Title:	Local Political Actor Network Diachronic Analysis Tools
Version:	0.1.1
Description:	Provides functions to prepare, visualize, and analyse diachronic network data on local political actors, with a particular focus on the development of local party systems and identification of actor groups. Formalizes and automates a continuity diagram method that has been previously applied in research on Czech local politics, e.g. Bubenicek and Kubalek (2010, ISSN:1803-8220), Kubalek and Bubenicek (2012, ISSN:1803-8220), and Cmejrek, Bubenicek, and Copik (2010, ISBN:978-80-247-3061-5).
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	dplyr, igraph (≥ 2.1.0), magrittr, RColorBrewer, scales
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
URL:	https://github.com/localpolitics/lpanda
BugReports:	https://github.com/localpolitics/lpanda/issues
Depends:	R (≥ 3.5)
LazyData:	true
NeedsCompilation:	no
Packaged:	2025-08-25 23:28:13 UTC; vasco
Author:	Vaclav Bubenicek [aut, cre, cph]
Maintainer:	Vaclav Bubenicek <bubenicek@pef.czu.cz>
Repository:	CRAN
Date/Publication:	2025-09-01 09:10:07 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Municipal Election Data: Doubice (DC, CZ)

Description

A dataset containing individual-level candidacy records from municipal elections in the municipality of Doubice (district Decin, Czech republic).

Usage

Doubice_DC_cz

Format

An object of class data.frame with 151 rows and 14 columns.

Details

Description of variables

Each record describes one candidate’s run for office, including their candidate list affiliation, position on the list, nominating party, political affiliation, number of preferential votes, and whether they were elected or held specific positions (mayor, deputy mayor, member of the executive body).

The dataset also includes contextual election-level information, such as the number of eligible voters and ballots cast, which can be used to calculate voter turnout and related indicators.

Source

Czech Statistical Office, Municipality website, Acta Politologica article

Municipal Election Data: Jilove (DC, CZ)

Description

A dataset containing individual-level candidacy records from municipal elections in the municipality of Jilove (district Decin, Czech republic).

Usage

Jilove_DC_cz

Format

An object of class data.frame with 745 rows and 14 columns.

Details

Description of variables

Each record describes one candidate’s run for office, including their candidate list affiliation, position on the list, nominating party, political affiliation, number of preferential votes, and whether they were elected or held specific positions (mayor, deputy mayor, member of the executive body).

The dataset also includes contextual election-level information, such as the number of eligible voters and ballots cast, which can be used to calculate voter turnout and related indicators.

Source

Czech Statistical Office, Municipality website, Student thesis

Visualization of Candidacy Continuity Diagram

Description

Visualizes the continuity of candidacies over time, illustrating the evolution of the local party system through a network of candidate lists linked by candidate transitions across elections.

Usage

plot_continuity(
  netdata,
  mark = NULL,
  separate_groups = FALSE,
  lists = c("all", "elected"),
  elections = NULL,
  show_elections_between = TRUE,
  parties = NULL,
  links = c("continuity", "all"),
  order_lists_by = c("votes", "seats"),
  order_groups_by = c("elections", "votes", "seats"),
  personalization = FALSE,
  coloured = TRUE,
  group_colours = c(),
  show_legend = TRUE,
  plot_title = NULL,
  ...
)

Arguments

Details

Recommendation about using the raw data

For more advanced use, especially when identifying political parties or analyzing system stability, it is recommended to first process the election data using prepare_network_data. This function builds the necessary network structures and attributes also for groups of candidate lists that sometimes takes few minutes but you would need to do it only once. Using raw data frames as input in case of plot_continuity is intended mainly for quick and basic visualizations, without the group identification.

Usage of mark argument

A central feature of this function is the mark argument, which allows highlighting of specific groups in the diagram. The most common options are "parties" or "cores", referring to communities of candidate lists detected through community detection.

When using mark = "parties" or "cores", you can further specify which groups to highlight visually by adding their IDs (e.g., mark = c("parties", 2, 5)). Party and core IDs are available in netdata$parties$node_attr$vertices or netdata$cores$node_attr$vertices.

You can also highlight individual candidates by using mark = c("candidate", "Candidate Name"), which will highlight the candidate lists on which the person has appeared in colours of the candidate lists' groups.

