manifestoR: Access and Process Data and Documents of the Manifesto Project

Description

Provides access to coded election programmes from the Manifesto Corpus and to the Manifesto Project's Main Dataset and routines to analyse this data. The Manifesto Project https://manifesto-project.wzb.eu collects and analyses election programmes across time and space to measure the political preferences of parties. The Manifesto Corpus contains the collected and annotated election programmes in the Corpus format of the package 'tm' to enable easy use of text processing and text mining functionality. Specific functions for scaling of coded political texts are included.

Author(s)

Maintainer: Sven Regel sven.regel@wzb.eu

Authors:

• Jirka Lewandowski jirka.lewandowski@wzb.eu

• Nicolas Merz nicolas.merz@wzb.eu

Other contributors:

• Pola Lehmann pola.lehmann@wzb.eu [contributor]

• Paul Muscat paul.muscat@wzb.eu [contributor]

See Also

Useful links:

• https://github.com/ManifestoProject/manifestoR

• https://manifesto-project.wzb.eu/

• Report bugs at https://github.com/ManifestoProject/manifestoR/issues

Manifesto Availability Information class

Description

Objects returned by mp_availability.

Details

ManifestoAvailability objects are data.frames with variables party and date identifying the requested manifestos as in the Manifesto Project's Main Datasets. The additional variables specify whether a machine readable document is available (manifestos, "Documents found"), whether digital CMP coding annotations are available (annotations, "Coded Documents found"), whether an orignal PDF is available (originals, "Originals found"), or whether an english translation for the (digitally annotated) machine-readable document is available (translation_en, "English Translations found").

Additional a ManifestoAvailability object has attributes query, containing the original id set which was queried, corpus_version, specifying the Corpus version ID used for the query, and date with the timestamp of the query.

Examples

## Not run: 
wanted <- data.frame(party=c(41320, 41320), date=c(200909, 200509))
mp_availability(wanted)

## End(Not run)

Manifesto Corpus class

Description

Objects of this class are returned by mp_corpus.

Usage

ManifestoCorpus(csource = ManifestoJSONSource())

Arguments

Details

A tm Corpus storing ManifestoDocuments

For usage and structure of the stored documents see ManifestoDocument.

Examples

## Not run: corpus <- mp_corpus(subset(mp_maindataset(), countryname == "Russia"))

Manifesto Document

Description

A ManifestoDocument represents a document from the Manifesto Corpus and contains text, coding and meta information. ManifestoDocument objects need not be constructed manually but are the content of the ManifestoCorpus objects downloaded from the Manifesto Corpus Database API via mp_corpus.

ManifestoDocuments subclass the TextDocument class from the package tm. Hence they can be and usually are collected in a tm Corpus to interface easily with text mining and other linguistic analysis functions. manifestoR uses the subclass ManifestoCorpus of tms Corpus, but ManifestoDocuments can be stored in any kind of Corpus.

As in tm any ManifestoDocument has metadata which can be accessed and modified via the meta function, as well as content, accessible via content. Additionally, via codes(), the coding of the (quasi-)sentence ccording to the CMP category scheme can be accessed (and modified). The CMP category scheme can be found online at https://manifesto-project.wzb.eu/coding_schemes/mp_v4 (version 4) or https://manifesto-project.wzb.eu/coding_schemes/mp_v5 (version 5).

Usage

ManifestoDocument(
  content = data.frame(),
  id = character(0),
  meta = ManifestoDocumentMeta()
)

Arguments

Details

Internally, a ManifestoDocument is a data.frame with a row for every quasi-sentence and the columns text and code.

Examples

## Not run: 
corpus <- mp_corpus(subset(mp_maindataset(), countryname == "New Zealand"))
doc <- corpus[[1]]
print(doc)

## End(Not run)

Manifesto Document Metadata

Description

Manifesto Document Metadata

Usage

ManifestoDocumentMeta(meta = list(), id = character(0))

Arguments

Data Source for Manifesto Corpus

Description

Data Source for Manifesto Corpus

Usage

ManifestoSource(texts)

ManifestoJSONSource(
  texts = list(manifesto_id = c(), items = c()),
  query_meta = data.frame()
)

Arguments

Details

Used internally for constructing ManifestoCorpus objects.

