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When publishing work using the Manifesto Corpus, please make sure to cite it correctly and to give the identification number of the corpus version used for your analysis.
You can print citation and version information with the function mp_cite().
First of all, load the manifestoR package with the usual R syntax:
library(manifestoR)
To access the data in the Manifesto Corpus, an account for the Manifesto Project webpage with an API key is required. If you do not yet have an account, you can create one at https://manifesto-project.wzb.eu/signup. If you have an account, you can create and download the API key on your profile page.
For every R session using manifestoR and connecting to the Manifesto Corpus database,
you need to set the API key in your work environment.
This can be done by passing either a key or the name of a file containing the
key to manifestoR's mp_setapikey() function (see documentation ?mp_setapikey
for details).
Thus, your R script using manifestoR usually will start like this:
library(manifestoR)
mp_setapikey("manifesto_apikey.txt")
This R code presumes that you have stored and downloaded the API key in a file
name manifesto_apikey.txt in your current R working directory.
Note that it is a security risk to store the API key file or a script containing the key in public repositories.
You can download the Manifesto Project Dataset (MPDS) with the function mp_maindataset().
By default the most recent update is returned, but you can specify older versions
to get for reproducibility (type mp_coreversions() for a list of version and ?mp_maindataset
for usage information). For analysing the dataset using scaling functions, refer
to the section Using manifestoR's scaling functions below.
(Bulk-)Downloading documents works via the function mp_corpus(...).
It can be called with a logical expression specifying the subset of the Manifesto
Corpus that you want to download:
my_corpus <- mp_corpus(countryname == "Austria" & edate > as.Date("2000-01-01"))
## Connecting to Manifesto Project DB API...
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
my_corpus
## <<ManifestoCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 15
mp_corpus returns a ManifestoCorpus object, a subclass of Corpus as defined
in the natural language processing package tm (Feinerer & Hornik 2015).
Following tms logic, a ManifestoCorpus consists of ManifestoDocuments.
For both, corpus and documents, tm provides accessor functions to the corpus
and documents content and metadata:
head(content(my_corpus[[1]]))
## [1] "“Wir können heute die Existenzgrundlagen"
## [2] "künftiger Generationen zerstören."
## [3] "Oder sie sichern.”"
## [4] "Dr. Eva Glawischnig"
## [5] "Österreich braucht jetzt Weitblick."
## [6] "Nachhaltigkeit für zukünftige Generationen"
meta(my_corpus[[1]])
## manifesto_id : 42110_2002
## party : 42110
## date : 200211
## language : german
## source : MARPOR
## has_eu_code : FALSE
## is_primary_doc : TRUE
## may_contradict_core_dataset: TRUE
## md5sum_text : 04f07de517283243fdaaf0cbddc2a09e
## url_original : NA
## md5sum_original : NA
## annotations : TRUE
## id : 1
For more information on the available metadata per document, refer to the section
Using the document metadata below.
For more information on how to use the text mining functions provided by tm
for the data from the Manifesto Corpus, refer to the section
Processing and analysing the corpus documents below.
The variable names in the logical expression used for querying the corpus
database (countryname and edate in the example above) can be any column names from the
Manifesto Project's Main Dataset or your current R environment. The Main Dataset
itself is available in manifestoR via the funcion mp_maindataset():
mpds <- mp_maindataset()
print(head(names(mpds)))
## [1] "country" "countryname" "oecdmember" "eumember" "edate"
## [6] "date"
mp_corpus(rile > 60) ## another example of data set based corpus query
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## <<ManifestoCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 5
Alternatively, you can download election programmes on an individual basis
by listing combinations of party ids and election dates in a data.frame
and passing it to mp_corpus(...):
wanted <- data.frame(party=c(41220, 41320),
date=c(200909, 200909))
mp_corpus(wanted)
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## <<ManifestoCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 1
The party ids (41220 and 41320 in the example) are the ids as in the Manifesto Project's main dataset. They can be found in the current dataset documentation at https://manifesto-project.wzb.eu/datasets or in the main dataset.
