Package 'coalitions' reference manual

Title:	Bayesian "Now-Cast" Estimation of Event Probabilities in Multi-Party Democracies
Description:	An implementation of a Bayesian framework for the opinion poll based estimation of event probabilities in multi-party electoral systems (Bender and Bauer (2018) <doi:10.21105/joss.00606>).
Authors:	Andreas Bender [aut, cre] (ORCID: <https://orcid.org/0000-0001-5628-8611>), Alexander Bauer [aut] (ORCID: <https://orcid.org/0000-0003-3495-5131>), Rebekka Schade [ctb]
Maintainer:	Andreas Bender <[email protected]>
License:	MIT + file LICENSE
Version:	0.6.27
Built:	2026-05-08 17:15:35 UTC
Source:	https://github.com/cran/coalitions

Calculate coalition probability from majority table

Description

Given a table with simulations in the rows and coalitions in the columns, this function returns the coalition probabilities for a specified coalition, by default excluding superior coalitions first

Usage

calculate_prob(majority_df, coalition, exclude_superior = TRUE, ...)
calculate_prob(majority_df, coalition, exclude_superior = TRUE, ...)

Arguments

majority_df

A data frame containing logical values indicating if the coalitions (columns) have a majority (rows).

coalition

The coalition of interest for which superior coalitions will be obtained by get_superior.

exclude_superior

Logical. If TRUE, superior coalitions will be excluded, otherwise total coalition probabilities will be returned. Usually it makes sense to exclude superior coalitions.

...

Further arguments passed to get_superior

Value

A data frame with one numeric column giving the coalition probability (percentage of simulations in which the coalition obtained a majority, after optionally excluding superior coalitions).

Examples

test_df <- data.frame(
 cdu            = c(rep(FALSE, 9), TRUE),
 cdu_fdp        = c(rep(FALSE, 8), TRUE, TRUE),
 cdu_fdp_greens = c(TRUE, TRUE, rep(FALSE, 6), TRUE, TRUE))
calculate_prob(test_df, "cdu_fdp_greens") # exclude_superior defaults to TRUE
calculate_prob(test_df, "cdu_fdp_greens", exclude_superior=FALSE)
test_df <- data.frame(
 cdu            = c(rep(FALSE, 9), TRUE),
 cdu_fdp        = c(rep(FALSE, 8), TRUE, TRUE),
 cdu_fdp_greens = c(TRUE, TRUE, rep(FALSE, 6), TRUE, TRUE))
calculate_prob(test_df, "cdu_fdp_greens") # exclude_superior defaults to TRUE
calculate_prob(test_df, "cdu_fdp_greens", exclude_superior=FALSE)

Calculate coalition probabilities for multiple coalitions

Description

Given a table with simulations in the rows and coalitions in the columns, this function returns the coalition probabilities for a specified coalition, by default excluding superior coalitions first

Usage

calculate_probs(majority_df, coalitions, exclude_superior = TRUE, ...)
calculate_probs(majority_df, coalitions, exclude_superior = TRUE, ...)

Arguments

majority_df

A data frame containing logical values indicating if the coalitions (columns) have a majority (rows).

coalitions

A list of coalitions for which coalition probabilities should be calculated. Each list entry must be a vector of party names. Those names need to correspond to the names in majority_df.

exclude_superior

Logical. If TRUE, superior coalitions will be excluded, otherwise total coalition probabilities will be returned. Usually it makes sense to exclude superior coalitions.

...

Further arguments passed to get_superior

Value

A data frame with columns coalition (character) and probability (numeric, 0–100), one row per coalition.