Aggregate category percentages in groups

Description

aggregate_pers is a general function to aggregate percentage variables by creating a new variable holding the sum. If a variable with the name for the aggregate already exists and if it is part of the 'overwrite' parameter, it is overwritten.

Usage

aggregate_pers(
  data,
  groups = v5_v4_aggregation_relations(),
  na.rm = FALSE,
  keep = FALSE,
  overwrite = names(groups),
  verbose = TRUE
)

Arguments

See Also

aggregate_pers_cee

Aggregate cee-categories to main categories

Description

Adds the code frequencies in a dataset of the 4 digit per-variables (per1011 to per7062 - mostly used in codings of Central and Eastern European countries) to the main categories in the coding scheme (3 digits).

Usage

aggregate_pers_cee(data)

Arguments

Details

A wrapper of aggregate_pers using cee_aggregation_relations.

See Also

aggregate_pers

Compute year from date variable in MPDS

Description

Compute year from date variable in MPDS

Usage

attach_year(mpds)

Arguments

Value

input data with year variable attached

Default programmatic clarity dimensions from Giebler/Lacewell/Regel/Werner 2015.

Description

Default programmatic clarity dimensions from Giebler/Lacewell/Regel/Werner 2015.

Usage

clarity_dimensions()

References

Giebler/Lacewell/Regel/Werner (2015). Mass, Catch-all, or Programmatic? Toward an Empirical Classification of Party Types. Manuscript.

Access the codes of a Manifesto Document or Corpus

Description

With the accessor the codes of a Manifesto Document can be read and modified. The codes of a Manifesto Corpus can only be read, modification needs to be done document-wise.

Usage

codes(x, layer = "cmp_code")

## S3 method for class 'ManifestoDocument'
codes(x, layer = "cmp_code")

## S3 method for class 'ManifestoCorpus'
codes(x, layer = "cmp_code")

codes(x, layer = "cmp_code") <- value

## S3 replacement method for class 'ManifestoDocument'
codes(x, layer = "cmp_code") <- value

code_layers(x)

Arguments

Count the codings from a ManifestoDocument

Description

Count the codings from a ManifestoDocument

Usage

count_codes(
  doc,
  code_layers = c("cmp_code"),
  with_eu_codes = "auto",
  prefix = "per",
  relative = TRUE,
  include_codes = if ("cmp_code" %in% code_layers) {
     v4_categories()
 } else {
   
     c()
 },
  aggregate_v5_subcategories = TRUE,
  drop_codes = if ("cmp_code" %in% code_layers) {
     c("H")
 } else {
     c()
 }
)

Arguments

Value

A data.frame with onw row and the counts/percentages as columns

Format ids for web API queries

Description

Formats a data.frame of ids such that it can be used for querying the Manifesto Project Database. That is, it must have non-NA-fields party and date.

Usage

formatids(ids)

Arguments

Format the main data set

Description

Creates the format that is visible to the R user from the internal data.frames files (in cache or from the API)

Usage

formatmpds(mpds)

Arguments

Format the parties data set

Description

Creates the format that is visible to the R user from the internal data.frames files (in cache or from the API)

Usage

formatpartiesds(partiesds)

Arguments

Left-Right Scores based on Franzmann & Kaiser Method

Description

Computes left-right scores based on the Franzmann & Kaiser Method (see reference below). The issue structures are not calculated from scratch but taken as given from Franzmann 2009 (or later updates). Note that they are not available for the entire Manifesto Project Dataset, but only for a subset of countries and elections.

Usage

franzmann_kaiser(
  data,
  basevalues = TRUE,
  smoothing = TRUE,
  vars = grep("per\\d{3}$", names(data), value = TRUE),
  issue_structure = read_fk_issue_structure(mean_presplit = mean_presplit),
  party_system_split = split_belgium,
  mean_presplit = TRUE,
  ...
)

read_fk_issue_structure(
  path = system.file("extdata", "fk_issue_structure_2019.csv", package = "manifestoR"),
  mean_presplit = TRUE,
  format_version = 2
)

fk_smoothing(data, score_name, use_period_length = TRUE, ...)

Arguments

References

Franzmann, Simon/Kaiser, Andre (2006): Locating Political Parties in Policy Space. A Reanalysis of Party Manifesto Data, Party Politics, 12:2, 163-188

Franzmann, Simon (2009): The Change of Ideology: How the Left-Right Cleavage transforms into Issue Competition. An Analysis of Party Systems using Party Manifesto Data. PhD Thesis. Cologne.

