Package 'esaps' reference manual

Title:	Indicators of Electoral Systems and Party Systems
Description:	It allows structuring electoral data of different size and structure to calculate various indicators frequently used in the studies of electoral systems and party systems. Indicators of electoral volatility, electoral disproportionality, party nationalization and the effective number of parties are included.
Authors:	Nicolas Schmidt [aut, cre]
Maintainer:	Nicolas Schmidt <[email protected]>
License:	GPL-2
Version:	0.2.2
Built:	2025-02-18 05:57:12 UTC
Source:	https://github.com/nicolas-schmidt/esaps

Indicators of Electoral Systems and Party Systems

Description

It allows structuring electoral data of different size and structure to calculate various indicators frequently used in the studies of electoral systems and party systems:

Electoral Volatility:
- Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>
- Powell and Tucker (2014) <doi:10.1017/S0007123412000531>
- Lago and Torcal (2019) <doi:10.1177/1354068818795191>
Effective Number of Parties:
- Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>
Electoral disproportionality:
- Rae (1971, ISBN:9780300015171)
- Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>
- Lijphart (1986, ISBN:0875860648, 0875860745)
- Lijphart (1994) <doi:10.1093/acprof:oso/9780198273479.001.0001>
- Cox and Shugart (1991) <doi:10.1016/0261-3794(91)90025-N>
- Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>
Party System Nationalization:
- Mainwaring and Jones (2003) <doi:10.1177/13540688030092002>
- Chhibber and Kollman (2004, ISBN:9781400826377)

Author(s)

Nicolas Schmidt [email protected]

Converts data in table form to tidy_data

Description

Convert data in table format to tidy_data to use in the indicators of the tidy_data.

Usage

convert_esaps(
  path = NULL,
  dataset = NULL,
  file.name = NULL,
  nSheets = 1,
  election.name,
  unit.name,
  M.name = NULL,
  votes_nac.name = NULL,
  seats = FALSE,
  allSheet = FALSE
)
convert_esaps(
  path = NULL,
  dataset = NULL,
  file.name = NULL,
  nSheets = 1,
  election.name,
  unit.name,
  M.name = NULL,
  votes_nac.name = NULL,
  seats = FALSE,
  allSheet = FALSE
)

Arguments

`path`	Character vector containing one or more path names.
`dataset`	Electoral results by party. It can be a `data.frame` or a `list`.
`file.name`	Name of the data file with extension.
`nSheets`	Number of countries (number of sheets).' 'Country' is a generic unit of analysis (province, department, etc.)
`election.name`	Name of the variable that contains elections.
`unit.name`	Name of the variable that contains the unit. 'unit' is a generic unit of analysis (province, department, etc.)
`M.name`	Name of the variable that contains the district magnitude (M+1). It is for the calculation of endogenous and exogenous electoral volatility (Torcal and Lago, 2015).
`votes_nac.name`	la la la
`seats`	By default it is `FALSE`. If it is `TRUE`, it indicates that, in addition to electoral data per party, there is allocation data per seat. In this case, one column must be loaded for each party with the electoral result and another with the number of seats it obtained. The structure must be: party_1, party_2, ..., party_n, seats_party_1, seats_party_2, ..., seats_party_n.
`allSheet`	By default it is `FALSE`. Load all the sheets that are in the files selected in `file.name`. This argument takes precedence over `nSheets`.

Value

data.frame

Author(s)

Nicolas Schmidt [email protected]

Examples

votes <- list(data.frame(country = rep("ARG", 3),
                         year = c(1995, 2000, 2005),
                         party_A = c(40,10,20),
                         party_B = c(35,20,40),
                         party_C = c(25,70,40)),
              data.frame(country = rep("URY", 4),
                         year = c(1995, 2000, 2005, 2010),
                         party_A = c(30,30,20,20),
                         party_B = c(30,50,40, 30),
                         party_C = c(30,10,30, 25),
                         party_D = c(10,10,10,25)),
              data.frame(country = rep("BRA", 2),
                         year = c(1993, 1998),
                         party_A = c(30, 55),
                         party_B = c(70, 45)))

votes <- convert_esaps(dataset = votes, unit.name = "country", election.name = "year")


votes2 <- data.frame(year = c(2000, 2005),
                     country = "URY",
                     votes_party1 = c(20, 30),
                     votes_party2 = c(30, 35),
                     votes_party3 = c(40, 25),
                     votes_party4 = c(10, 10),
                     seats_party1 = c(25, 35),
                     seats_party2 = c(20, 30),
                     seats_party3 = c(40, 30),
                     seats_party4 = c(15, 5))

votes <- convert_esaps(dataset = votes2, unit.name = "country", election.name = "year",
                       seats = TRUE)