Examples

test_df <- data.frame(
 cdu            = c(rep(FALSE, 9), TRUE),
 cdu_fdp        = c(rep(FALSE, 8), TRUE, TRUE),
 cdu_fdp_greens = c(TRUE, TRUE, rep(FALSE, 6), TRUE, TRUE))
calculate_probs(test_df, list("cdu", "cdu_fdp", "cdu_fdp_greens"))
calculate_probs(test_df, list("cdu", "cdu_fdp", "cdu_fdp_greens"), exclude_superior=FALSE)
test_df <- data.frame(
 cdu            = c(rep(FALSE, 9), TRUE),
 cdu_fdp        = c(rep(FALSE, 8), TRUE, TRUE),
 cdu_fdp_greens = c(TRUE, TRUE, rep(FALSE, 6), TRUE, TRUE))
calculate_probs(test_df, list("cdu", "cdu_fdp", "cdu_fdp_greens"))
calculate_probs(test_df, list("cdu", "cdu_fdp", "cdu_fdp_greens"), exclude_superior=FALSE)

Transform surveys in long format

Description

Given a data frame containing multiple surveys (one row per survey), transforms the data into long format with one row per party.

Usage

collapse_parties(
  surveys,
  parties = c("cdu", "spd", "greens", "fdp", "left", "pirates", "fw", "afd", "bsw",
    "others")
)
collapse_parties(
  surveys,
  parties = c("cdu", "spd", "greens", "fdp", "left", "pirates", "fw", "afd", "bsw",
    "others")
)

Arguments

surveys

A data frame with one survey per row.

parties

A character vector containing names of parties to collapse.

Value

Data frame in long format

Examples


emnid <- scrape_wahlrecht()
emnid.long <- collapse_parties(emnid)

emnid <- scrape_wahlrecht()
emnid.long <- collapse_parties(emnid)

Seat Distribution by D'Hondt

Description

Calculates number of seats for the respective parties according to the method of d'Hondt.

Usage

dHondt(votes, parties, n_seats = 183)
dHondt(votes, parties, n_seats = 183)

Arguments

votes

Number of votes per party.

parties

Names of parties (must be same length as votes).

n_seats

Number of seats in parliament. Defaults to 183 (seats in Austrian parliament).

Value

A named integer vector of seat counts, one entry per party, in the same order as parties. The vector has a logical attribute ties: TRUE if two or more parties had equal claim to the last seat (i.e. the result is not uniquely determined and was resolved randomly), FALSE otherwise. When ties = TRUE, re-running with a different random seed may produce a different but equally valid seat distribution.

Examples

library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1) %>% tidyr::unnest("survey")
# calculate the seat distribution based on D'Hondt for a parliament with 300 seats
dHondt(surveys$votes, surveys$party, n_seats = 300)
library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1) %>% tidyr::unnest("survey")
# calculate the seat distribution based on D'Hondt for a parliament with 300 seats
dHondt(surveys$votes, surveys$party, n_seats = 300)

Draw random numbers from posterior distribution

Description

Draw random numbers from posterior distribution

Usage

draw_from_posterior(
  survey,
  nsim = 10000,
  seed = as.numeric(now()),
  prior = NULL,
  correction = NULL
)
draw_from_posterior(
  survey,
  nsim = 10000,
  seed = as.numeric(now()),
  prior = NULL,
  correction = NULL
)

Arguments

survey

survey object as returned by as_survey or getSurveys

nsim

number of simulations

seed

sets seed

prior

optional prior information. Defaults to 1/2 (Jeffrey's prior).

correction

A positive number. If not NULL, each sample from the Dirichlet distribution will be additionally "corrected" by a random number from U(-1*correction, 1*correction). This can be used to introduce extra variation which might be useful due to rounding errors from reported survey results (or add an additional source of variation in general).

Value

data.frame containing random draws from Dirichlet distribution which can be interpreted as election results.