Download content from the Manifesto Database

Description

Internal implementation. For more convenient access and caching use one of mp_corpus, mp_availability, mp_maindataset.

Usage

get_mpdb(type, parameters = c(), versionid = NULL, apikey = NULL)

Arguments

Get API results via cache

Description

Get API results via cache

Usage

get_viacache(type, ids = c(), cache = TRUE, versionid = NULL, ...)

Arguments

Details

This function is internal to manifestoR and not designed for use from other namespaces

Apply a function if and only if test is TRUE

Description

otherwise return input value unchanged

Usage

iff(obj, test, fun, ...)

iffn(obj, test, fun, ...)

Arguments

Details

iffn is ... if and only if test is FALSE

Issue Attention Diversity

Description

Effective number of Manifesto Issues suggested by Zac Greene. When using the measure please cite Greene 2015 (see reference below)

Usage

issue_attention_diversity(
  data,
  method = "shannon",
  prefix = "per",
  include_variables = paste0(prefix, setdiff(v4_categories(), "uncod")),
  aggregate_categories = list(c(101, 102), c(104, 105), c(107, 109), c(108, 110), c(203,
    204), c(301, 302), c(406, 407), c(409, 414), c(504, 505), c(506, 507), c(601, 602),
    c(603, 604), c(607, 608), c(701, 702))
)

Arguments

References

Greene, Z. (2015). Competing on the Issues How Experience in Government and Economic Conditions Influence the Scope of Parties' Policy Messages. Party Politics.

Median Voter position

Description

The position of the median voter, calculated after Kim and Fording (1998; 2003), with possible adjustment after McDonald 2002.

Usage

median_voter(
  positions,
  voteshares = "pervote",
  scale = "rile",
  groups = c("country", "edate"),
  na.rm.voteshares = FALSE,
  na.rm.positions = FALSE,
  ...
)

median_voter_single(
  positions,
  voteshares,
  adjusted = FALSE,
  scalemin = -100,
  scalemax = 100,
  na.rm.voteshares = FALSE,
  na.rm.positions = FALSE
)

Arguments

Details

median_voter is able to compute the median voter positions for multiple elections at once, while median_voter_single treats data as coming from a single election.

calculated according to the formula by Kim and Fording (1998; 2003)

m = L + \frac{K-C}{F} W

Where m is the median voter position, L is lower end of the interval containing the median, K is 0.5*sum(voteshare), C is the cumulative vote share up to but not including the interval containing the median, F is the vote share in the interval containing the median and W is the width of the interval containing the median.

Different parties with the same left-right position (e.g. alliances) are treated as one party with the cumulative vote share.

In the adjusted formula the midpoint is "mirrored" from the midpoint of the other side: "Rather than assuming the party's voters are so widely dispersed, this variable assumes they are spread in a symmetrical interval around the party's position. For example, for a leftmost party at -15 and a 0 midpoint between it and an adjacent party on the right, we assume the left boundary of that party's voters is -30." (McDonald 2002)

References

Kim, Heemin and Richard C. Fording (1998). "Voter ideology in western democracies, 1946-1989". In: European Journal of Political Research 33.1, 73-97. doi: 10.1111/1475-6765.00376.

Kim, Heemin and Richard C. Fording (2003). "Voter ideology in Western democracies: An update". In: European Journal of Political Research 42.1, 95-105.

McDonald, Michael D. (2002). Median Voters: 1950-1995. url: www2.binghamton.edu/political-science/research/MedianVoter.doc

Availability information for election programmes

Description

Availability information for election programmes

Usage

mp_availability(ids, apikey = NULL, cache = TRUE)

Arguments

Value

an object of class ManifestoAvailability containing availability information. Can be treated as a data.frame and contains detailed availability information per document

Examples

## Not run: 
mp_availability(countryname == "New Zealand")

wanted <- data.frame(party=c(41320, 41320), date=c(200909, 200509))
mp_availability(wanted)

## End(Not run)

Compute bootstrap distributions for scaling functions

Description

Bootstrapping of distributions of scaling functions as described by Benoit, Mikhaylov, and Laver (2009). Given a dataset with percentages of CMP categories, for each case the distribution of categories is resampled from a multinomial distribution and the scaling function computed for the resampled values. Arbitrary statistics of the resulting bootstrap distribution can be returned, such as standard deviation, quantiles, etc.

