("default", "gephi", "igraph", "cograph", "matrix"),
  vars = NULL,
  actor = NULL,
  action = NULL,
  time = NULL,
  session = NULL,
  order = NULL,
  time_threshold = 900,
  group = NULL,
  aggregate_by = NULL,
  aggregate = c("sum", "mean", "min", "max"),
  window = NULL,
  lambda = 1,
  ...
)

co(
  data,
  field = NULL,
  by = NULL,
  sep = NULL,
  weight_by = NULL,
  split_by = NULL,
  similarity = c("none", "jaccard", "cosine", "inclusion", "association", "dice",
    "equivalence", "relative"),
  counting = c("full", "fractional", "attention"),
  scale = NULL,
  threshold = 0,
  min_occur = 1L,
  top_n = NULL,
  output = c("default", "gephi", "igraph", "cograph", "matrix"),
  vars = NULL,
  actor = NULL,
  action = NULL,
  time = NULL,
  session = NULL,
  order = NULL,
  time_threshold = 900,
  group = NULL,
  aggregate_by = NULL,
  aggregate = c("sum", "mean", "min", "max"),
  window = NULL,
  lambda = 1,
  ...
)

Arguments

data	Input data. Accepts: A data.frame with a delimited column (field + sep). A data.frame in long/bipartite format (field + by). A binary (0/1 or TRUE/FALSE) data.frame or matrix (auto-detected). A one-hot or count table with vars naming the indicator columns; other columns (ids, metadata) are ignored. A wide sequence data.frame or matrix (non-binary). A list of character vectors (each element is a transaction). A nestimate_data object from Nestimate::prepare(); its sequence_data is used, so event logs can be sessionized there and networked here.
field	Character. The entity column — determines what the nodes are. For delimited format, a single column split by sep. For long/bipartite, the item column. For multi-column delimited, a vector of column names pooled per row. Use field = "all" for wide sequence data (e.g. TraMineR / tna format) where every column is a time point and cell values are the items.
by	Character or NULL. Grouping column for long/bipartite format. Each unique value defines one transaction.
sep	Character or NULL. Separator for splitting delimited fields.
weight_by	Character or NULL. Column name containing a numeric association strength for each entity-transaction pair. Only accepted for long format (field + by). When supplied, each entity contributes its weight rather than 1, so $C_{ij} = \sum_d w_{id} \cdot w_{jd}$. Typical use: topic-document probability matrices from LDA or similar models.
split_by	Character or NULL. Column name to split the data by before computing co-occurrence. A separate network is computed per group and the results are combined into a single data frame with an additional group column. Only works with data.frame inputs.
similarity	Character. Similarity measure: "none" Raw co-occurrence counts. "jaccard" $C_{ij} / (f_i + f_j - C_{ij})$. "cosine" Salton's cosine: $C_{ij} / \sqrt{f_i \cdot f_j}$. "inclusion" Simpson coefficient: $C_{ij} / \min(f_i, f_j)$. "association" Association strength: $C_{ij} / (f_i \cdot f_j)$ (van Eck & Waltman, 2009). "dice" $2 C_{ij} / (f_i + f_j)$. "equivalence" Salton's cosine squared: $C_{ij}^2 / (f_i \cdot f_j)$. "relative" Row-normalized: each row sums to 1.
counting	Character. Counting method: "full" Each co-occurring pair adds 1 regardless of transaction size. Default. "fractional" Each pair adds $1 / (n_i - 1)$ where $n_i$ is the number of items in transaction $i$. Transactions with many items contribute less per pair (Perianes-Rodriguez et al., 2016). "attention" Each pair within a transaction contributes $\exp(-|pos_i - pos_j| / \lambda)$ — closer positions give a stronger edge, distant pairs decay. Requires ordered transactions (list / wide / delimited / windowed input). The decay rate $\lambda$ is controlled by the lambda argument.
scale	Character or NULL. Optional scaling applied to weights after similarity normalization: NULL or "none" No scaling. "minmax" Min-max to $[0, 1]$. "log" Natural log: $\log(1 + w)$. "log10" Log base 10: $\log_{10}(1 + w)$. "binary" Binary: 1 if $w > 0$, else 0. "zscore" Z-score standardization. "sqrt" Square root. "proportion" Divide by sum of all weights.
threshold	Numeric. Minimum edge weight to retain, applied after similarity and scaling. The default 0 means no filtering rather than "drop negative weights", so centering scalings such as scale = "zscore" keep their negative half. Pass a positive value to filter.
min_occur	Integer. Minimum entity frequency. Entities appearing in fewer than min_occur transactions are dropped. Default 1.
top_n	Integer or NULL. Keep only the top top_n edges by weight. When split_by is used, applied per group. Default NULL (all edges).
output	Character. Column naming convention for the output: "default" from, to, weight, count. "gephi" Source, Target, Weight, Type (= "Undirected"). Ready for Gephi import. "igraph" Returns an igraph graph object directly. "cograph" Returns a cograph_network object directly. "matrix" Returns the square co-occurrence matrix.
vars	Column specification or NULL. The indicator (one-hot) columns, one column per item. Resolved like select in subset, so all of these work: a bare range (vars = A:D), bare names (vars = c(A, B, C)), positions (vars = 2:5), negative selection (vars = -c(id, note)), a logical mask, or a character vector. Every other column — ids, timestamps, metadata — is ignored, so a one-hot table that also carries identifier columns needs no pre-processing. Cell values are read as presence: any non-zero, non-NA value marks the item present, so count tables such as document-term matrices work as well as 0/1 and TRUE/FALSE. NA counts as absent. Cannot be combined with field, by, or sep. A purely binary table with no extra columns is still auto-detected without vars.
actor	Character vector or NULL. Column(s) identifying who performed the action. When NULL, all rows are treated as one actor. Only used with action.
action	Character or NULL. Column holding the event/state for raw event-log input. When supplied, the log is sessionized into ordered sequences first and each session becomes one transaction, so window and counting = "attention" apply. Requires the Nestimate package, which performs the sessionization.
time	Character or NULL. Timestamp column. Accepts ISO8601, Unix time, and common date/time formats. When NULL, row order defines the sequence. Only used with action.
session	Character vector or NULL. Column(s) giving an explicit session grouping. Combined with time, sessions are further split on time gaps. Only used with action.
order	Character or NULL. Column used to break ties when timestamps are identical. Only used with action.
time_threshold	Numeric. Maximum gap in seconds between consecutive events before a new session starts. Use Inf to keep each actor as a single sequence regardless of gaps. Default 900 (15 minutes). Only used with action and time.
group	Character or NULL. Alias for split_by.
aggregate_by	Character or NULL. Column name to group the data by before computing co-occurrence. For each unique value, the per-group network is computed (with the chosen similarity, counting, scale, window); the per-group edge weights are then combined across groups via aggregate into ONE final network. Differs from split_by, which keeps groups separate. Cannot be combined with split_by. Only applies to data frame inputs.
aggregate	Character. How to combine edge weights across groups when aggregate_by is used: "sum" (default), "mean", "min", or "max". The count column is always summed. threshold and top_n are applied AFTER aggregation.
window	Integer or NULL. Sliding-window size for categorical time-series / ordered-sequence input. When set to an integer $w \ge 2$, every window of w consecutive positions in a sequence becomes a mini-transaction; states inside the same window co-occur. Sequences shorter than w contribute no transactions. Only applies to ordered formats: wide (field = "all") and list. Default NULL (whole sequence treated as one transaction — bag of states).
lambda	Numeric. Decay rate for counting = "attention". Higher lambda → slower decay → distant pairs still contribute. Default 1.0, matching the tna package. Ignored for other counting methods.
...	Currently unused.