## Not run: 
v1 <- convert_esaps(path = getwd(),
                   file.name = c("electionBRA.xlsx", "electionARG.xlsx"),
                   election.name = "elec",
                   unit.name = "district",
                   allSheet = TRUE)

v2 <-convert_esaps(path = getwd(),
                   file.name = c("ARG.ods", "URY.ods", "BRA.ods"),
                   nCountry = c(2, 3, 1),
                   election.name = "elec",
                   unit.name = "province")

v3 <- convert_esaps(path = here::here(),
                   file.name = list.files(pattern = "*.xlsx"),
                   election.name = "year",
                   unit.name = "country",
                   M.name = "magnitude",
                   seats = TRUE,
                   allSheet = TRUE)

## End(Not run)

votes <- list(data.frame(country = rep("ARG", 3),
                         year = c(1995, 2000, 2005),
                         party_A = c(40,10,20),
                         party_B = c(35,20,40),
                         party_C = c(25,70,40)),
              data.frame(country = rep("URY", 4),
                         year = c(1995, 2000, 2005, 2010),
                         party_A = c(30,30,20,20),
                         party_B = c(30,50,40, 30),
                         party_C = c(30,10,30, 25),
                         party_D = c(10,10,10,25)),
              data.frame(country = rep("BRA", 2),
                         year = c(1993, 1998),
                         party_A = c(30, 55),
                         party_B = c(70, 45)))

votes <- convert_esaps(dataset = votes, unit.name = "country", election.name = "year")


votes2 <- data.frame(year = c(2000, 2005),
                     country = "URY",
                     votes_party1 = c(20, 30),
                     votes_party2 = c(30, 35),
                     votes_party3 = c(40, 25),
                     votes_party4 = c(10, 10),
                     seats_party1 = c(25, 35),
                     seats_party2 = c(20, 30),
                     seats_party3 = c(40, 30),
                     seats_party4 = c(15, 5))

votes <- convert_esaps(dataset = votes2, unit.name = "country", election.name = "year",
                       seats = TRUE)


## Not run: 
v1 <- convert_esaps(path = getwd(),
                   file.name = c("electionBRA.xlsx", "electionARG.xlsx"),
                   election.name = "elec",
                   unit.name = "district",
                   allSheet = TRUE)

v2 <-convert_esaps(path = getwd(),
                   file.name = c("ARG.ods", "URY.ods", "BRA.ods"),
                   nCountry = c(2, 3, 1),
                   election.name = "elec",
                   unit.name = "province")

v3 <- convert_esaps(path = here::here(),
                   file.name = list.files(pattern = "*.xlsx"),
                   election.name = "year",
                   unit.name = "country",
                   M.name = "magnitude",
                   seats = TRUE,
                   allSheet = TRUE)

## End(Not run)

Electoral Disproportionality

Description

Electoral Disproportionality: Rae (1971), Loosemore and Hanby (1971), Lijphart (1986), Lijphart (1994), Gallagher (1991) and Cox and Shugart (1991).

Usage

dispro(tidy_data, method, scale = 1)
dispro(tidy_data, method, scale = 1)

Arguments

tidy_data

data.frame that contains the following variables with these names:

election: year of election.
unit: the unit of analysis (province, department ...)
party: name of the political parties that obtain votes.
votes: votes obtained by each party.
seats: .

If the data is not structured in this way you can order it with: convert_esaps.

method

Method to calculate electoral volatility:

method = "Rae" or method = 1.
method = "Loosemore and Hanby" or method = 2.
method = "Lijphart_1" or method = 3.
method = "Lijphart_2" or method = 4.
method = "Gallagher" or method = 5.
method = "Cox and Shugart" or method = 6.

scale

By default it is 100, the indices will vary between 0 and 100. If scale = 1 the variation will be between 0 and 1.

Value

data.frame.