Usage

mp_bootstrap(
  data,
  fun = rile,
  col_filter = "^per(\\d{3}|\\d{4}|uncod)$",
  statistics = list(sd),
  N = 1000,
  ignore_na = TRUE,
  rescale = TRUE,
  ...
)

Arguments

References

Benoit, K., Laver, M., & Mikhaylov, S. (2009). Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions. American Journal of Political Science, 53(2), 495-513. http://doi.org/10.1111/j.1540-5907.2009.00383.x

Check for Updates of Corpus in Manifesto Project DB

Description

mp_check_for_copus_update checks if the currently cached version of corpus text and metadata is older than the most recent version available via the Manifesto Project DB API.

Usage

mp_check_for_corpus_update(apikey = NULL, only_stable = TRUE)

mp_which_corpus_version(cache_env = mp_cache())

mp_which_dataset_versions(cache_env = mp_cache())

mp_update_cache(apikey = NULL, only_stable = TRUE)

Arguments

Details

mp_update_cache checks if a new corpus version is available and loads the new version via: mp_use_corpus_version. That is, the internal cache of manifestoR will automatically be updated to newer version and all future calls to the API will request for the newer version. Note that updating/downgrading the corpus version after having already downloaded translated manifestos is not yet implemented and will result in an error message.

Note that this versioning applies to the corpus' texts and metadata, and not the versions of the core dataset. For this see mp_coreversions

Value

mp_update_cache returns a list with a boolean update_available and versionid, a character string identifying the most recent online version available

mp_which_corpus_version returns the current version id of the corpus and metadata stored in the cache

mp_which_dataset_versions returns the names of the main dataset versions which are in the cache, i.e. have been downloaded

mp_update_cache returns the character identifier of the version updated to

Print Manifesto Corpus citation information

Description

Print Manifesto Corpus citation information

Usage

mp_cite(
  corpus_version = mp_which_corpus_version(),
  core_versions = mp_which_dataset_versions(),
  apikey = NULL
)

Arguments

Programmatic clarity measures (PC)

Description

Computes party clarity measures suggested by Giebler/Lacewell/Regel/Werner 2015.

Usage

mp_clarity(
  data,
  weighting_kind = "manifesto",
  weighting_source = NULL,
  auto_rescale_weight = TRUE,
  auto_rescale_variables = TRUE,
  dimensions = clarity_dimensions()
)

Arguments

Value

a vector of clarity values

References

Giebler, Heiko, Onawa Promise Lacewell, Sven Regel and Annika Werner. 2015. Niedergang oder Wandel? Parteitypen und die Krise der repraesentativen Demokratie. In Steckt die Demokratie in der Krise?, ed. Wolfgang Merkel, 181-219. Wiesbaden: Springer VS.

Access to the Codebook for the Manifesto Project Main Dataset

Description

These functions provide access to machine- and human-readable versions of the Codebook (variable descriptions) of the Manifesto Project Main Dataset, as can be found in PDF form under https://manifesto-project.wzb.eu/datasets . As of this manifestoR release only the content-analytical variables (categories) are accessible. Note also that the codebook contains only condensed descriptions of the categories. For detailed information on coding instructions, you can refer to the different handbook versions under https://manifesto-project.wzb.eu/information/documents/handbooks . Only codebooks from version MPDS2017b on are accessible via the API.

Usage

mp_codebook(version = "current", cache = TRUE, chapter = "categories")

mp_describe_code(
  code,
  version = "current",
  columns = c("title", "description_md"),
  print = TRUE
)

mp_view_codebook(version = "current", columns = c("type", "code", "title"))

Arguments

Details

mp_codebook returns the codebook as a tibble, ideal for further automatic processing.

mp_describe_code pretty prints with information about the requested code(s), ideal for quick interactive use, but also returns invisible the code(s) information as a tibble

mp_view_codebook displays a searchable table version of the codebook in the Viewer pane.