Note that we received only 1 document, while querying for two.
This is because the party with the id 41220 (KPD) did not run for elections
in September 2009.
Also, not for every party and election data manifesto documents are availabe in the Manifesto Project Corpus.
You can check availability of your query beforehand with the function
mp_availability(...):
mp_availability(party == 41113)
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Queried for Documents found Coded Documents found
## 6 6 (100%) 5 (83.333%)
## Originals found Languages
## 6 (100%) 2 (german NA)
Downloaded documents are automatically cached locally. To learn about the caching mechanism read the section Efficiency and reproducibility: caching and versioning below.
Apart from the machine-readable, annotated documents, the Manifesto Corpus also
contains original layouted election programmes in PDF format. If available, they
can be viewed via the function mp_view_originals(...), which takes exactly the
format of arguments as mp_corpus(...) (see above), e.g.:
mp_view_originals(party == 41320 & date == 200909)
The original documents are shown in you system's web browser. All URLs opened
by this function refer only to the Manifesto Project's Website. If you want to
open more than 5 PDF documents at once, you have to specify the maximum number
of URLs allows to be opened manually via the parameter maxn. Since opening
URLs in an external browser costs computing resources on your local machine,
make sure to use only values for maxn that do not slow down or make your computer
unresponsive.
mp_view_originals(party > 41000 & party < 41999, maxn = 20)
As in tm, the textual content of a document is returned by the function content:
txt <- content(my_corpus[[2]])
class(txt)
## [1] "character"
head(txt, n = 4)
## [1] "1 Lebensqualität"
## [2] "1.1 Grüne Energiewende"
## [3] "Lebensqualität bedeutet in einer unversehrten Umwelt zu leben."
## [4] "Die Verantwortung dafür liegt bei uns: Wir alle gestalten Umwelt."
The central way for accessing the CMP codings is the accessor method codes(...).
It can be called on ManifestoDocuments and ManifestoCorpuss and returns a vector
of the CMP codings attached to the quasi-sentences of the document/corpus in a row:
doc <- my_corpus[[2]]
head(codes(doc), n = 15)
## [1] NA NA "501" "606" "501" "501" "501" "416" "416" "412" "503"
## [12] "411" "501" "416" NA
head(codes(my_corpus), n = 15)
## [1] "305" "305" "305" NA NA NA "601" "416" "416" "107" "107"
## [12] "107" "416" "416" "416"
Thus you can for example use R's functionality to count the codes or select quasi- sentences (units of texts) based on their code:
table(codes(doc))
##
## 104 105 106 107 108 109 201 202 203 303 305 401 402 403 408 409 411 412
## 3 9 2 52 36 11 36 17 1 3 1 2 6 20 1 1 38 17
## 413 416 501 502 503 504 506 601 604 605 606 607 608 701 703 704 706
## 1 13 62 48 83 24 46 14 20 9 10 15 5 33 13 9 32
doc_subcodes <- subset(doc, codes(doc) %in% c(202, 503, 607))
length(doc_subcodes)
## [1] 115
length(doc_subcodes)/length(doc)
## [1] 0.1489637
The CMP coding scheme can be found in the online documentation of the Manifesto Project dataset at https://manifesto-project.wzb.eu/coding_schemes/1.
Besides the main layer of CMP codings, you can create, store and access additional
layers of codings in ManifestoDocuments by passing a name of the coding layer
as additional argument to the function codes():
## assigning a dummy code of alternating As and Bs
codes(doc, "my_code") <- rep_len(c("A", "B"), length.out = length(doc))
head(codes(doc, "my_code"))
## [1] "A" "B" "A" "B" "A" "B"
You can view the names of the coding layers stored in a ManifestoDocument with
the function code_layers():
code_layers(doc)
## [1] "cmp_code" "eu_code" "my_code"
Note that certain documents downloaded from the Manifesto Corpus Database already
have a second layer of codes named eu_code. These are codes that have been assigned
to quasi-sentences by CMP coders additionally to the main CMP code to indicate
policy statements that should or should not be implemented on the level of the
European union. The documents that were coded in this way are marked in the
corpus' metadata with the flag has_eu_code
(see below Using the document metadata).