Author(s)

Nicolas Schmidt [email protected]

Examples

votes <- data.frame(election = rep(c(2000, 2005), each = 4),
                   unit  = rep(c("ARG", "URY"), each = 4),
                   party = c("party_A", "party_B","party_C","party_D"),
                   votes = c(20, 30, 40, 10, 30, 35, 25, 10),
                   seats = c(25, 20, 40, 15, 35, 30, 30, 5)
                   )

dispro(votes, 1:6, 1)
dispro(votes, 3)
dispro(tidy_data = votes, method = 3:5)
dispro(tidy_data = votes, method = c(1,3,6))
dispro(tidy_data = votes, method = c("Rae", "Gallagher"))


votes <- data.frame(election = rep(c(2000, 2005), each = 4),
                   unit  = rep(c("ARG", "URY"), each = 4),
                   party = c("party_A", "party_B","party_C","party_D"),
                   votes = c(20, 30, 40, 10, 30, 35, 25, 10),
                   seats = c(25, 20, 40, 15, 35, 30, 30, 5)
                   )

dispro(votes, 1:6, 1)
dispro(votes, 3)
dispro(tidy_data = votes, method = 3:5)
dispro(tidy_data = votes, method = c(1,3,6))
dispro(tidy_data = votes, method = c("Rae", "Gallagher"))

The Effective Number of Parties (ENP) is an index developed by Laakso and Taagepera (1979) that allows to count the relevant parties in a party system. The formula consists on dividing one over the sum of the squares of the proportions (votes or seats) that the parties obtain in an electoral instance.

Usage

enp(tidy_data, enp_seats = FALSE, summary = FALSE)
enp(tidy_data, enp_seats = FALSE, summary = FALSE)

Arguments

tidy_data

data.frame that contains the following variables with these names:

election: year of election.
unit: the unit of analysis (province, department ...)
party: name of the political parties that obtain votes.
votes: votes obtained by each party.
seats: .

If the data is not structured in this way you can order it with: convert_esaps.

enp_seats

enp_seats = TRUE allows us to calculate jointly the effective number of electoral parties and in the congress.

summary

Summary of the data by unit, by default it is FALSE.

Value

if summary = FALSE, return data.frame.

if summary = TRUE, return a list with two data.frame.

list[[1]] Indicator
list[[2]] Summary
- min variable 'election'
- max variable 'election'
- number of elections
- mean indicator

Author(s)

Nicolas Schmidt [email protected]

Examples

votes <- data.frame(election = rep(c(2000, 2005), each = 4),
                   unit  = rep(c("ARG", "URY"), each = 4),
                   party = c("party_A", "party_B","party_C","party_D"),
                   votes = c(20, 20, 50, 10, 30, 35, 25, 10),
                   seats = c(25, 25, 40, 10, 30, 30, 30, 10)
                   )

enp(votes)
enp(votes, enp_seats = TRUE)
enp(votes, summary = TRUE)

votes <- data.frame(election = rep(c(2000, 2005), each = 4),
                   unit  = rep(c("ARG", "URY"), each = 4),
                   party = c("party_A", "party_B","party_C","party_D"),
                   votes = c(20, 20, 50, 10, 30, 35, 25, 10),
                   seats = c(25, 25, 40, 10, 30, 30, 30, 10)
                   )

enp(votes)
enp(votes, enp_seats = TRUE)
enp(votes, summary = TRUE)

Electoral volatility calculation

Description

Electoral volatility calculation: Pedersen (1979), Powell and Tucker (2014) and Torcal and Lago (2015).

Usage

evolat(
  tidy_data,
  method,
  threshold = 2,
  summary = FALSE,
  digits = 2,
  scale = 100
)
evolat(
  tidy_data,
  method,
  threshold = 2,
  summary = FALSE,
  digits = 2,
  scale = 100
)

Arguments

`tidy_data`	data.frame that contains the following variables with these names: `election`: year of election. `unit`: the unit of analysis (province, department ...) `party`: name of the political parties that obtain votes. `votes`: votes obtained by each party. `M`: magnitude of the district. Only if the `method = 3`. It refers to the M + 1 rule (only 'M' must be loaded). If the data is not structured in this way you can order it with: `convert_esaps`.
`method`	Method to calculate electoral volatility: `method = "Pedersen"` or `method = 1`. `method = "Powell and Tucker"` or `method = 2`. `method = "Torcal and Lago"` or `method = 3`.
`threshold`	Minimum threshold for 'Type A' electoral volatility calculation (Powell and Tucker, 2014). By default is 2%.
`summary`	Summary of data by unit, by default it is `FALSE`.
`digits`	integer indicating the number of decimal places to be used.
`scale`	By default it is `100`, the indices will vary between 0 and 100. If `scale = 1` the variation will be between 0 and 1.