List the available versions of the Manifesto Project's Main Dataset

Description

List the available versions of the Manifesto Project's Main Dataset

Usage

mp_coreversions(apikey = NULL, cache = TRUE, kind = "main")

Arguments

Details

For the available versions of the corpus, see mp_corpusversions

Examples

## Not run: mp_coreversions()

Get documents from the Manifesto Corpus Database

Description

Documents are downloaded from the Manifesto Project Corpus Database. If CMP coding annotations are available, they are attached to the documents, otherwise raw texts are provided. The documents are cached in the working memory to ensure internal consistency, enable offline use and reduce online traffic.

Usage

mp_corpus(
  ids,
  apikey = NULL,
  cache = TRUE,
  codefilter = NULL,
  codefilter_layer = "cmp_code",
  translation = NULL,
  as_tibble = FALSE,
  tibble_metadata = "simplified"
)

mp_corpus_df(
  ids,
  apikey = NULL,
  cache = TRUE,
  codefilter = NULL,
  codefilter_layer = "cmp_code",
  translation = NULL,
  tibble_metadata = "simplified"
)

mp_corpus_df_bilingual(
  ids,
  apikey = NULL,
  cache = TRUE,
  codefilter = NULL,
  codefilter_layer = "cmp_code",
  translation = "en",
  tibble_metadata = "simplified"
)

Arguments

Details

'mp_corpus_df' is a shorthand for getting the documents of the Manifesto Corpus as a tibble/data.frame object instead of a ManifestoCorpus object. It takes the same parameters as 'mp_corpus' (it is equivalent to mp_corpus(..., as_tibble = TRUE)). See mp_save_cache for ensuring reproducibility by saving cache and version identifier to the hard drive. See mp_update_cache for updating the locally saved content with the most recent version from the Manifesto Project Database API.

'mp_corpus_df_bilingual' is a shorthand for getting the original text and the english translations (or in case further translation languages become available also other translation languages than english) from the Manifesto Corpus as a tibble/data.frame object. The original text ends up in the "text" column and the english translation in "text_en" (or more abstract in case of further translation languages in a column named "text_<two digit ISO language code>"). It accepts the same additional parameters as 'mp_corpus_df'.

Value

an object of Corpus's subclass ManifestoCorpus holding the available of the requested documents

Examples

## Not run: 
corpus <- mp_corpus(party == 61620 & rile > 10)

wanted <- data.frame(party=c(41320, 41320), date=c(200909, 201309))
mp_corpus(wanted)

mp_corpus(subset(mp_maindataset(), countryname == "France"))

partially_available <- data.frame(party=c(41320, 41320), date=c(200909, 200509))
mp_corpus(partially_available)

corpus_df <- mp_corpus(party == 61620 & rile > 10, as_tibble = TRUE)
corpus_df <- mp_corpus_df(party == 61620 & rile > 10)
corpus_df <- mp_corpus_df(party == 61620 & rile > 10, tibble_metadata = "all")

mp_corpus(wanted, translation = "en")
mp_corpus_df(wanted, translation = "en")

mp_corpus_df_bilingual(wanted, translation = "en")

## End(Not run)

List the available versions of the Manifesto Project's Corpus

Description

The Manifesto Project Database API assigns a new version code whenever changes to the corpus texts or metadata are made.

Usage

mp_corpusversions(apikey = NULL, only_stable = TRUE)

Arguments

Details

This function always bypasses the cache.

Value

a data.table with the available version ids (name, tag)

Print manifestoR package dedication

Description

Print manifestoR package dedication

Usage

mp_dedication()

Value

mp_dedication returns the package dedication

Empty the manifestoR's cache

Description

Empty the manifestoR's cache

Usage

mp_emptycache()

Interpolate values within election periods

Description

As the Manifesto Project's variables are collected election-wise, values for the time/years in between elections are not naturally available. mp_interpolate allows to approximate them by several methods from the abjacent observations.

Usage

mp_interpolate(
  df,
  vars = "(^rile$)|(^per((\\d{3}(_\\d)?)|\\d{4})$)",
  by = "year",
  approx = zoo::na.approx,
  ...
)

Arguments

Examples

## Not run: 
mp_interpolate(mp_maindataset(), method = "constant")
mp_interpolate(mp_maindataset(), approx = na.spline, maxgap = 3)

## End(Not run)

Load manifestoR's cache

Description

Load a cache from a variable or file to manifestoR's current working environment.

