Package 'Relectoral'

Description

This function serves to download the data in Excel format at the municipal level and creates a data.frame in your R environment with that information. Note: The fields returned are all of character type, so if you need to perform operations with them, you will have to convert the necessary fields to numeric.

Usage

Agregado_Mun_MIR(Ano, Mes, Tipo, Ruta, Borrar = TRUE)

Arguments

Value

Object of type 'tbl_df' with the data at municipal level.

Examples

c<-Agregado_Mun_MIR(1989,"06",Tipo = "Europeas","F:/")

Description

This function is used to download the data in Excel format at the provincial level and upload a data.frame with that information into R. Note: The returned fields are all of character format, so if you need to perform operations, you will have to convert the required fields to numeric.

Usage

Agregado_Prov_MIR(Ano, Mes, Tipo, Ruta, Borrar = TRUE)

Arguments

Value

Object of type 'tbl_df' with the data at provincial level.

Examples

c<-Agregado_Prov_MIR(2019,"05",Tipo = "Europeas","F:/")

Description

With this function, the following indicators of electoral disproportionality can be obtained for aggregated data:

• Sainte Lagüe (SL)

• RAE (R)

• RAE Corrected (Rco)

• Loosemore & Hanby (LH)

• Gallagher's Minimum Squares index (Gcm)

• Maximum deviation index (Lmax)

• Cox & Shugart index (Cs)

• Corrected Cox & Shugart index (CS_correg)

• Tukey's line (LT)

All the values of these indicators are in an output data.frame where the names of the columns coincide with the identification of these indicators which appear between brackets above.

Likewise the following voting size indicators are also obtained:

• Electoral and Parliamentary fragmentation (F)

• Effective number of political parties (N)

• Hyperfractionality index (Hiper)

• Molinar's effective number of political parties (NP)

• Concentration of vote (Con)

• Competitiveness between parties (Comp)

The electoral and parliamentary versions are calculated for these voting size indicators. Their values are stored in the output of this function within a data.frame with the name of the columns indicated with the initials shown above in parentheses and the suffix '_electoral' is added to indicate that it is the electoral version or the suffix '_parliamentary' to indicate that it is the parliamentary version.

Important note. With this function you can get the results automatically or manually. With the automatic form, the data are downloaded from the Spanish Ministry of Home Office. With the manual form, you have to provide the data using a data.frame with the structure that will be indicated later.

Usage

AgregadosIndi(Ano = 0, Mes = "", RutaDescarga = "", Auto = TRUE, datos = "")

Arguments

Value

Returns a data.frame with four elements:

1.- Its name is 'dat' and it is a data.frame containing the votes and seats of each candidate party, in absolute terms and in percentages, also the accumulated distribution of these values and a last column containing an indicator of electoral disproportionality that is calculated as the difference between the percentage of votes and the percentage of seats obtained.

2.- Its name is 'grafico' and it is a ggpplot object where the difference between the percentage of votes and seats is represented in a bar chart.

3.- Its name is 'In_despro' and it is a data.fame containing the values of the disproportionality indices indicated above.

4.- Its name is 'In_dimen' and it is a dataframe with the values of the voting dimension indicators indicated above. Both the electoral and parliamentary versions are provided.

Examples

d<-AgregadosIndi(2019,"04",RutaDescarga = "F:/")

Description

With this graph two parliamentary arcs will be obtained with the purpose of being able to compare the structure of them, that is to say, the results obtained in two different electoral processes.

Usage

Arc_Comparacion(
  Partidos1,
  Partidos2,
  Escanos1,
  Escanos2,
  cols = NULL,
  repr = c("absolute", "proportion"),
  titu1 = "Election1",
  titu2 = "Election2",
  titulo = "Poner un titulo"
)

Arguments

Value

A ggplot object is obtained containing two graphs representing the two parliamentary arcs to be compared.

Examples

bt1 <- data.frame(parties = c("PP", "CSU", "SPD","IU"),
                 seats   = c(200, 46, 153,58),
                 cols    = c("black", "blue", "red","jjj"),
                 stringsAsFactors = FALSE)

bt2 <- data.frame(parties = c("PP", "CSU","UP1"),
                 seats   = c(100, 146,200),
                 cols    = c("green", "blue","GGG"),
                 stringsAsFactors = FALSE)

Arc_Comparacion(bt1$parties,bt2$parties, bt1$seats,
               bt2$seats, cols=c("PP"="blue", "CSU"="red","SPD"="green",
                                 "IU"="pink","UP1"="antiquewhite" ),
               titu1 = "Abril-2019",titu2 = "Noviembre-2019",
               titu = " Comparación de elecciones")

Description

This function facilitates the representation of the distribution of seats obtained by each of the parties that have some parliamentary representation. It can be introduced a title for the graph, the colors to be used for each party, as well as to choose whether to make a distribution of seats in absolute or relative terms.