Note that, since these codes also have been used for computing the per and rile
variables in the Manifesto Project Main Dataset, they are also used in manifestoRs
count_codes and rile functions (see below Scaling texts) if the respective metadata flag is present.
Since the Manifesto Corpus uses the infrastructure of the tm package
(Feinerer & Hornik 2015), all of tms filtering and transformation functionality
can be applied directly to the downloaded ManifestoCorpus.
For example, standard natural language processors are available to clean the corpus:
head(content(my_corpus[[3]]))
## [1] "1. SONNE STATT ÖL: WIR HELFEN BEIM SPAREN"
## [2] "Der Umstieg hat begonnen."
## [3] "Die Menschen in Österreich fahren weniger Auto"
## [4] "und mehr mit dem öffentlichen Verkehr"
## [5] "und dem Rad."
## [6] "Sie sanieren Häuser und Wohnungen"
corpus_cleaned <- tm_map(my_corpus, removePunctuation)
corpus_nostop <- tm_map(corpus_cleaned, removeWords, stopwords("german"))
head(content(corpus_nostop[[3]]))
## [1] "1 SONNE STATT ÖL WIR HELFEN BEIM SPAREN"
## [2] "Der Umstieg begonnen"
## [3] "Die Menschen Österreich fahren weniger Auto"
## [4] " mehr öffentlichen Verkehr"
## [5] " Rad"
## [6] "Sie sanieren Häuser Wohnungen"
So is analysis in form of term document matrices:
tdm <- TermDocumentMatrix(corpus_nostop)
inspect(tdm[c("menschen", "wahl", "familie"),])
## <<TermDocumentMatrix (terms: 3, documents: 15)>>
## Non-/sparse entries: 36/9
## Sparsity : 20%
## Maximal term length: 8
## Weighting : term frequency (tf)
##
## Docs
## Terms 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## menschen 65 41 20 15 78 24 50 38 0 6 47 49 27 8 3
## wahl 2 0 3 3 2 0 0 1 0 0 2 0 1 1 0
## familie 2 4 2 0 2 3 2 17 3 1 20 20 12 4 6
findAssocs(tdm, "stadt", 0.97) ## find correlated terms, see ?tm::findAssocs
## $stadt
## schrittweise auszubauen erfordert
## 0.99 0.98 0.98
## övp pflegeberufe denkmalschutz
## 0.98 0.98 0.97
## dienstes geprüft nonprofitorganisationen
## 0.97 0.97 0.97
For more information about the functionality provided by the tm,
please refer to its documentation.
For applications in which not the entire text of a document is of interest, but
rather a subset of the quasi-sentences matching certain criteria,
manifestoR provides a function subset(...) working just like R's internal
subset function.
It can, for example, be used to filter quasi-sentences based on codes or the text:
# subsetting based on codes (as example above)
doc_subcodes <- subset(doc, codes(doc) %in% c(202, 503, 607))
length(doc_subcodes)
## [1] 115
# subsetting based on text
doc_subtext <- subset(doc, grepl("Demokratie", content(doc)))
head(content(doc_subtext), n = 3)
## [1] "Eine Demokratie benötigt auch die Unterstützung von Forschung jenseits wirtschaftlicher Interessen."
## [2] "In einer Demokratie sollen all jene wählen dürfen, die von den politischen Entscheidungen betroffen sind."
## [3] "Demokratie braucht die Teilhabe der BürgerInnen."
head(codes(doc_subtext), n = 10)
## [1] "506" "202" "202" "201" "108" NA "202" "107"
Via tm_map the filtering operations can also be applied to an entire corpus:
corp_sub <- tm_map(my_corpus, function(doc) {
subset(doc, codes(doc) %in% c(202, 503, 607))
})
head(content(corp_sub[[3]]))
