The esaps package

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.

Installation

You can install the released version (0.1.0) of esaps from CRAN with:

And the development version (0.2.2) from GitHub with:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("Nicolas-Schmidt/esaps")

Functions

Indicator Method Variables Function
Electoral Volatility Pedersen (1979) election unit party votes evolat(., method=1)
Powell and Tucker (2014) election unit party votes evolat(., method=2)
Torcal and Lago (2015) election unit party votes M evolat(., method=3)
Effective Number of Parties Laakso and Taagepera (1979) election unit party votes or seats enp()
Party System Nationalization Mainwaring and Jones (2003) election unit party votes votes_nac psn(., method=1)
Chhibber and Kollman (2004) election unit party votes votes_nac psn(., method=2)
Party Nationalization Mainwaring and Jones (2003) election unit party votes psn(., method=1, pn=TRUE)
Electoral Disproportionality Rae (1971) election unit party votes seats dispro(., method=1)
Loosemore and Hanby (1971) election unit party votes seats dispro(., method=2)
Lijphart_1 (1986) election unit party votes seats dispro(., method=3)
Lijphart_2 (1994) election unit party votes seats dispro(., method=4)
Gallagher (1991) election unit party votes seats dispro(., method=5)
Cox and Shugart (1991) election unit party votes seats dispro(., method=6)

Indicators and Variables

Examples

Load data

library(esaps)

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
#> [[1]]
#>   country year party_A party_B party_C
#> 1     ARG 1995      40      35      25
#> 2     ARG 2000      10      20      70
#> 3     ARG 2005      20      40      40
#> 
#> [[2]]
#>   country year party_A party_B party_C party_D
#> 1     URY 1995      30      30      30      10
#> 2     URY 2000      30      50      10      10
#> 3     URY 2005      20      40      30      10
#> 4     URY 2010      20      30      25      25
#> 
#> [[3]]
#>   country year party_A party_B
#> 1     BRA 1993      30      70
#> 2     BRA 1998      55      45

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

votes
#>    election unit   party votes
#> 1      1995  ARG party_A    40
#> 2      2000  ARG party_A    10
#> 3      2005  ARG party_A    20
#> 4      1995  ARG party_B    35
#> 5      2000  ARG party_B    20
#> 6      2005  ARG party_B    40
#> 7      1995  ARG party_C    25
#> 8      2000  ARG party_C    70
#> 9      2005  ARG party_C    40
#> 10     1993  BRA party_A    30
#> 11     1998  BRA party_A    55
#> 12     1993  BRA party_B    70
#> 13     1998  BRA party_B    45
#> 14     1995  URY party_A    30
#> 15     2000  URY party_A    30
#> 16     2005  URY party_A    20
#> 17     2010  URY party_A    20
#> 18     1995  URY party_B    30
#> 19     2000  URY party_B    50
#> 20     2005  URY party_B    40
#> 21     2010  URY party_B    30
#> 22     1995  URY party_C    30
#> 23     2000  URY party_C    10
#> 24     2005  URY party_C    30
#> 25     2010  URY party_C    25
#> 26     1995  URY party_D    10
#> 27     2000  URY party_D    10
#> 28     2005  URY party_D    10
#> 29     2010  URY party_D    25

## VOTES AND SEATS

votes_2 <- 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_2
#>   year country votes_party1 votes_party2 votes_party3 votes_party4 seats_party1
#> 1 2000     URY           20           30           40           10           25
#> 2 2005     URY           30           35           25           10           35
#>   seats_party2 seats_party3 seats_party4
#> 1           20           40           15
#> 2           30           30            5

votes_2c <- convert_esaps(dataset = votes_2, unit.name = "country", election.name = "year", seats = TRUE)

votes_2c
#>   election unit        party votes seats
#> 1     2000  URY votes_party1    20    25
#> 2     2000  URY votes_party2    30    20
#> 3     2000  URY votes_party3    40    40
#> 4     2000  URY votes_party4    10    15
#> 5     2005  URY votes_party1    30    35
#> 6     2005  URY votes_party2    35    30
#> 7     2005  URY votes_party3    25    30
#> 8     2005  URY votes_party4    10     5