Usage

Arc_Parlamentario(
  Partidos,
  Escanos,
  cols = NULL,
  repr = c("absolute", "proportion"),
  titulo = "Enter a title here"
)

Arguments

Value

Returns a ggplot object, containing the representation of the obtained parliamentary arc.

Examples

bt <- data.frame(parties = c("PP", "CSU", "SPD", "AfD", "FDP", "UPyD", "CC", "Verdes"),
seats   = c(200, 46, 153, 92, 80, 69, 67, 2),
cols    = c("black", "blue", "red", "lightblue", "yellow", "purple", "lavenderblush", "grey"),
stringsAsFactors = FALSE)

Arc_Parlamentario(bt$parties, bt$seats, cols = bt$cols)

Description

BAZI is a freely available Java program that implements various methods of distributing seats for proportional representation systems. It offers the possibility of using divisor methods as well as quota methods. The program can be downloaded from https://www.math.uni-augsburg.de/htdocs/emeriti/pukelsheim/bazi/ Instructions on its use can be found at the following webpage: https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/601. With this function you obtain flat ASCII files, with extension '.bazi' that structures the data in the form that can be understood by BAZI program and obtain the results that can be accessed with this application. This function has been designed so that it can be used both automatically or manually, that is to say, it will work autonomously if after giving the corresponding parameters it connects to the databases of the Spanish Ministry of the Interior, downloads the results and generates the file with extension ".bazi". The manual procedure, is designed to be provided with the appropriate data and that this function is responsible for transforming them to the format that BAZI understands to generate the results for which it is programmed. Let's see below how to proceed in each case.

First of all, it should be pointed out that the "camino" parameter must be provided both with the manual and the automatic procedure, and it should indicate the path to be used to store the file with the ".bazi" extension that is generated as a result of this process. It must also be taken into account that a path with writing permission for R must be provided.

The manual or automatic procedure is indicated by the "Auto" parameter of the Bazi() function, so that if "TRUE" (default value) is valid, the automatic procedure will be executed and otherwise the manual one.

Usage

Bazi(
  Ano = 0,
  Mes,
  camino = "",
  cota = 3,
  Auto = TRUE,
  votes,
  seats,
  Titulo = ""
)

Arguments

Value

The result is a flat ASCII file with its own information structure to be read by BAZI Java program and which has the following generic name 'Congreso_AAAA_MM.bazi', where YYYY is the year of the electoral process and MM is the two-digit month. In the case of using a manual procedure the file will have the name 'Congreso.bazi'.

Examples

Bazi(Ano = 2019,"04","F:/")

# Manual example

vo <- data.frame(
 circu=c("c1","c2","c3","c4","c5","c6","c7","c8","c9"),
 p1=c(200,300,0,0,250,360,145,0,0),
 p2=c(0,0,450,467,576,346,234,0,437),
 p3=c(243,567,0,0,345,634,456,634,0),
 p4=c(0,367,384,134,267,0,0,364,146),
 p5=c(345,123,234,254,123,543,342,45,0),
 p6=c(23,45,234,0,0,354,254,56,123)
)

se <- data.frame(
 circu=c("c1","c2","c3","c4","c5","c6","c7","c8","c9"),
 dip=c(3,4,5,3,2,6,2,4,3)
)

Bazi(camino="F:/",Auto=FALSE, votes=vo,seats=se, Titulo="Fichero_prueba")

Description

The standardised party nationalisation index developed by Bochsler (2010) can be calculated using an excel spreadsheet, which can be downloaded from the following website: https://www.bochsler.eu/pns/. This Excel file contains a macro to calculate the match nationalisation index, which is widely used nowadays. The aim of this function is to create an Excel sheet that is directly loaded with the data provided by the Spanish Ministry of the Interior (MIR), http://www.infoelectoral.mir.es/. This data is arranged in such a way that it is only necessary to copy and paste it into the macro created by Bochsler. This makes it much easier to load the macro function with data. In the Excel sheet that is created, the last row contains the totals that are the ones to be placed in row 8 of Daniel Bochsler's macro function.

Usage

Bochsler(Ano, Mes, RutaDescarga, RutaSalida)

Arguments

Value

With this function you obtain an Excel sheet called 'Bochsler.xlsx' placed in the route that has been indicated with the parameter 'RutaSalida'.

Utilities

NA

Examples

Bochsler(2019,"04","F:/","F:/")
Bochsler(2016,"06","F:/","F:/")

Description

This index will measure the level of electoral competition that exists in a given party system between the first and second party that have obtained more votes in an specific election. This indicator will therefore show how much rivalry there is between the two parties that have received the most votes. To measure this phenomenon, the proximity or remoteness of the results of these two political formations are used. This indicator is calculated using the following formula:

$competitiveness=1-(p_1-p_2)$

Where p1 and p2 are the percentages of votes (or seats), as parts per unit, of the two most voted political formations. A value close to 1 (p1=p2) indicates strong competitiveness, while a value of zero (p1=1 p2=0) will indicate there is no competitiveness.