Usage

mp_load_cache(cache = NULL, file = "mp_cache.RData")

Arguments

Examples

## Not run: mp_load_cache() ## loads cache from file "mp_cache.RData"

Access the Manifesto Project's Main Dataset

Description

Gets the Manifesto Project's Main Dataset from the project's web API or the local cache, if it was already downloaded before.

Usage

mp_maindataset(
  version = "current",
  south_america = FALSE,
  download_format = NULL,
  apikey = NULL,
  cache = TRUE
)

mp_southamerica_dataset(...)

Arguments

Details

mp_southamerica_dataset is a shorthand for getting the Manifesto Project's South America Dataset (it is equivalent to mp_maindataset(..., south_america = TRUE)). It is nowadays deprecated, for details see explanation in 'south_america' parameter documentation.

Value

The Manifesto Project Main Dataset with classes data.frame and tbl_df

Examples

## Not run: 
mpds <- mp_maindataset()
head(mpds)
median(subset(mpds, countryname == "Switzerland")$rile, na.rm = TRUE)

## End(Not run)
## Not run: 
mp_maindataset(download_format = "dta") %>% read_dta() ## requires package haven

## End(Not run)

Get meta data for election programmes

Description

Get meta data for election programmes

Usage

mp_metadata(ids, apikey = NULL, cache = TRUE)

Arguments

Details

Meta data contain information on the available documents for a given party and election date. This information comprises links to the text as well as original documents if available, language, versions checksums and more.

Value

an object of class ManifestoMetadata, subclassing data.frame as well as tbl_df and containing the requested metadata in rows per election programme

Examples

## Not run: 
mp_metadata(party == 21221)

wanted <- data.frame(party=c(41320, 41320), date=c(200909, 200509))
mp_metadata(wanted)

## End(Not run)

Party nicheness measures

Description

Computes party nicheness measures suggested by Bischof 2015 and Meyer and Miller 2013.

Usage

mp_nicheness(data, method = "bischof", ...)

nicheness_meyer_miller(
  data,
  groups = meyer_miller_2013_policy_dimensions(),
  transform = NULL,
  smooth = FALSE,
  weights = "pervote",
  party_system_normalization = TRUE,
  only_non_zero = TRUE
)

nicheness_bischof(
  data,
  out_variables = c("party", "date", "specialization", "nicheness", "nicheness_two"),
  groups = bischof_issue_groups(),
  diversification_bounds = c(0, rep(1/length(groups), length(groups)) %>% {
     -(. *
    log(.))
 } %>% sum()),
  smooth = function(x) {
     (x + lag(x, default = first(first(x))))/2
 }
)

Arguments

Details

List of possible outputs of nicheness_bischof:

diversification: Shannon's entropy $s_p$ in Bischof 2015

max_divers: used maximum for diversification

min_divers: used minimum for diversification

specialization: inverted diversification

specialization_stand: standardized specialization

nicheness: nicheness according to Meyer & Miller 2013 without vote share weighting

nicheness_stand: standardized nicheness

nicheness_two: sum of nicheness_stand and specialization_stand as proposed by Bischof 2015

References

Bischof, D. (2015). Towards a Renewal of the Niche Party Concept Parties, Market Shares and Condensed Offers. Party Politics.

Meyer, T.M., & Miller, B. (2013). The Niche Party Concept and Its Measurement. Party Politics 21(2): 259-271.

Baeck, H., Debus, M., & Dumont, P. (2010). Who gets what in coalition governments? Predictors of portfolio allocation in parliamentary democracies. European Journal of Political Research 50(4): 441-478.

Access to the Parties Lists for the Manifesto Project Data

Description

These functions provide access to machine-readable versions of the List of Parties of the Manifesto Project Data as can be found in CSV form under https://manifesto-project.wzb.eu/datasets . Note: the list of parties is not available for all of the past datasets. You can check the availability by going to the datasets page and check for specific datasets whether they have party lists in the following formats "List – Short (CSV)" or "List – Long (CSV)" available. There you can also find the codebooks with details for these list of parties.

Usage

mp_parties(
  version = "current",
  apikey = NULL,
  cache = TRUE,
  list_form = "short"
)

Arguments

Details

mp_parties returns the list of parties as a tibble, ideal for further automatic processing.

Relative measure of party size (RMPS)

Description

Computes the relative measure of party size as suggested by Giebler/Lacewell/Regel/Werner 2015.