Functions

Electoral volatility

evolat(votes, 1)
#>   election unit eVolat
#> 1     2000  ARG     45
#> 2     2005  ARG     30
#> 3     1998  BRA     25
#> 4     2000  URY     20
#> 5     2005  URY     20
#> 6     2010  URY     15

evolat(tidy_data = votes, method = 1, summary = TRUE)
#> [[1]]
#>   election unit eVolat
#> 1     2000  ARG     45
#> 2     2005  ARG     30
#> 3     1998  BRA     25
#> 4     2000  URY     20
#> 5     2005  URY     20
#> 6     2010  URY     15
#> 
#> [[2]]
#>   unit first_elec last_elec election mean_volat sd_volat
#> 1  ARG       2000      2005        2      37.50    10.61
#> 2  BRA       1998      1998        1      25.00       NA
#> 3  URY       2000      2010        3      18.33     2.89

Effective Number of Parties

enp(votes)
#>   election unit  enp
#> 1     1995  ARG 2.90
#> 2     2000  ARG 1.85
#> 3     2005  ARG 2.78
#> 4     1993  BRA 1.72
#> 5     1998  BRA 1.98
#> 6     1995  URY 3.57
#> 7     2000  URY 2.78
#> 8     2005  URY 3.33
#> 9     2010  URY 3.92

Electoral Disproportionality

votes_2
#>   year country votes_party1 votes_party2 votes_party3 votes_party4 seats_party1
#> 1 2000     URY           20           30           40           10           25
#> 2 2005     URY           30           35           25           10           35
#>   seats_party2 seats_party3 seats_party4
#> 1           20           40           15
#> 2           30           30            5

v2 <- convert_esaps(dataset = votes_2, unit.name = "country", election.name = "year", seats = TRUE)
dispro(v2, 1:6, 1)
#>   election unit  Rae  LH Lijphart_1 Lijphart_2 Gallagher Cox_Shugart
#> 1     2000  URY 0.05 0.1       0.10       0.06      0.32        1.00
#> 2     2005  URY 0.05 0.1       0.05       0.06      0.32        0.73

library(magrittr)

convert_esaps(dataset = votes_2, unit.name = "country", election.name = "year", seats = TRUE) %>%
        dispro(1:3)
#>   election unit  Rae  LH Lijphart_1
#> 1     2000  URY 0.05 0.1       0.10
#> 2     2005  URY 0.05 0.1       0.05


dispro(tidy_data = v2, method = c("Rae", "Gallagher"))
#>   election unit  Rae Gallagher
#> 1     2000  URY 0.05      0.32
#> 2     2005  URY 0.05      0.32

Party System Nationalization Score and Party Nationalization Score

votes3 <- 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)
                    )

votes3
#>   election       unit   party votes votes_nac
#> 1     2000 District_1 party_A   0.6      0.55
#> 2     2000 District_1 party_B   0.4      0.45
#> 3     2000 District_2 party_A   0.3      0.55
#> 4     2000 District_2 party_B   0.7      0.45

psns(tidy_data = votes3, method = 1)
#>   election   psns
#> 1     2000 84.695

psns(tidy_data = votes3, method = 1, pns = TRUE)
#> $PSNS
#>   election   psns
#> 1     2000 84.695
#> 
#> $PNS
#>     party election   pns
#> 1 party_A     2000 0.833
#> 2 party_B     2000 0.864

Linear Model

library(tidyverse)

dat <- inner_join(evolat(votes, 1), enp(votes))

dat
#>   election unit eVolat  enp
#> 1     2000  ARG     45 1.85
#> 2     2005  ARG     30 2.78
#> 3     1998  BRA     25 1.98
#> 4     2000  URY     20 2.78
#> 5     2005  URY     20 3.33
#> 6     2010  URY     15 3.92

lm(formula(dat[,4:3]), data = dat)
#> 
#> Call:
#> lm(formula = formula(dat[, 4:3]), data = dat)
#> 
#> Coefficients:
#> (Intercept)       eVolat  
#>     4.25394     -0.05731

ggplot(dat, aes(x = enp, y = eVolat )) +
        geom_point() +
        geom_smooth(method = lm, se = TRUE)

Citation

To cite esaps in publications, please use:

citation("esaps")

Author

Nicolas Schmidt ()