Usage

competitividad(votes, seats)

Arguments

Value

Returns a dataframe, with the columns "electoral" and "parliamentary" to store the value of the index of the competitiveness index in the electoral or the parliamentary way respectively.

Examples

competitividad(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

Another very important indicator of the party system is the concentration of votes, which indicates what percentage of votes or seats are taken by the two parties that have the largest number of votes. The formula used to calculate this indicator is as follows:

$concentration=p_1+p_2$

The maximum value that this indicator takes is 1, so the closer it is to 1, the more concentrated the vote is.

Usage

concentracion(votes, seats)

Arguments

Value

Returns a dataframe, with the columns "electoral" and "parliamentary" to store the value of the index of the concentration of votes in the electoral or the parliamentary way respectively.

Examples

concentracion(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

Cox_Shugart's (1991) disproportionality index measures the disproportion in the distribution of seats by means of a regression line between the percentage of seats and the percentage of votes. If the distribution were approximately proportional, the slope of the regression line would be one. If it moves away from that value there will be disproportionality in the distribution of seats.

Usage

Cox_Shugart(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

Cox_Shugart(c(3947,3189,1971,466,345,82),c(184,99,44,10,0,0))

Description

This index tries to rectify the defect in the Cox_Shugart index, since the latter is very sensitive to the presence of small political parties. To correct this deficiency, this indicator is calculated by not taking into account parties that do not obtain parliamentary representation. In fact, when its value is calculated the only thing that is done is to eliminate the votes for parties that do not have obtained any seats and those results are passed to the "Cox_Shugart()" function.

Usage

Cox_Shugart_correg(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

Cox_Shugart_correg(c(3947,3189,1971,466,345,82),c(184,99,44,10,0,0))

Description

This function serves to download the data in Excel format at polling station level and then uploads a data.frame with that information into R. Note: The returned fields are all of character format, so if you need to perform numeric operations, you will have to convert the required fields to numeric.

Usage

Desagregados_Mesa_Mir(Ano, Mes, Tipo, Ruta, Borrar = TRUE)

Arguments

Value

Object of type 'tbl_df' with the data at polling station level.

Examples

c2<-Desagregados_Mesa_Mir(2019,"04",Tipo = "Congreso", Ruta = "F:/",Borrar = TRUE)

Description

With this function, the same results will be obtained as with the 'AgregadosIndi()' function, but for each of the regions listed in the data provided. Specifically, when downloading data from the Spanish Ministry of Home Office, the indicated data will be obtained both for each Autonomous Community and for each province. The format of the output resulting from this function is indicated below.

IMPORTANT NOTE: Again, data can be obtained automatically or provided manually.

Usage

DesAgregadosIndi(
  Ano = 0,
  Mes = "",
  RutaDescarga = "",
  Auto = TRUE,
  datos_v = "",
  datos_d = ""
)

Arguments

Value

The output consists of a list that contains two lists:

1. The fisrt list contains the layer of Autonomous Communities or geographical grouping units. There is a list for each Autonomous Community or grouping unit. The order of this list corresponds to the order in which the Autonomous Communities or grouping units appear in the input data.frame. After selecting an Autonomous Community or grouping unit, another list is obtained with the same four elements that contained the output of the function 'AgregadosIndi()' but for that specific geographical grouping unit.

2. The second list contains the layer of provinces or disaggregated electoral districts. In this case, a list is also obtained for each province or disaggregated unit, with the identifier equal to that shown for the province or disaggregated unit in the input files. Finally, for each province, a list is obtained with the four elements that were available with the output of the function 'AgergadosIndi()'.

Examples

d2<-DesAgregadosIndi(2019,"04",RutaDescarga = "F:/")

Description

This indicator seeks to summarize in one value the level of dispersion or concentration of political power, that is to say, whether or not the seats or votes received are concentrated in a reduced number of political parties. The values of the index vary between 0 and 1, so that a value of zero indicates no fragmentation (all the votes go to a single party), while a value close to one indicates strong electoral fragmentation

Este indicador pretende resumir en un valor el nivel de dispersión o concentración del poder político, es decir y de forma resumida si se encuentra concentrado o no los escaños o votos recibidos en una serie de partidos polítcos. Los valores del índice varían entre 0 y 1, de forma que un valor cero indica no hay ninguna fragmentación ( todos los votos van a un sólo partido), mientras que un valor dercano a uno indica fuerte fragmentación electoral

The formula used for its calculation is as follows:

$1-\sum_{i}q_{i}^{2}$

for $i = 1, 2, \ldots,n$

Where q_i are the proportions (as parts per unit) of the number of seats or votes (electoral or parliamentary indicator), of the votes obtained by each candidate.