Wrapper for calculation of coalition probabilities from survey

Description

Given a table with simulations in the rows and coalitions in the columns, this function returns the coalition probabilities for a specified coalition, by default excluding superior coalitions first

Usage

get_probabilities(
  x,
  coalitions = list(c("cdu"), c("cdu", "fdp"), c("cdu", "fdp", "greens"), c("spd"),
    c("spd", "left"), c("spd", "left", "greens")),
  nsim = 1e+05,
  distrib.fun = sls,
  seats_majority = 300L,
  seed = as.numeric(now()),
  correction = NULL
)
get_probabilities(
  x,
  coalitions = list(c("cdu"), c("cdu", "fdp"), c("cdu", "fdp", "greens"), c("spd"),
    c("spd", "left"), c("spd", "left", "greens")),
  nsim = 1e+05,
  distrib.fun = sls,
  seats_majority = 300L,
  seed = as.numeric(now()),
  correction = NULL
)

Arguments

x

A table containing one row per survey and survey information in long format in a separate column named survey.

coalitions

A list of coalitions for which coalition probabilities should be calculated. Each list entry must be a vector of party names. Those names need to correspond to the names in majority_df.

nsim

number of simulations

distrib.fun

Function to calculate seat distribution. Defaults to sls (Sainte-Lague/Schepers).

seats_majority

The number of seats needed to obtain majority.

seed

sets seed

correction

Value

A tibble with the same rows as x (one per survey) and an additional list-column probabilities containing a data frame of coalition names and their probabilities (0–100) for each survey.

Examples

library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# calculate probabilities for two coalitions
probs <- get_probabilities(surveys,
                           coalitions = list(c("cdu", "fdp"),
                                             c("spd", "left", "greens")),
                           nsim = 100) # ensure fast runtime with only 100 simulations
probs %>% tidyr::unnest("probabilities")
library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# calculate probabilities for two coalitions
probs <- get_probabilities(surveys,
                           coalitions = list(c("cdu", "fdp"),
                                             c("spd", "left", "greens")),
                           nsim = 100) # ensure fast runtime with only 100 simulations
probs %>% tidyr::unnest("probabilities")

Calculate seat distribution from draws from posterior

Description

Calculate seat distribution from draws from posterior

Usage

get_seats(
  dirichlet.draws,
  survey,
  distrib.fun = sls,
  samplesize = NULL,
  hurdle = 0.05,
  others = "others",
  ...
)
get_seats(
  dirichlet.draws,
  survey,
  distrib.fun = sls,
  samplesize = NULL,
  hurdle = 0.05,
  others = "others",
  ...
)

Arguments

dirichlet.draws

Matrix containing random draws from posterior.

survey

The actual survey results on which dirichlet.draws were based on.

distrib.fun

Function to calculate seat distribution. Defaults to sls (Sainte-Lague/Schepers).

samplesize

Number of individuals participating in the survey.

hurdle

The percentage threshold which has to be reached by a party to enter the parliament. Any party called "ssw" will be exempt from the hurdle.

others

A string indicating the name under which parties not listed explicitly are subsumed.

...

Further arguments passed to distrib.fun.

Value

A data frame containing seat distributions for each simulation in dirichlet.draws

Examples

library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# simulate 100 seat distributions
surveys <- surveys %>% mutate(draws = purrr::map(survey, draw_from_posterior, nsim = 100),
                              seats = purrr::map2(draws, survey, get_seats))
surveys$seats
library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# simulate 100 seat distributions
surveys <- surveys %>% mutate(draws = purrr::map(survey, draw_from_posterior, nsim = 100),
                              seats = purrr::map2(draws, survey, get_seats))
surveys$seats

Scrape surveys from all pollsters

Description

Given a specific date, extract the survey from this date or the last one before this date.

Usage

get_surveys(country = "DE")

get_surveys_by()

get_surveys_rp()

get_surveys_nds()

get_surveys_saxony()

get_surveys_brb()

get_surveys_thuringen()

get_latest(surveys = NULL, max_date = Sys.Date())
get_surveys(country = "DE")

get_surveys_by()

get_surveys_rp()

get_surveys_nds()

get_surveys_saxony()

get_surveys_brb()

get_surveys_thuringen()

get_latest(surveys = NULL, max_date = Sys.Date())

Arguments

country

Choose country from which surveys should be scraped. Currently "DE" (Germany) is supported.

surveys

If provided, latest survey will be obtained from this object, otherwise calls get_surveys.

max_date

Specifies the date, relative to which latest survey will be searched for. Defaults to Sys.Date.