## [1] "Das hat einen einzigen Grund: die hohen Öl- und Gaspreise."
## [2] "Immer mehr Menschen können sich Heizung"
## [3] "und Mobilität immer weniger leisten."
## [4] "Ob wir das wollen oder nicht – Erdöl und Erdgas werden weiter teurer."
## [5] "Wir verbrennen Milliarden in unseren Tanks und Öfen,"
## [6] "und: SPAREN STATT VERSCHWENDEN."
head(codes(corp_sub))
## [1] "503" "202" "202" "503" "503" "503"
For convenience, it is also possible to filter quasi-sentences with specific
codes directly when downloading a corpus. For this, the additional argument
codefilter with a list of CMP codes of interest is passed to mp_corpus:
corp_sub <- mp_corpus(countryname == "Australia", codefilter = c(103, 104))
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
head(content(corp_sub[[1]]))
## [1] "The pursuit of military and economic dominance by the United States at the expense of international law and human rights is destabilising the world."
## [2] "and Iraqis allowed their self-determination."
## [3] "while maintaining an adequate defence force"
head(codes(corp_sub))
## [1] "103" "103" "104" "104" "103" "103"
Each document in the Manifesto Corpus has meta information about itself attached.
They can be accessed via the function meta:
meta(doc)
## manifesto_id : 42110_2006
## party : 42110
## date : 200610
## language : german
## source : MARPOR
## has_eu_code : FALSE
## is_primary_doc : TRUE
## may_contradict_core_dataset: FALSE
## md5sum_text : 37744e88ed32bbf176656883526fc56c
## url_original : /down/originals/42110_2006.pdf
## md5sum_original : CURRENTLY_UNAVAILABLE
## annotations : TRUE
## id : 2
It is possible to access and also modify specific metadata entries:
meta(doc, "party")
## [1] 42110
meta(doc, "manual_edits") <- TRUE
meta(doc)
## manifesto_id : 42110_2006
## party : 42110
## date : 200610
## language : german
## source : MARPOR
## has_eu_code : FALSE
## is_primary_doc : TRUE
## may_contradict_core_dataset: FALSE
## md5sum_text : 37744e88ed32bbf176656883526fc56c
## url_original : /down/originals/42110_2006.pdf
## md5sum_original : CURRENTLY_UNAVAILABLE
## annotations : TRUE
## id : 2
## manual_edits : TRUE
Document metadata can also be bulk-downloaded with the function mp_metadata,
taking the same set of parameters as mp_corpus:
metas <- mp_metadata(countryname == "Spain")
## Connecting to Manifesto Project DB API... corpus version: 2015-4
head(metas)
## Source: local data frame [6 x 12]
##
## party date language source has_eu_code is_primary_doc
## (dbl) (dbl) (chr) (chr) (lgl) (lgl)
## 1 33908 201111 galician MARPOR FALSE TRUE
## 2 33907 201111 spanish MARPOR FALSE TRUE
## 3 33905 201111 catalan MARPOR FALSE TRUE
## 4 33902 201111 spanish MARPOR FALSE TRUE
## 5 33611 201111 catalan MARPOR FALSE TRUE
## 6 33610 201111 spanish MARPOR FALSE TRUE
## Variables not shown: may_contradict_core_dataset (lgl), manifesto_id
## (chr), md5sum_text (chr), url_original (chr), md5sum_original (chr),
## annotations (lgl)
The field …
party contains the party id from the Manifesto Project Dataset.date contains the month of the election in the same format as in the
Manifesto Project Dataset (YYYYMM)language specifies the language of the document as a word.is_primary_doc is FALSE only in cases where for a single party and
election date multiple manifestos are available and this is the document not used
for coding by the Manifesto Project.may_contradict_core_dataset is TRUE for documents where the CMP codings
in the corpus documents might be inconsistent with the coding aggregates in the
Manifesto Project's Main Dataset. This applies to manifestos which have been either
recoded after they entered the dataset or cases where the dataset entries are
derived from hand-written coding sheets used prior to the digitalization of the
Manifesto Project's data workflow, but the documents were digitalized and added
to the Manifesto Corpus afterwards.annotations is TRUE whenenver there are CMP codings available for the document.has_eu_code marks document in which the additional coding layer eu_code is present. These codes have been assigned to quasi-sentences by CMP coders additionally to the main CMP code to indicate policy statements that should or should not be implemented on the level of the European union.The other metadata entries have primarily technical functions for communication
between the manifestoR package and the online database.