Usage

fragmentacion_rae(votes, seats)

Arguments

Value

Returns a dataframe, with the columns "electoral" and "parliamentary" to store respectively the value of the electoral or parliamentary fragmentation index.

Examples

fragmentacion_rae(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

The Gallagher's electoral disproportionality index of minimum squares uses a somewhat more elaborate formula than the previous indices (R, LH), for which it calculates the differences squared between the votes and seats, adds them up and divides the result by two and then calculates its square root. It therefore gives appropriate weight to the distortions of disproportionality. Its mathematical formula is as follows:

$Gcm=\sqrt{\frac{\sum(V_{i}-E_{i})^{2}}{2}}$

for $i = 1, 2, \ldots,n$

Usage

Gallagher(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

Rae(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))
Gallagher(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

With this function you can obtain the ID codes of the Autonomous Communities of Spain used by the Ministry of Home Affairs to code their data. This codification can also be seen in this link. It should be noted that this coding system does not fully coincide with the Autonomous Community codes of Spanish National Institute of Statistics. This function does not take any parameters.

Usage

get_CCAA(...)

Arguments

Value

Return a data.frame with two columns. The first column contains the ID code of the Autonomous Community and the second field contains the name of that region.

Examples

get_CCAA()

Description

With this function, the name of all the Spanish provinces is obtained, along with their ID codes, which are the same as those provided by the Spanish National Institute of Statistics on this website. This function does not take any parameters.

Usage

get_Provincias(...)

Arguments

Value

It returns a data.frame with two columns. The first column contains the ID code of the province and the second column contains the name of the province.

Examples

get_Provincias()

Description

The hyperfractionality index, proposed by Kesselman (1996) and Wilden (1991), is another indicator used to measure the number of parties that are relevant in any type of election. This index is very sensitive to the presence of small parties since it gives them more relevance than they really have. The mathematical formula used to calculate the value of this index is as follows:

$I=exp(-\sum p_{i}ln(p_{i}))$

for $i = 1, 2, \ldots,n$ and p_i>0 the percentages (as parts per unit) of votes or seats of the party i

Usage

hiper(votes, seats)

Arguments

Value

Returns a dataframe, with the columns "electoral" and "parliamentary" to store the value of the hyperfractionality index in the electoral or parliamentary way respectively

Examples

hiper(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

This nationalisation index, whose value ranges between 0 and 1, was proposed by Lago and Montero (2010) and is based on the decision of the parties to take part in the electoral process in all or only some districts. It is calculated by taking into account the electoral results of the parties, as well as the number of seats in the districts where they are elegible. The formula that is used is as follows:

$E=\sum_{j=1}^{J}p_{j}^{e}*q_{j}$

Where: p_j is the number of votes obtained in all the territories over the total expressed as parts per unit. q_j is the number of seats (out of the total number of seats) in the districts where political formation j is presented, expressed as parts per unit.

Two procedures have been enabled for the calculation of this index, one will be called 'automatic' and the other one 'manual'. With the automatic procedure it will use the data of the Spanish Ministry of Home Office and, without further intervention, the indicator for the elections to the General Courts that has been indicated with the parameters of the function will be calculated. For the manual procedure, the data to be processed must be passed as an input of the function, as indicated below in the section concerning the parameters.

Usage

IN_LAGO_MONTERO(
  Ano,
  Mes,
  Ruta,
  n_escanos = 350,
  Auto = TRUE,
  d_votos,
  d_escanos
)

Arguments

Value

The returned value is a list object, with three positions. The first position contains the value of the index (it is called 'V_indice'), the second one the vector containing the votes with respect to the national total expressed as part per unit (it is called 'Porcentaje_votos'), and the third component are the disputed seats (expressed as part per unit) of the districts where the political party under study is presented. Its name is 'Porcentaje_escanos'.

Examples

#Procedimiento automático
s<-IN_LAGO_MONTERO(2019,"04","F:/",n_escanos = 350)
if(!(is.null(s) || is.na(s))){
print(s$V_indice)
print(s$Porcentaje_votos)
print(s$Porcentaje_escanos)
}

#Procedimiento manual
da1 <- data.frame( # Contains the votes
 Reg=c("Alava","Albacete","Madrid","Barcelona","Valladolid"),
 PSOE=c(400,300,0,50,25),
 PP=c(300,200,10,150,2),
 Cs=c(400,0,3,300,45),
 Uno=c(465,23,341,263,0))

da2 <- data.frame( # Contains the total seats of each province
 Reg=c("Alava","Albacete","Madrid","Barcelona","Valladolid"),
 escanos=c(2,3,6,5,4))

s2<-IN_LAGO_MONTERO(Auto = FALSE,d_votos = da1,d_escanos = da2)

Description

This indicator was proposed by Edward V. Huntington and measures the degree of "injustice" in an electoral system by translating the votes obtained by political parties into seats. In this sense, Edward V.Huntington defined this mathematical injustice between two parties competing in an electoral process as the difference in absolute value between the ratios of seats and votes obtained by those two political parties. That is, for each political party the ratio between seats and votes is obtained, and the mathematical injustice will be the difference in absolute value of those ratios.