Value

Nested tibble. When fully unnested, the dataset contains the following columns:

pollster: Character name of the polling institute.
date: Publication date of the poll.
start, end: Start and end date of the field period, i.e. the dates during which the poll was conducted.
respondents: Number of respondents in the poll.
party: Character name of an individual party.
percent: Percentage of respondents that chose the party. Given in percentage points, i.e. 38% is given as 38.
votes: Number of respondents that chose the party.

Examples


library(coalitions)
get_surveys()

library(coalitions)
### Scrape the newest poll for the German federal election
# Possibility 1: Calling get_latest without arguments scrapes surveys from the web
# Possibility 2: Use get_latest() on an already scraped dataset
surveys <- get_latest(surveys_sample)
library(coalitions)
get_surveys()

library(coalitions)
### Scrape the newest poll for the German federal election
# Possibility 1: Calling get_latest without arguments scrapes surveys from the web
# Possibility 2: Use get_latest() on an already scraped dataset
surveys <- get_latest(surveys_sample)

Plot voter shares observed in one survey

Description

Bar chart of the raw voter shares observed in one survey. Additionally to plotting positive voter shares, the function can be used to plot party-specific differences (e.g. between a survey and the election result), including negative numbers.

Usage

gg_survey(data, colors = NULL, labels = NULL, annotate_bars = TRUE, hurdle = 5)
gg_survey(data, colors = NULL, labels = NULL, annotate_bars = TRUE, hurdle = 5)

Arguments

data

Scraped dataset containing one row per party in the column party and the observed voter share in the column percent

colors

Named vector containing party colors. If NULL (default) tries to guess color based on party names, gray otherwise.

labels

Named vector containing party labels. If NULL (default) tries to guess party names from data.

annotate_bars

If TRUE (default) bars are annotated by the respective vote share (percentage).

hurdle

Hurdle for single parties to get into the parliament, e.g. '5' for '5%'. If set to NULL no horizontal line is plotted. The horizontal line can be suppressed using NULL.

Value

A ggplot object displaying voter shares as a bar chart.

Examples

library(tidyr)
library(dplyr)
library(coalitions)

survey <- surveys_sample$surveys[[1]]$survey[[1]]

gg_survey(survey)
library(tidyr)
library(dplyr)
library(coalitions)

survey <- surveys_sample$surveys[[1]]$survey[[1]]

gg_survey(survey)

Seat Distribution by Hare/Niemeyer

Description

Calculates number of seats for the respective parties that have received more than hurdle percent of votes (according to the method of Hare/Niemeyer)

Usage

hare_niemeyer(votes, parties, n_seats = 183)
hare_niemeyer(votes, parties, n_seats = 183)

Arguments

votes

Number of votes per party.

parties

Names of parties (must be same length as votes).

n_seats

Number of seats in parliament. Defaults to 183 (seats in Austrian parliament).

Value

A data.frame containing parties above the hurdle and the respective seats/percentages after redistribution via Hare/Niemeyer

Examples

library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1) %>% tidyr::unnest("survey")
# calculate the seat distribution based on Hare/Niemeyer for a parliament with 300 seats
hare_niemeyer(surveys$votes, surveys$party, n_seats = 300)
library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1) %>% tidyr::unnest("survey")
# calculate the seat distribution based on Hare/Niemeyer for a parliament with 300 seats
hare_niemeyer(surveys$votes, surveys$party, n_seats = 300)