You may combine the mark argument with group separation, and filtering.

Groups separation

The separate_groups argument improves diagram readability by placing each group on its own line. This is particularly helpful when analyzing continuity, volatility, and structural reproduction of the party system.

Elections filtering

Filtering elections using the elections argument is useful when dealing with many elections that may not fit into a single figure in a report or publication. In such cases, you can split the diagram into two parts (e.g., one with elections = "-2002" and one with elections = "2002-", so that the links between the elections adjacent to the 2002 elections are not lost) and stack them vertically.

When selecting non-consecutive elections, it is strongly recommended to set links = "all" to retain meaningful connections between candidate lists across time. Otherwise, continuity may appear broken due to missing intermediate elections.

For a meaningful continuity analysis, include at least two elections.

About order_groups_by argument

The order_groups_by argument is relevant only when separate_groups = TRUE. You can sort groups by "elections", "votes", "seats", or "none" (the original order in the data). If multiple criteria are provided (e.g., c("votes", "elections")), they are applied in priority order. The criteria of "votes" and "seats" will sort the groups according to the value of the given criterion. The "elections" criterion ranks groups based on their participation in the most recent election and falls back recursively to earlier ones in case of ties.

About personalization argument

The personalization option appends the coefficient of variation of preferential votes to the name of each candidate list. A lower value may indicate a party's electoral program voting, while higher variability may suggest a personalized choice (for example, where voters support a prominent individual rather than the whole candidate list). In the case of a limited number of preferential votes, such an interpretation may be debatable and should therefore be used with caution.

Value

NULL, invisibly. Called for its side effect: plotting the continuity diagram.

Note

The mark = "cores" option is currently experimental, as the conversion of their definition into code is still being sought, and may be subject to change in future versions. Use with caution.

Examples

data(sample_data, package = "lpanda")

# basic continuity diagram
plot_continuity(sample_data)

# preparing network data
netdata <- prepare_network_data(sample_data, verbose = FALSE, quick = TRUE)

# highlighting groups
plot_continuity(netdata, mark = "parties")
plot_continuity(netdata, mark = c("parties", 3), order_lists = "seats")
plot_continuity(netdata, mark = "parties", separate_groups = TRUE, show_legend = FALSE)

# candidate tracking
plot_continuity(netdata, mark = c("candidate", "c03"))

# filtering elections and parties
plot_continuity(netdata, mark = "parties", elections = "18-")
plot_continuity(netdata, elections = c(14, 22), links = "all", show_elections_between = FALSE)
plot_continuity(netdata, parties = 1)

Prepare Network Data for LPANDA

Description

Transforms time series data of local election results into a set of network data for use in Local Political Actor Network Diachronic Analysis (LPANDA). The function constructs a bipartite network (candidate – candidate list), its projected one-mode networks (candidate – candidate and list – list), a continuity graph (linking candidate lists between adjacent elections), and an elections network (its node attributes can serve as electoral statistics). It also detects parties (as clusters of candidate lists based on community detection applied to the bipartite network) and constructs their network.

Usage

prepare_network_data(df, input_variable_map = list(), verbose = TRUE, ...)

Arguments

Value

A list of network data objects for diachronic analysis using LPANDA or other social network analysis tools. Each component contains edgelist (data.frame of edges) and node_attr (data.frame of node attributes). The exact set of columns depends on the input and may evolve. See Output data structure for a description of the returned object.

Expected structure of input data

The input data frame (df) must include at least the election identifiers (year[.month]), candidates' names (uniquely identifying individuals), and list names. Other variables are optional. If variable names in the dataset differ from the expected ones, they should be specified in the input_variable_map as a named list (only differing names need to be listed).

Just in case - a named list is a list where each element has a name (the expected variable name) and a value (the actual name used in your data frame), for example: list(list_name = "party", elected = "seat", list_votes = "votes_total").

Examples of expected and acceptable values in df:

• elections (required): Election identifier in the format YY[YY][.MM]: e.g., 94 | 02 | 1998 | "2024" | 2022.11

• candidate (required): e.g., "John Doe" | "John Smith (5)" | "Jane Doe, jr."

• list_name (required): for independent candidates, you can use: e.g., "John Smith, Independent Candidate" | "J.S., IND."