If we express the above in a mathematical formula we get the following:

$IM_{ij}=\left|\frac{e_{i}}{v_{i}}-\frac{e_{j}}{v_{j}}\right|$

for $i =1, 2, \ldots,n$ and vi the votes of the party i and ei its seats

Usage

InjusticiaM(dates)

Arguments

Value

The result is a matrix object, of dimension n x n, with n equal to the number of parties provided in the input data.frame. The names of the rows and columns coincide with the names of the parties provided in the input data.frame in the first column.

Examples

a<- data.frame(par=c('A','B','C','D'),
              vot=c(200,300,100,24),sea=c(3,4,1,0), stringsAsFactors = FALSE)

InjusticiaM(a)

Description

The function of this same package called 'InjusticeM()', allows to obtain the mathematical injustice for the dataset that is going going to be used for the study. However, if we want to obtain the mathematical injustice for different geographical areas, a significant effort and time is required to prepare and execute the data. With this function, this process is greatly expedited, if what we want to evaluate are results to the Congress of Deputies in Spain (or its counterpart in other countries), since thanks to this function, the information from the Ministry of Interior can be automatically extracted and processed directly for each province and national data, and the result is returned in a list() type object, where each element corresponds to the result obtained for each province or the total for the whole nation under study.

Likewise, if you have another type of election, you can use the parameter Auto=FALSE, and two data.frame will be provided with the characteristics that will be indicated later. In this way the mathematical injustices will be calculated for each region that is indicated in those data.frames

Usage

InjusticiaM_desagregada(Ano, Mes, Ruta, Auto = TRUE, d_votos, d_escanos)

Arguments

Value

The result is a list with 53 elements (for the automatic procedure), so that each element is a matrix containing the data of the calculated mathematical injustice. The 'names' in this list are those of Spanish provinces, or the expression 'Total' if they are the data of all Spain. When the procedure is not automatic, a list is also obtained containing a number of elements equal to the number of regions provided plus one, as there is an element called 'Total' which refers to all the territories provided as a whole.

Utilities

NA

Examples

f<-InjusticiaM_desagregada(2019,"04","F:/") # Aggregated mode

da1 <- data.frame( # Contains the votes
   Reg=c("Alava","Albacete","Madrid","Barcelona","Valladolid"),
     PSOE=c(400,300,0,50,25),
     PP=c(300,200,10,150,2),
     Cs=c(400,0,3,300,45),
     Uno=c(465,23,341,263,0))

da2 <- data.frame( # Contains the seats
     Reg=c("Alava","Albacete","Madrid","Barcelona","Valladolid"),
     PSOE=c(4,3,0,0,0),
     PP=c(2,3,0,1,0),
     Cs=c(4,0,0,2,1),
      Uno=c(3,0,3,2,0))

f2<- InjusticiaM_desagregada(Auto=FALSE,d_votos=da1,d_escanos = da2) #No agregegado

Description

Jones and Mainwaring (2003), proposed the calculation of this index based on the Gini income concentration index. This index will take values between 0 and 1. A value close to 1 indicates that the party is highly nationalized, and a value close to zero will indicate the opposite.

This index measures the degree of homogeneity of the votes a party receives in all the electoral districts where it competes. In a very brief way, the calculation formula for this indicator is as follows:

$IPN=1- Gini coefficient$

Usage

INP(dat)

Arguments

Value

A real number containing the value of the indicator.

Examples

INP(c(0.15,0.22,0.24,0.26,0.27,0.27,0.27,0.32,0.33,0.34))

INP(c(0.3,0.1,0.2))

Description

This indicator is based on the nationalization index of each party. Its calculation is computed using the aggregation of party nationalisation scores weighted with the value of electoral weight, that is, the number of votes obtained. As with the Party Nationalisation Index (INP), values close to 1 will indicate strong nationalisation and values close to zero will indicate the opposite. The formula used for this indicator is as follows:

$INSP=sum(INPi x pi)$

Where INPi is the nationalization index of party i, and pi is the number of votes obtained by party i.

Usage

INSP(datos)

Arguments

Value

A real number containing the value of the indicator.