Do coalitions have a majority

Description

Do coalitions have a majority

Usage

have_majority(
  seats_tab,
  coalitions = list(c("cdu"), c("cdu", "fdp"), c("cdu", "fdp", "greens"), c("spd"),
    c("spd", "left"), c("spd", "left", "greens")),
  seats_majority = 300L,
  collapse = "_"
)
have_majority(
  seats_tab,
  coalitions = list(c("cdu"), c("cdu", "fdp"), c("cdu", "fdp", "greens"), c("spd"),
    c("spd", "left"), c("spd", "left", "greens")),
  seats_majority = 300L,
  collapse = "_"
)

Arguments

seats_tab

A data frame containing number of seats obtained by a party. Must have columns party and seats.

coalitions

A list of coalitions for which coalition probabilities should be calculated. Each list entry must be a vector of party names. Those names need to correspond to the names in majority_df.

seats_majority

The number of seats needed to obtain majority.

collapse

Character string passed to base::paste.

Value

A data frame with one column per coalition. Each column is logical indicating whether the coalition obtained a majority in each simulation row.

Examples

library(coalitions)
library(dplyr)
library(purrr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# check for majorities of two coalitions
coals <- list(c("cdu", "fdp"),
              c("spd", "left", "greens"))
# only use 100 simulations for a fast runtime
surveys <- surveys %>% mutate(draws = map(survey, draw_from_posterior, nsim = 100),
                              seats = map2(draws, survey, get_seats),
                              majorities = map(seats, have_majority, coalitions = coals))
surveys$majorities
library(coalitions)
library(dplyr)
library(purrr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# check for majorities of two coalitions
coals <- list(c("cdu", "fdp"),
              c("spd", "left", "greens"))
# only use 100 simulations for a fast runtime
surveys <- surveys %>% mutate(draws = map(survey, draw_from_posterior, nsim = 100),
                              seats = map2(draws, survey, get_seats),
                              majorities = map(seats, have_majority, coalitions = coals))
surveys$majorities

Colors for German parties

Description

A vector of colors associated with German parties.

Usage

party_colors_de
party_colors_de

Format

A named character vector. Names indicate parties. Values contain color strings for the respective parties

Labels for German parties

Description

A vector of labels associated with German parties.

Usage

party_labels_de
party_labels_de

Format

A named character vector. Names indicate parties. Values contain party names suitable for plot labels.

Obtain pooled survey during specified period

Description

Per default, pools surveys starting from current date and going 14 days back. For each pollster within the defined time-frame, only the most recent survey is used.

Usage

pool_surveys(
  surveys,
  last_date = Sys.Date(),
  pollsters = c("allensbach", "emnid", "forsa", "fgw", "gms", "infratest", "dimap",
    "infratestdimap", "insa"),
  period = 14,
  period_extended = NA,
  corr = 0.5,
  weights = NULL
)
pool_surveys(
  surveys,
  last_date = Sys.Date(),
  pollsters = c("allensbach", "emnid", "forsa", "fgw", "gms", "infratest", "dimap",
    "infratestdimap", "insa"),
  period = 14,
  period_extended = NA,
  corr = 0.5,
  weights = NULL
)

Arguments

surveys

A tibble containing survey results for multiple pollsters as returned by get_surveys.

last_date

Only surveys in the time-window from last_date to last_date - period will be considered for each pollster. Defaults to current date.

pollsters

Character vector of pollsters that should be considered for pooling.

period

See last_date argument.

period_extended

Optional. If specified, all surveys in the time-window from last_date - period_extended to last_date - period will also be considered for each pollster, but only after down-weighting them by halving their true sample size.

corr

Assumed correlation between surveys (of different pollsters). Defaults to 0.5.

weights

Additional weights for individual surveys.

Value

A data frame with one row per party containing columns pollster (set to "pooled"), date, start, end, respondents (effective sample size), party, percent, and votes.