To save time and network traffic, manifestoR caches all downloaded data and
documents in your computer's working memory and connects to the online database
only when data is required that has not been downloaded before.
corpus <- mp_corpus(wanted)
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
subcorpus <- mp_corpus(wanted[3:7,])
Note that in the second query no message informing about the connection to the Manifesto Project's Database is printed, since no data is actually downloaded.
This mechanism also ensures reproducibility of your scripts, analyses and results: executing your code again will yield the same results, even if the Manifesto Project's Database is updated in the meantime. Since the cache is only stored in the working memory, however, in order to ensure reproducibility across R sessions, it is advisable to save the cache to the hard drive at the end of analyses and load it in the beginning:
mp_save_cache(file = "manifesto_cache.RData")
## ... start new R session ... then:
library(manifestoR)
mp_setapikey("manifesto_apikey.txt")
mp_load_cache(file = "manifesto_cache.RData")
This way manifestoR always works with the same snapshot of the Manifesto Project
Database and Corpus, saves a lot of unnecessary online traffic and also enables
you to continue with your analyses offline.
Each snapshot of the Manifesto Corpus is identified via a version number, which is stored in the cache together with the data and can be accessed via
mp_which_corpus_version()
## [1] "2015-4"
When collaborating on a project with other researchers, it is advisable to use
the same corpus version for reproducibility of the results.
manifestoR can be set to use a specific version with the functions
mp_use_corpus_version("2015-3")
## Connecting to Manifesto Project DB API... corpus version: 2015-3
In order to guarantee reproducibility of published work, please also mention the corpus version id used for the reported analyses in the publication.
For updating locally cached data to the most recent version of the
Manifesto Project Corpus, manifestoR provides two functions:
mp_check_for_corpus_update()
## $update_available
## [1] TRUE
##
## $versionid
## [1] "2015-4"
mp_update_cache()
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## [1] "2015-4"
mp_check_for_corpus_update()
## $update_available
## [1] FALSE
##
## $versionid
## [1] "2015-4"
For more detailed information on the caching mechanism and on how to use and load
specific snapshots of the Manifesto Corpus, refer to the R documentations of the
functions mentioned here as well mp_use_corpus_version, mp_corpusversions,
mp_which_corpus_version.
If required ManifestoCorpus as well as ManifestoDocument objects can be
converted to R's internal data.frame format and processed further:
doc_df <- as.data.frame(doc)
head(within(doc_df, {
## for pretty printing
text <- paste0(substr(text, 1, 60), "...")
}))
## text cmp_code
## 1 1 Lebensqualität... <NA>
## 2 1.1 Grüne Energiewende... <NA>
## 3 Lebensqualität bedeutet in einer unversehrten Umwelt zu lebe... 501
## 4 Die Verantwortung dafür liegt bei uns: Wir alle gestalten Um... 606
## 5 Ein Umdenken in der Energiepolitik ist eine wesentliche Vora... 501
## 6 Wir Grüne stehen für eine Energiewende hin zu einem Aufbruch... 501
## eu_code my_code pos
## 1 <NA> A 1
## 2 <NA> B 2
## 3 <NA> A 3
## 4 <NA> B 4
## 5 <NA> A 5
## 6 <NA> B 6
The function also provides a parameter to include all available metadata in the export:
doc_df_with_meta <- as.data.frame(doc, with.meta = TRUE)
print(names(doc_df_with_meta))
## [1] "text" "cmp_code"
## [3] "eu_code" "my_code"
## [5] "pos" "manifesto_id"
## [7] "party" "date"
## [9] "language" "source"
## [11] "has_eu_code" "is_primary_doc"
## [13] "may_contradict_core_dataset" "md5sum_text"
## [15] "url_original" "md5sum_original"
## [17] "annotations" "id"
## [19] "manual_edits"
For more information on the available metadata per document, refer to the section Using the document metadata above.