Examples

a <- data.frame(prov=c("Alava","Albacete","Valladolid"),
 PP=c(20,30,10),PSOE=c(34,12,45),   stringsAsFactors = FALSE)

INSP(a)


b<-data.frame(prov=c("1","2"),A=c(400,190),B=c(200,1000),stringsAsFactors = FALSE)
INSP(b)

Description

An indicator of disproportionality that is appropriate for those systems that use the D'Hondt Law for seats distribution, as is the case in Spain (except in the Senate elections), presents the disadvantage that it only takes into account the difference between seats and votes for the most voted political force. The formula used for its calculation is as follows:

$Lmax=max[|V_i-E_i]$

for $i = 1, 2, \ldots,n$

Usage

L_max(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

L_max(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

This indicator is also known as the robust bias index. Its value is obtained by using the slope of the well-known "Tuckey Line" and its use is recommended when there is a "small" group of parties that behave significantly differently from the rest. This procedure is qualified as "robust" because the presence of behaviors of certain parties different from the rest does not substantially alter the line obtained, unlike what happened when it was used in the least squares criteria to make the regression adjustment.#'

Usage

L_Tukey(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

L_Tukey(c(3947,3189,1971,466,345,82),c(184,99,44,10,0,0))

Description

Loosemore and Hanby disproportionality index ( 1971 ) aims to address the difficulties encountered with the Rae index. To calculate this disproportionality index, the absolute values of the differences between votes and seats are added together and the result is divided by two. The specific formula used is as follows: '

$LH=\frac{\sum_{i=1}^{n}|E_{i}-V_{i}|}{2}$

for $i = 1, 2, \ldots,n$

Usage

Loos_Hanby(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

Loos_Hanby(c(1200, 30, 4000),c(10,6,8))

Description

With this function you can make choropleth maps, to carry out representations of the data on a map. By default, the map used is that of Spain, but any map of another country could be used too, as long as it is of shapefile type. NOTE: The shapefile to be used can be downloaded from the following web page https://github.com/Miguelro/Electoral/tree/master/Mapa and all the files will be placed in a folder, which will serve to point out the path to be indicated in the 'camino' parameter.

Usage

mapa(
  dat,
  camino,
  titulo,
  size_letra = 3,
  color_text = "brown",
  ver_text = TRUE
)

Arguments

Value

Returns a ggplot2 object, containing the map to be represented

Examples

d <-data.frame(
cod=c(1,2,5,9,15,23,43,50),
da = c(3,5,7,10,1,3,80,4)
)
suppressWarnings(mapa(d,system.file("maps/", package="Relectoral"),
                     titulo="An example map and some NA values",ver_text = TRUE))

Description

This indicator (Laakso and Taagepera 1979) is complementary to the electoral fragmentation indicator, and its objective is to measure the number of parties that actually compete in an electoral process. The mathematical formula used to calculate this indicator is as follows:

$N=\frac{1}{\sum p_{i}^{2}}$

for $i = 1, 2, \ldots,n$ and p_i the percentages (as parts per unit) of votes or seats of the party i

Usage

nep(votes, seats)

Arguments

Value

Returns a dataframe, with the columns "electoral" and "parliamentary" to store the value of the index of the effective number of parties in the electoral or parliamentary way respectively

Examples

nep(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

This indicator proposed by Molinar (1991), attempts to evaluate the relevant number of parties that actually exist in an electoral process. It can be said that this index shows a better performance than the indexes of the number of parties and hyperfractionality, both in the weighting it gives to the winning party and in the difference between the first and second parties, as well as in the degree of concentration of the minority parties. The formula used for the calculation of this index is the following:

$NP=1+N^{2}\sum_{i=2}p_{i}^{2}$

for $i = 2, \ldots,n$ and p_i the percentages (as parts per unit) of votes or seats of the party i

Usage

nepMolinar(votes, seats)

Arguments

Value

Returns a dataframe, with the columns "electoral" and "parliamentary" to store the value of the index of the Number of parties of Molinar in the electoral or the parliamentary way respectively.

Examples

nepMolinar(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

This dimension is one of the most important in democratic systems, as it explains part of the stability and bankruptcy problems of democracies. The index that is calculated with this function needs the data of the ideological location of the parties, which are difficult to obtain, and that is something as simple as a scale of values between 1 and 10, in such a way that the closer to 1 the valuation of a political party is, it will be more classified as left-wing ideology, and the opposite case will occur when a political formation has a rating at the other end of the scale. The value obtained for this ideological position will be placed in the second column of the data.frame called "data", which is the first parameter that is passed to the function and whose structure is explained later, in the parameters section. If the indicator takes a value close to zero, it means there will be no polarization, and the polarization will increase as the indicator takes higher values.

In this section the weighted polarization index is calculated. For its calculation the following formulas will be used:

$Pp=\sum_{i=1}^{n}p_{i}(x_{i}-\overline{x}_{p})^{2}$

for $i = 1, 2, \ldots,n$

$Pp2=\sum_{i=1}^{n}p_{i}\left|x_{i}-\overline{x}_{p}\right|$

for $i = 1, 2, \ldots,n$

Usage

polarizacion(datos, Tipo = 1)

Arguments

Value

Returns a numeric value of this indicator.