Examples

library(coalitions)
library(dplyr)
latest <- get_latest(surveys_sample)
pool_surveys(surveys_sample, last_date=as.Date("2017-09-02"))
library(coalitions)
library(dplyr)
latest <- get_latest(surveys_sample)
pool_surveys(surveys_sample, last_date=as.Date("2017-09-02"))

Calculate percentage of votes/seats after excluding parties with `votes < hurdle`

Description

Calculate percentage of votes/seats after excluding parties with votes < hurdle

Usage

redistribute(survey, hurdle = 0.05, others = "others", epsilon = 1e-05)
redistribute(survey, hurdle = 0.05, others = "others", epsilon = 1e-05)

Arguments

survey

The actual survey results on which dirichlet.draws were based on.

hurdle

The percentage threshold which has to be reached by a party to enter the parliament. Any party called "ssw" will be exempt from the hurdle.

others

A string indicating the name under which parties not listed explicitly are subsumed.

epsilon

Percentages should add up to 1. If they do not, within accuracy of epsilon, an error is thrown.

Value

A data frame with the same structure as survey but with parties below the hurdle removed and vote percentages renormalized.

Examples

library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# redistribute the shares of 'others' parties and parties with a share of under 5\%
surveys <- surveys %>% mutate(survey_redist = purrr::map(survey, redistribute))
surveys$survey # results before redistribution
surveys$survey_redist # results after redistribution
library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1)
# redistribute the shares of 'others' parties and parties with a share of under 5\%
surveys <- surveys %>% mutate(survey_redist = purrr::map(survey, redistribute))
surveys$survey # results before redistribution
surveys$survey_redist # results after redistribution

Scrape surveys for German general election

Description

Scrapes survey tables and performs sanitation to output tidy data

Usage

scrape_wahlrecht(
  address = "https://www.wahlrecht.de/umfragen/emnid.htm",
  parties = c("CDU", "SPD", "GRUENE", "FDP", "LINKE", "PIRATEN", "AFD", "BSW",
    "SONSTIGE")
)

scrape_by(
  address = "https://www.wahlrecht.de/umfragen/landtage/bayern.htm",
  parties = c("CSU", "SPD", "GRUENE", "FDP", "LINKE", "PIRATEN", "FW", "AFD", "SONSTIGE")
)

scrape_rp(
  address = "https://www.wahlrecht.de/umfragen/landtage/rheinland-pfalz.htm",
  parties = c("CDU", "SPD", "GRUENE", "FDP", "LINKE", "AFD", "FW", "SONSTIGE"),
  ind_row_remove = -c(1:3)
)

scrape_ltw(
  address = "https://www.wahlrecht.de/umfragen/landtage/niedersachsen.htm",
  parties = c("CDU", "SPD", "GRUENE", "FDP", "LINKE", "PIRATEN", "FW", "AFD", "BSW",
    "SONSTIGE"),
  ind_row_remove = -c(1:2)
)
scrape_wahlrecht(
  address = "https://www.wahlrecht.de/umfragen/emnid.htm",
  parties = c("CDU", "SPD", "GRUENE", "FDP", "LINKE", "PIRATEN", "AFD", "BSW",
    "SONSTIGE")
)

scrape_by(
  address = "https://www.wahlrecht.de/umfragen/landtage/bayern.htm",
  parties = c("CSU", "SPD", "GRUENE", "FDP", "LINKE", "PIRATEN", "FW", "AFD", "SONSTIGE")
)

scrape_rp(
  address = "https://www.wahlrecht.de/umfragen/landtage/rheinland-pfalz.htm",
  parties = c("CDU", "SPD", "GRUENE", "FDP", "LINKE", "AFD", "FW", "SONSTIGE"),
  ind_row_remove = -c(1:3)
)

scrape_ltw(
  address = "https://www.wahlrecht.de/umfragen/landtage/niedersachsen.htm",
  parties = c("CDU", "SPD", "GRUENE", "FDP", "LINKE", "PIRATEN", "FW", "AFD", "BSW",
    "SONSTIGE"),
  ind_row_remove = -c(1:2)
)

Arguments

address

http-address from which tables should be scraped.

parties

A character vector containing names of parties to collapse.

ind_row_remove

Negative vector of rows that will be skipped at the beginning.