Usage

mp_rmps(data, adapt_zeros = TRUE, ignore_na = TRUE, threshold_sum = 75)

Arguments

Details

Hint: In a dataset with multiple elections the usage of the function might require to calculate the measure per election (eg. using group_by)

Value

a vector of rmps values

References

Giebler, Heiko, Onawa Promise Lacewell, Sven Regel and Annika Werner. 2015. Niedergang oder Wandel? Parteitypen und die Krise der repraesentativen Demokratie. In Steckt die Demokratie in der Krise?, ed. Wolfgang Merkel, 181-219. Wiesbaden: Springer VS.

Save manifestoR's cache

Description

Saves manifestoR's cache to the file system. This function can and should be used to store downloaded snapshots of the Manifesto Project Corpus Database to your local hard drive. They can then be loaded via mp_load_cache. Caching data in the file system ensures reproducibility of the scripts and analyses, enables offline use of the data and reduces unnecessary traffic and waiting times.

Usage

mp_save_cache(file = "mp_cache.RData")

Arguments

Examples

## Not run: mp_save_cache() ## save to "mp_cache.RData" in current working directory

Scaling annotated manifesto documents

Description

Since scaling functions such as scale_weighted only apply to data.frames with code percentages, the function mp_scale makes them applies them to a ManifestoCorpus or ManifestoDocument.

Usage

mp_scale(
  data,
  scalingfun = rile,
  scalingname = as.character(substitute(scalingfun)),
  recode_v5_to_v4 = (scalingname %in% c("rile", "logit_rile")),
  ...
)

document_scaling(
  scalingfun,
  returndf = FALSE,
  scalingname = "scaling",
  recode_v5_to_v4 = FALSE,
  ...
)

corpus_scaling(
  scalingfun,
  scalingname = "scaling",
  recode_v5_to_v4 = FALSE,
  ...
)

Arguments

See Also

scale

Set the API key for the Manifesto Documents Database.

Description

If you do not have an API key for the Manifesto Documents Database, you can create one via your profile page on https://manifesto-project.wzb.eu. If you do not have an account, you can register on the webpage.

Usage

mp_setapikey(key.file = NULL, key = NA_character_)

Arguments

Details

The key is read from the file specified in key.file. If this argument is NULL, the key given in the argument key is used.

Use a specific version of the Manifesto Project Corpus

Description

The internal cache of manifestoR will be updated to the specified version and all future calls to the API will request for the specified version. Note that this versioning applies to the corpus' texts and metadata, and not the versions of the core dataset. For this see mp_coreversions. Also note that updating/downgrading the corpus version after having already downloaded translated manifestos is not yet implemented and will result in an error message.

Usage

mp_use_corpus_version(versionid, apikey = NULL)

Arguments

View original documents from the Manifesto Corpus Database

Description

Original documents are opened in the system's browser window. All original documents are stored on the Manifesto Project Website and the URLs opened are all from this site.

Usage

mp_view_originals(ids, maxn = 5, apikey = NULL, cache = TRUE)

Arguments

Examples

## Not run: 
mp_view_originals(party == 41320 & date == 200909)

## End(Not run)

Manifesto Project DB API request

Description

gets the requested url and passes HTTP header error codes on to raise R errors with the same text

Usage

mpdb_api_request(file, body)

Arguments

Replace NAs in vector with fixed value

Description

Replace NAs in vector with fixed value

Usage

na_replace(vec, value = 0L)

Arguments

Convert NULL to NA

Description

Convert NULL to NA

Usage

null_to_na(x)

Arguments

Value

NA if the element is NULL, the element otherwise

Prefix a string of text

Description

Convenience function to use with magrittr wraps paste0, hence vectorised as paste0

Usage

prefix(text, ...)

Arguments

Reader for ManifestoSource

Description

Reader for ManifestoSource

Usage

readManifesto(elem, language, id)

Arguments

Details

Used internally for constructing ManifestoCorpus objects. For the general mechanism refer to tms Reader documentation.

Process CMP codings

Description

Several functions to process the CMP codings

Usage

recode_cee_codes(x)

aggregate_cee_codes(x)

recode_v5_to_v4(x)

Arguments

Details

recode_cee_codes recode the sub-categories used in coding several manifestos in Central and Eastern Europe (4 digits) to the main categories in the coding scheme (3 digits).

recode_v5_to_v4 recode the CMP codings according to the more specialized Coding Handbook Version 5 to the more general categories of Handbook Version 4. Codes 202.2, 605.2 and 703.2 are converted to a 000, while all other subcategory codes with an appended dot and fourth digit are aggregated to the corresponding three-digit main category.