Examples

d <- data.frame(partidos=c("RN","PDC","PS","PPD","UDI","PRSC","otros"),
 ubicacion=c(6.36,5.31,2.73,4.13,7.04,4.00,5.33),
 c(19,20,15,21,33,7,5))
polarizacion(d,Tipo = 2)

Description

With this function the slightly adapted version of the polarisation index from Dalton (2008) is computed to measure polarization. This index weights the ideological positions of the parties by their election results. This indicator ranges from 0 (in the hypothetical case that all parties occupy the same ideological position) to 10 if the parties are at the extremes of the scale position. The formula to compute it is as follows:

$Pd=\sqrt{\sum_{i=1}^{n}p_{i}\left[\frac{\overline{x}_{i}-\overline{x}_{p}}{4.5}\right]^{2}}$

Pd = sqrt(sum(p_j*(x_j-mean(x_p))/4.5)^2) for $i = 1, 2, \ldots,n$

Usage

polarizacion_Dalton(datos)

Arguments

Value

Returns the numerical value of this indicator

Examples

d2<- data.frame(partidos=c("RN","PDC","PS","PPD","UDI","PRSC"),
 ubicacion=c(6.36,5.31,2.73,4.13,7.04,4.00),
 c(14.12,20.76,10.05,15.42,22.36,3.54))

polarizacion_Dalton(d2)

Description

This index measures electoral disproportionality based on the result obtained in an election. It has the disadvantage that it is highly influenced by the number of small parties that compete in the elections. The formula used is as follows:

$R=\frac{\sum_{i=1}^{n}|E_{i}-V_{i}|}{n}$

for $i = 1, 2, \ldots,n$

To compute its value, an integer vector corresponding to the votes obtained by each party and another with the corresponding seats must be provided as parameters of the formula. The R code will be in charge of calculating the corresponding percentages to obtain the index.

Usage

Rae(votes, seats)

Arguments

Value

Returns the value obtained for the index

Functions

• Rae: indicators

Indicator

NA

Examples

Rae(c(1200, 30, 4000),c(10,6,8))

Description

It was Rae himself who realised the problems in calculating the RAE index by taking all parties into account. For this reason he reviewed the basis of the index and decided to exclude from the calculation all parties that did not reach 0.5 percent of the votes. The function created to calculate this index takes this corrective value by default, although it is allowed to enter another value, as it can be seen in the example below.

To compute its value, the integer vectors corresponding to the votes obtained by each party and their seats must be provided as parameters of the formula. The R code will be in charge of calculating the corresponding percentages in order to obtain the index.

Usage

Rae_corregido(votes, seats, correc = 0.5)

Arguments

Value

Returns the value obtained for the index

Examples

Rae_corregido(c(1200, 30, 4000),c(10,6,8), correc = 1)
Rae_corregido(c(1200, 30, 4000),c(10,6,8))

Description

With this function we calculate the regionalism indexes described in the book "Analysis of electoral data" from Pablo Oñate y Francisco A. Ocaña (page 48). The reader is referred to this publication in order to understand the meaning of the indicators computed with this function. More precisely, the Regionalist Voting Index (VRta), the Differentiated Regionalist Voting Index (VRtaD), and the Differentiated Regional Voting Index (VRD) will be calculated.

Usage

Regionalismo(
  Ano = 0,
  Mes = "",
  RutaDescarga = "",
  Auto = TRUE,
  datos = "",
  PANES = "",
  Generate_PANES = FALSE
)

Arguments

Value

It returns a list of all the indicators obtained with this function. This initial list, in turn, contains two other lists:

1. VRtaD. It is composed of three other lists, each one containing a data.frame with the following information: $VRta_Provincias: contains the values of the VRta indicator at the lowest level (in the case of Spain, the provinces). $VRta_CCAA contains the VRta indicator at the intermediate level (in the case of Spain, at an autonomous region level). $VRtaD contains the indicator VRtaD at the lowest level.

2. VRD. Composed of three other lists, each one containing a data.frame with the following information: $inferior_medio contains the VRD value of the lower level (provinces in Spain) with respect to the intermediate level (autonomous regions in Spain). $inferior_superior contains the VRD value for the lower level with respect to the upper level (State level). $medio_superior contains the value of the VRD index at the intermediate level with respect to the upper one.

Examples

r <- Regionalismo(Ano=2019,Mes = "11",RutaDescarga = "F:/",
 PANES = system.file("regionalismo","Regionalismo.csv", package="Relectoral"))

Description

This option uses various methods whose main common feature is that the number of votes obtained by the political candidatures are divided by a series of numbers. The name of the method used depends on the numbers which compose that series. In this sense, the methods admitted are the following:

1. dhont. It is the so-called D'Hondt Law and is the procedure used in Spain to transform votes into seats in the Congress of Deputies. The series of numbers used as a quotient are the integer numbers: 1,2,...n , where n is the number of seats to be distributed.