Value

if summary = FALSE, return data.frame.

if summary = TRUE, return a list with two data.frame.

list[[1]] Indicator
list[[2]] Summary by 'unit'
- min variable 'election'
- max variable 'election'
- number of elections
- mean indicator
- standard deviation indicator

Author(s)

Nicolas Schmidt [email protected]

Examples


votes <- data.frame(election = rep(c(1995, 2000, 2005, 2010),4),
                    unit = "ARG",
                    party = rep(c("party_A","party_B","party_C","party_D"), each = 4),
                    votes = c(30,30,20,20,30,50,40,30,30,10,30,25,10,10,10,25))
evolat(votes, 1)
evolat(tidy_data = votes, method = 1, summary = TRUE)

votes <- data.frame(election = rep(c(1995, 2000, 2005, 2010),4),
                    unit = "ARG",
                    party = rep(c("party_A","party_B","party_C","party_D"), each = 4),
                    votes = c(30,30,20,20,30,50,40,30,30,10,30,25,10,10,10,25))
evolat(votes, 1)
evolat(tidy_data = votes, method = 1, summary = TRUE)

Party System Nationalization Score

Description

Party System Nationalization Score Mainwaring and Jones (2003) and Chhibber and Kollman (2004)

Usage

psns(tidy_data, method, pns = FALSE, scale = 100)
psns(tidy_data, method, pns = FALSE, scale = 100)

Arguments

`tidy_data`	data.frame that contains the following variables with these names: `election`: year of election. `unit`: the unit of analysis (province, department ...) `party`: name of the political parties that obtain votes. `votes`: votes obtained by each party. `votes_nac`: votes at national level for each party. If the data is not structured in this way you can order it with: `convert_esaps`.
`method`	Method to calculate Party System Nationalization Score: `method = "Mainwaring and Jones"` or `method = 1`. `method = "Chhibber and Kollman"` or `method = 2`.
`pns`	by default it is `FALSE`. If `TRUE`, the Party Nationalization Score is calculated. In method, you must indicate: `method = 1`.
`scale`	By default it is `100`, the indices will vary between 0 and 100. If `scale = 1` the variation will be between 0 and 1.

Value

if pns = FALSE, return data.frame.

if pns = TRUE, return a list with two data.frame.

list[[1]] PSNS: Party System Nationalization Score
list[[2]] PNS: Party Nationalization Score

Author(s)

Nicolas Schmidt [email protected]

Examples

votes <- data.frame(election = rep(2000,4),
                    unit  = rep(c("District_1", "District_2"), each = 2),
                    party = rep(c("party_A", "party_B"), 2),
                    votes = c(0.60,0.40, 0.30, 0.70),
                    votes_nac = rep(c(0.55,0.45),2)
                   )
psns(tidy_data = votes, method = 1)
psns(tidy_data = votes, method = 1, pns = TRUE)

votes <- data.frame(election = rep(2000,4),
                    unit  = rep(c("District_1", "District_2"), each = 2),
                    party = rep(c("party_A", "party_B"), 2),
                    votes = c(0.60,0.40, 0.30, 0.70),
                    votes_nac = rep(c(0.55,0.45),2)
                   )
psns(tidy_data = votes, method = 1)
psns(tidy_data = votes, method = 1, pns = TRUE)

Package 'esaps'

Help Index

Indicators of Electoral Systems and Party Systems

Description

Author(s)

See Also

Converts data in table form to tidy_data

Description

Usage

Arguments

Value

Author(s)

Examples

Electoral Disproportionality

Description

Usage

Arguments

Value

Author(s)

Examples

Effective Number of Parties

Description

Usage

Arguments

Value

Author(s)

Examples

Electoral volatility calculation

Description

Usage

Arguments

Value

Author(s)

Examples

Party System Nationalization Score

Description

Usage

Arguments

Value

Author(s)

Examples