Replicates cases in a data.frame

Description

Replicates cases in a data.frame

Usage

## S3 method for class 'data.frame'
rep(x, times = 1, ...)

Arguments

Value

data.frame with cases replicated

Simple linear rescaling of positions

Description

Simple linear rescaling of positions

Usage

rescale(pos, newmin = -1, newmax = 1, oldmin = min(pos), oldmax = max(pos))

Arguments

RILE

Description

Computes the RILE or other bipolar linear scaling measures for each case in a data.frame or ManifestoCorpus or for a ManifestoDocument

Usage

rile(x)

logit_rile(x)

Arguments

Scaling functions

Description

Scaling functions take a data.frame of variables with information about political parties/text and position the cases on a scale, i.e. output a vector of values. For applying scaling functions directly to text documents, refer to mp_scale.

Usage

scale_weighted(
  data,
  vars = grep("per((\\d{3}(_\\d)?)|\\d{4}|(uncod))$", names(data), value = TRUE),
  weights = 1
)

scale_logit(data, pos, neg, N = data[, "total"], zero_offset = 0.5, ...)

scale_bipolar(data, pos, neg, ...)

scale_ratio_1(data, pos, neg, ...)

scale_ratio_2(data, pos, neg, ...)

Arguments

Details

scale_weighted scales the data as a weighted sum of the variable values

If variable names used for the definition of the scale are not present in the data frame they are assumed to be 0. scale_weighted scales the data as a weighted sum of the category percentages

scale_logit scales the data on a logit scale as described by Lowe et al. (2011).

scale_bipolar scales the data by adding up the variable values in pos and substracting the variable values in neg.

scale_ratio_1 scales the data taking the ratio of the difference of the sum of the variable values in pos and the sum of the variable values in neg to the sum of the variable values in pos and neg as suggested by Kim and Fording (1998) and by Laver & Garry (2000).

scale_ratio_2 scales the data taking the ratio of the sum of the variable values in pos and the sum of the variable values in neg.

References

Lowe, W., Benoit, K., Mikhaylov, S., & Laver, M. (2011). Scaling Policy Preferences from Coded Political Texts. Legislative Studies Quarterly, 36(1), 123-155.

Kim, H., & Fording, R. C. (1998). Voter ideology in western democracies, 1946-1989. European Journal of Political Research, 33(1), 73-97.

Laver, M., & Garry, J. (2000). Estimating Policy Positions from Political Texts. American Journal of Political Science, 44(3), 619-634.

See Also

mp_scale

Split Belgium party system into separate groups

Description

Recodes the country variable of a dataset to 218 (Flanders parties) and 219 (Wallonia parties) from 21 for Belgium

Usage

split_belgium(
  data,
  wallonia_parties = c(21111, 21322, 21422, 21423, 21425, 21426, 21522, 21911),
  brussels_parties = c(21424, 21912),
  belgium_parties = c(21320, 21420, 21520),
  flanders_parties = c(21112, 21221, 21321, 21330, 21421, 21430, 21521, 21913, 21914,
    21915, 21916, 21917),
  presplit_countrycode = 21,
  ...
)

Arguments

Lists of categories and category relations

Description

Code numbers of the Manifesto Project's category scheme. For documentation see https://manifesto-project.wzb.eu/datasets.

Usage

v4_categories()

v5_categories(include_parents = TRUE)

cee_categories()

v5_v4_aggregation_relations()

cee_aggregation_relations()

rile_r()

rile_l()

Arguments

Vanilla Scaling by Gabel & Huber

Description

Computes scores based on the Vanilla method suggested by Gabel & Huber. A factor analysis identifies the dominant dimension in the data. Factor scores using the regression method are then considered as party positions on this dominant dimension.

Usage

vanilla(
  data,
  vars = grep("per\\d{3}$", names(data), value = TRUE),
  invert = FALSE
)

Arguments

References

Gabel, M. J., & Huber, J. D. (2000). Putting Parties in Their Place: Inferring Party Left-Right Ideological Positions from Party Manifestos Data. American Journal of Political Science, 44(1), 94-103.