• list_pos, pref_votes, list_votes: must be numeric

• elected, mayor, dep_mayor, board, gov_support: 1 | "0" | T | "F" | "TRUE" | FALSE (non‑logical inputs will be coerced to logical).

• nom_party: for independent candidates, you can use: "IND" | "Independent Candidate"

• pol_affil: for independent candidates, you can use: "non-partisan"

• elig_voters, ballots_cast, const_size: A numeric that should appear only once in any candidate row within a given election and constituency

If pref_votes are present but list_votes are not, the function assumes a voting system where list votes are calculated by summing the preferential votes of candidates on the list.

If const_size is missing, it will be estimated based on the number of elected candidates (if available).

For the purposes of analysis, a new variable list_id (class character) is added to the internally processed copy of df and carried to the output. It uniquely identifies each candidate list in a given election (combining list_name and elections), e.g., Besti Flokkurinn (2010), SNP (2019), or "John Smith (5), IND. (2022.11)". This variable serves as a key identifier in LPANDA for tracking candidate lists across elections and constructing network relations.

Output data structure

The returned object is a named list with up to seven network objects:

• bipartite: bipartite network (candidates-lists).

• candidates: projected candidate–candidate network.

• lists: projected list–list network (directed by election order).

• continuity: filtered version of lists network (edges of adjacent elections only).

• parties: network of detected party clusters (via community detection applied on bipartite network).

• (cores): higher-level clusters of parties. Cores are currently experimental and will not appear in the standard output network data. See Note below.

• elections: inter-election candidate flow and election-level stats

Each object is a list with two components:

• edgelist: a data.frame representing network edges

• node_attr: a data.frame with attributes for each node

For example, ...$candidates$edgelist contains edges between individuals who appeared on the same candidate list, and ...$elections$node_attr includes several election statistics (e.g., number of candidates, distributed seats, plurality index, voter turnout for each election, etc.).

Note

Cores are currently experimental, as the conversion of their definition into code is still being sought, and may be subject to change in future versions. It is not yet intended for standard use in analyses and academic studies, since their calculation is not yet comprehensive, so the cores' network structure will not appear in the standard output network data unless explicitly called with the include_cores = TRUE parameter. Use with caution, their interpretation is highly questionable.

Examples

data(sample_diff_varnames, package = "lpanda")
df <- sample_diff_varnames
str(df) # different variable names: "party" and "seat"
input_variable_map <- list(list_name = "party", elected = "seat")

netdata <- prepare_network_data(df, input_variable_map, verbose = FALSE)
str(netdata, vec.len = 1)

Sample Dataset with Binary Values

Description

A variant of sample_data containing binary values instead of TRUE/FALSE values.

Usage

sample_binary_values

Format

A data frame with 18 rows and 5 variables (same structure as sample_data).

Source

Fictitious data

Simple Sample Dataset

Description

Basic fictitious dataset simulating election results.

Usage

sample_data

Format

A data frame with 18 rows and 5 variables:

elections

Election identifier (numeric)

candidate

Candidate identifier (character)

list_name

Candidate list name (character)

elected

Logical; TRUE if the candidate was elected

mayor

Logical; TRUE if the candidate became mayor

Source

Fictitious data

Sample Dataset with Some Different Variable Names

Description

A variant of sample_data with different names for variables list_name (party) and elected (seat), useful for testing robustness of input handling.

Usage

sample_diff_varnames

Format

A data frame with 18 rows and 5 variables (same structure as sample_data).

Source

Fictitious data

Sample Dataset Without Continuity Between Elections

Description

A variant of sample_data in which no candidate appears in more than one election. This breaks the continuity between elections, making it useful for testing whether network-building functions correctly handle datasets with no longitudinal links between candidate lists.

Usage

sample_no_continuity

Format

A data frame with 15 rows and 5 variables (same structure as sample_data).

Source

Fictitious data

Sample Dataset Without Candidate Switching

Description

A variant of sample_data in which candidates may run in multiple elections, but always remain within the same political group (they do not switch between candidate list clusters). This is useful for testing continuity logic and verifying that no cross-group transitions occur.

Usage

sample_no_crossing

Format

A data frame with 15 rows and 5 variables (same structure as sample_data).

Source

Fictitious data