Value

A tibble with one row per survey date and columns for date, respondents, and one column per party containing the percentage of votes.

Examples


library(coalitions)
library(dplyr)
scrape_wahlrecht() %>% slice(1:5)


# Niedersachsen
scrape_ltw() %>% slice(1:5)
# Hessen
scrape_ltw("https://www.wahlrecht.de/umfragen/landtage/hessen.htm", ind_row_remove=-c(1)) %>%
 slice(1:5)

library(coalitions)
library(dplyr)
scrape_wahlrecht() %>% slice(1:5)


# Niedersachsen
scrape_ltw() %>% slice(1:5)
# Hessen
scrape_ltw("https://www.wahlrecht.de/umfragen/landtage/hessen.htm", ind_row_remove=-c(1)) %>%
 slice(1:5)

Seat Distribution by Sainte-Lague/Schepers

Description

Calculates number of seats for the respective parties that have received more than 5% of votes (according to the method of Sainte-Lague/Schepers, see https://www.wahlrecht.de/verfahren/rangmasszahlen.html).

Usage

sls(votes, parties, n_seats = 598L)
sls(votes, parties, n_seats = 598L)

Arguments

votes

A numeric vector giving the redistributes votes

parties

A character vector indicating the names of parties with respective votes.

n_seats

The total number of seats that can be assigned to the different parties.

Value

Examples

library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1) %>% tidyr::unnest("survey")
# calculate the seat distribution based on Sainte-Lague/Schepers for a parliament with 300 seats
sls(surveys$votes, surveys$party, n_seats = 300)
library(coalitions)
library(dplyr)
# get the latest survey for a sample of German federal election polls
surveys <- get_latest(surveys_sample) %>% ungroup() %>% slice(1) %>% tidyr::unnest("survey")
# calculate the seat distribution based on Sainte-Lague/Schepers for a parliament with 300 seats
sls(surveys$votes, surveys$party, n_seats = 300)

Sample of selected surveys

Description

A data set with surveys from seven different pollsters, three surveys per pollster. Surveys report support for different parties in the running for the German Bundestag prior to the 2017 election.

Usage

surveys_sample
surveys_sample

Format

A nested data frame with 7 rows and 2 columns:

institute: name of the pollster
surveys: a list of data frames, each containing one survey

Source

https://www.wahlrecht.de/

Try call of read_html that throws an error if the url cannot be resolved

Description

Try call of read_html that throws an error if the url cannot be resolved

Usage

try_readHTML(url)
try_readHTML(url)

Arguments

url

http-address that should be scraped.

Value

An xml_document object as returned by xml2::read_html.

Package 'coalitions'

Help Index

Calculate coalition probability from majority table

Description

Usage

Arguments

Value

Examples

Calculate coalition probabilities for multiple coalitions

Description

Usage

Arguments

Value

See Also

Examples

Transform surveys in long format

Description

Usage

Arguments

Value

Examples

Seat Distribution by D'Hondt

Description

Usage

Arguments

Value

See Also

Examples

Draw random numbers from posterior distribution

Description

Usage

Arguments

Value

See Also

Wrapper for calculation of coalition probabilities from survey

Description

Usage

Arguments

Value

See Also

Examples

Calculate seat distribution from draws from posterior

Description

Usage

Arguments

Value

See Also

Examples

Scrape surveys from all pollsters

Description

Usage

Arguments

Value

Examples

Plot voter shares observed in one survey

Description

Usage

Arguments

Value

Examples

Seat Distribution by Hare/Niemeyer

Description

Usage

Arguments

Value

See Also

Examples

Do coalitions have a majority

Description

Usage

Arguments

Value

Examples

Colors for German parties

Description

Usage

Format

Labels for German parties

Description

Usage

Calculate percentage of votes/seats after excluding parties with `votes < hurdle`