Value

Depends on output:

• "default": A cooccurrence data frame with columns from, to, weight, count (and group when split_by is used).

• "gephi": A data frame with columns Source, Target, Weight, Type, Count. Ready for Gephi CSV import.

• "igraph": An igraph graph object.

• "cograph": A cograph_network object.

• "matrix": A square numeric co-occurrence matrix.

For the data frame outputs, rows are sorted by weight descending and attributes store the full matrix, item frequencies, and parameters.

References

van Eck, N. J., & Waltman, L. (2009). How to normalize co-occurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651.

Examples

# Delimited keywords
df <- data.frame(
  id = 1:4,
  keywords = c("network; graph", "graph; matrix; network",
               "matrix; algebra", "network; algebra; graph")
)
cooccurrence(df, field = "keywords", sep = ";")

# Split by a grouping variable
df$year <- c(2020, 2020, 2021, 2021)
cooccurrence(df, field = "keywords", sep = ";", split_by = "year")
cooccurrence(df, field = "keywords", sep = ";", group = "year")

# List of transactions with Jaccard similarity
cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")),
             similarity = "jaccard")

# Short alias
co(df, field = "keywords", sep = ";", similarity = "cosine")

# Windowed co-occurrence on a categorical time series. With
# window = 2 only adjacent states co-occur; window = 3 also pairs
# states two positions apart, etc.
seqs <- list(
  c("focus", "focus", "distract", "focus", "confused"),
  c("focus", "distract", "distract", "focus")
)
cooccurrence(seqs, window = 2)

# Weighted long format (e.g. LDA topic-document probabilities)
theta <- data.frame(
  doc   = c("d1","d1","d1","d2","d2","d3","d3"),
  topic = c("T1","T2","T3","T1","T3","T2","T3"),
  prob  = c(0.6, 0.3, 0.1, 0.4, 0.6, 0.5, 0.5)
)
cooccurrence(theta, field = "topic", by = "doc", weight_by = "prob")