2. saint_lague. This is the Sainte Lagüe method, Webster's method or odd-numbered divisors. The set of divisors is formed by the odd numbers, that is 1,3,..,2n+1.

3. saint_lague_Mod.It is the modified Sainte Lagüe method, a variant of the Sainte Lagüe method, according to which the initial quotient is v/1.4 and once each party has a seat the standard formula v/(2n+1) is used.

4. Danish. In this method the divisors go from three to three units, that is to say it is formed by the numbers: 1,4,7,10,13... and the n-th divisor would be 3*n-2.

5. Imperiali. The divisors are the positive integers but starting from number 2 onwards, that is: 2,3,4,...

6. Hill_Huntington. In this case the divisors are formed by the following sequence: sqrt(2),sqrt(6),sqrt(12),...., sqrt(n*(n+1))

7. Dean.The set of divisors consists of the following numbers: 4/3, 12/5, 24/7,40/0,....,(2n)(n+1)/(2n+1)

In all cases the value of q is rounded up to the nearest integer.

Usage

reparto_div(candidaturas, votos, escanos, metodo)

Arguments

Value

Returns a data.frame with the names of the political parties and the number of Members allocated with the chosen method.

See Also

Restos_Mayores for allocation using the largest remainder method

https://en.wikipedia.org/wiki/D%27Hondt_method on Wikipedia

https://bit.ly/3aEidM9 on Wikipedia.

https://bit.ly/2Q0a0u0 on the internet.

Examples

reparto_div(c("A","B","C","D","E"),c(340000,280000,160000,60000,15000),7,metodo="dhondt")
reparto_div(c("A","B","C","D"),c(340000,280000,160000,60000),7,metodo='saint_lague')
reparto_div(c("A","B","C","D"),c(340000,280000,160000,60000),7,metodo='saint_lague_Mod')
reparto_div(c("A","B","C","D"),c(340000,280000,160000,60000),7,metodo='Danish')
reparto_div(c("A","B","C","D"),c(340000,280000,160000,60000),7,metodo='Imperiali')
reparto_div(c("A","B","C","D"),c(340000,280000,160000,60000),7,metodo='Dean')

Description

This function is used to translate votes into seats, following the criterion called "Largest Remains". In summary, this method is calculated as follows: the total number of votes is divided by the total number of seats (cost of each seat). The votes of each party are then divided by the above ratio. The integer part of the above results are taken, which will be the initial seats for each party. The remaining seats to be distributed are allocated to the parties with the largest remainders (see https://en.wikipedia.org/wiki/Largest_remainder_method ).

The cost of each seat is what differentiates one method from another. Thus if n is the total number of seats and m the sum of all votes, the denomination of the methods is as follows, depending on the ratio that is taken:

1. Hare (Hare): q=m/n

2. Droop (Droop):q=1+(m/(1+n))

3. Imperiali (Imperiali): q=m/(n+2)

4. Imperiali modificado (Mod_Imperiali): q=m/(n+3)

5. Hangenbach Bischof (hangenbach-bischo): q=m/(n+1)

In all cases the value of q is rounded to the nearest integer.

Usage

Restos_Mayores(partidos, votos, escanos, metodo = "Hare")

Arguments

Value

A dataframe with the political parties and the votes assigned to each.

See Also

reparto_div for the allocation using divisor method

https://en.wikipedia.org/wiki/Largest_remainder_method on Wikipedia.

reparto_div

Examples

Restos_Mayores(c("A","B","C","D","E","F","G"),
 c(391000,311000,184000,73000,27000,12000,2000),
 21,metodo = "Imperiali")

Description

This index is designed to study the disproportionality of those electoral systems that use the procedure of distribution of seats that has the same name (Sainte-Lague method). It should be noted that this indicator takes into account the relative difference between the seats-votes of each party, as well as that of the system as a whole. The formula used for its calculation is as follows:

$SL=\sqrt{\sum\frac{(E_{i}-V_{i})^{2}}{V_{i}}}$

for $i = 1, 2, \ldots,n vi>0$

Usage

Sainte_Lague(votes, seats)

Arguments

Value

Returns the value obtained for the index

Examples

Sainte_Lague(c(3947,3189,1971,466,345,82),c(184,99,44,10,1,0))

Description

This function calculates the total volatility of Pederson (1983:31 and 32), and the volatility between blocks. The formulas for calculating these values can be found in Oñate and Ocaña's book "Analysis of electoral data", page 45. This dimension compares the behaviour of the electorate in two different elections, so as to see the transfer of votes, either between blocks or between political parties.

Usage

volatilidad(dat1, dat2, enlace)

Arguments

Value

It returns a list with two objects. The first is the total volatility, and the second is the inter-block volatility.