# One-hot / indicator table: name the indicator columns, ignore the rest
onehot <- data.frame(
  doc = c("d1", "d2", "d3"),
  A = c(1, 0, 1), B = c(1, 1, 0), C = c(0, 1, 1)
)
cooccurrence(onehot, vars = c("A", "B", "C"))

# Raw event log: sessionized on a 15-minute gap, then windowed

if (requireNamespace("Nestimate", quietly = TRUE)) {
  events <- data.frame(
    student = rep(c("s1", "s2"), each = 3),
    code    = c("read", "write", "read", "test", "write", "read"),
    stamp   = as.POSIXct("2026-01-01 09:00:00") + c(0, 60, 120, 0, 30, 90)
  )
  cooccurrence(events, actor = "student", action = "code", time = "stamp")
}

---

Demo actor-movie-genre table

Description

A small hand-crafted dataset of 30 well-known actors across 10 classic films, with each film carrying its full genre list. Designed for quick exploration in the Shiny app. One row per movie-actor-genre triple, so every pairing of field and by yields a network: actors linked by shared films, genres linked by shared films, or actors linked by shared genres.

Usage

demo

Format

A data frame with 89 rows and 3 variables:

movie

Movie title.

actor

Actor name.

genre

Genre label. Films carry every genre they belong to, so a movie contributes several rows per actor.

Examples

head(demo)
# Actors linked by the films they share
cooccurrence(demo, field = "actor", by = "movie", similarity = "jaccard")

# Genres linked by the films they share
cooccurrence(demo, field = "genre", by = "movie", similarity = "jaccard")

# Actors linked by the genres they share
cooccurrence(demo, field = "actor", by = "genre")

---

Launch the cooccure Shiny explorer

Description

Opens an interactive Shiny application for building and exploring co-occurrence networks. Requires the shiny and DT packages.

Usage

launch_app(...)

Arguments

...	Passed to runApp (e.g. port, launch.browser).

Value

Called for its side effect (launches the app). No return value.

Examples

if (interactive()) {
  launch_app()
}

---

IMDB movie metadata (1970-2024)

Description

A sample of 1,000 highly-rated IMDB movies (rating >= 7.0, >= 1,000 votes) released between 1970 and 2024. The genres column is comma-delimited and suitable for use as the field argument to cooccurrence.

Usage

movies

Format

A data frame with 1,000 rows and 7 variables:

tconst

IMDB title identifier (e.g. "tt0068646").

primaryTitle

Movie title.

startYear

Release year (integer).

genres

Comma-separated genre labels (e.g. "Crime,Drama").

decade

Release decade as a character string (e.g. "1970s").

averageRating

IMDB average user rating.

numVotes

Number of IMDB user votes.

Source

https://developer.imdb.com/non-commercial-datasets/

Examples

head(movies)
cooccurrence(movies, field = "genres", sep = ",", similarity = "jaccard")

---

Plot a cooccurrence network

Description

Plots the co-occurrence matrix as a heatmap, an igraph network, or a base-R degree distribution.

Usage

## S3 method for class 'cooccurrence'
plot(x, type = c("heatmap", "network", "degree"), ...)

Arguments

x	A cooccurrence data frame.
type	Character. "heatmap" (default), "network" (requires igraph), or "degree" for a base-R degree distribution bar plot.
...	Passed to the plotting function.

Value

Invisibly returns x.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
plot(res)

---

Print a cooccurrence edge list

Description

Prints a compact network diagnostic header followed by an edge preview.

Usage

## S3 method for class 'cooccurrence'
print(x, n = 10L, ...)

Arguments

x	A cooccurrence data frame.
n	Integer. Number of rows to show. Default 10.
...	Ignored.

Value

Invisibly returns x.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
print(res)

---

Print a cooccurrence summary

Description

Print a cooccurrence summary

Usage

## S3 method for class 'summary.cooccurrence'
print(x, n = 5L, ...)

Arguments

x	A summary.cooccurrence object.
n	Integer. Number of top nodes/groups to show. Default 5.
...	Ignored.

Value

Invisibly returns x.

---

Summarize a cooccurrence network

Description

Node- and group-level metrics are available as tidy data frames via as.data.frame.

Usage

## S3 method for class 'cooccurrence'
summary(object, ...)

Arguments

object	A cooccurrence data frame.
...	Ignored.

Details

For a network built with split_by, the returned frame stacks one network per group, so a node pair can occur once per group. Pooled simple-graph metrics (density, mean_degree, possible_edges) are undefined in that case and reported as NA; the per-group table carries the correct values.

Value

Invisibly returns a summary.cooccurrence object with network, node, and group-level metrics.

Examples

res <- cooccurrence(list(c("A","B","C"), c("B","C"), c("A","C")))
summary(res)

# Node-level metrics as a tidy data frame
as.data.frame(summary(res))

---