Note again that also all functionality provided by tm, such as writeCorpus
is available on a ManifestoCorpus.
Scaling of political content refers to the estimation of its location in a policy
space (Grimmer & Stewart 2013). manifestoR provides several functions to scale coded documents by
known routines such as the RILE measure (see sections
Using manifestoR's scaling functions),
as well as infrastructure to create new scales (see section Writing custom scaling functions) and statistical analysis routines for the
distributions of scaling functions (see section Bootstrapping scaling function distributions and standard errors).
Implementationwise, a scaling function in manifestoR takes a data.frame of cases
and outputs a position value for each case. The Manifesto Project Dataset (MPDS)
can be downloaded in manifestoR using the function mp_maindataset() (see section
Downloading the Manifesto Project Dataset above). Then you can e.g. compute the RILE scores of cases from the main dataset by calling:
mpds <- mp_maindataset()
## Connecting to Manifesto Project DB API...
## Connecting to Manifesto Project DB API... corpus version: 2015-4
rile(subset(mpds, countryname == "Albania"))
## [1] 1.592900e+01 -1.146300e+01 1.027400e+01 1.111100e+01 7.176000e+00
## [6] 1.792300e+01 5.405000e+00 5.882000e+00 -7.298000e+00 -1.354000e+01
## [11] 6.012000e+00 4.232200e+01 1.431200e+01 2.220446e-16 -9.090000e+00
## [16] -9.350000e-01 -2.187000e+00 -9.180000e-01 5.596200e+01 -1.304900e+01
## [21] 8.059000e+00 9.919000e+00 -4.166000e+00 7.760000e-01 5.710000e-01
## [26] -2.187000e+00 -9.180000e-01 5.596200e+01 2.718500e+01 2.428600e+01
## [31] 9.919000e+00 -4.166000e+00 2.247200e+01 5.710000e-01 -2.187000e+00
## [36] -9.180000e-01 -4.166000e+00 2.247200e+01
What variables are used from the input data depends on the scaling function. All currently implemented functions use only the percentages of coded categories, in the form of variables starting with “per” as in the Manifesto Project Dataset. The following functions are currently available:
rilelogit_rilevanillaTo apply scaling functions directly to coded documents or corpora
you can use the function mp_scale. It takes a ManifestoCorpus
or ManifestoDocument and returns the scaled positions for each document:
corpus <- mp_corpus(countryname == "Romania")
## Connecting to Manifesto Project DB API... corpus version: 2015-4
## Connecting to Manifesto Project DB API... corpus version: 2015-4
mp_scale(corpus, scalingfun = logit_rile)
## party date logit_rile
## 1 93031 201212 0.7176668
## 2 93061 201212 -0.4813032
## 3 93981 201212 -0.7845814
Writing custom scaling functions for texts in manifestoR is easy, since it
requires nothing more than writing a function that takes a data.frame of cases
as input and returns a vector of values. mp_scale provides the mechanism
that converts a coded text to a data.frame with “per” variables such that your function
can handle it:
custom_scale <- function(data) {
data$per402 - data$per401
}
mp_scale(corpus, scalingfun = custom_scale)
## party date custom_scale
## 1 93031 201212 1.558442
## 2 93061 201212 3.296703
## 3 93981 201212 2.962963
In addition, manifestoR provides several function templates you can use for
creating scales, e.g. a weighted sum of per variables
(scale_weighted), the logit ratio of category counts (scale_logit)
or ratio scaling as suggested by Kim and Fording (1998) and by Laver & Garry (2000)
(scale_ratio). For example, the ratio equivalent to the simple function
above can be implemented as:
custom_scale <- function(data) {
scale_ratio(data, pos = c("per402"), neg = c("per401"))
}
mp_scale(corpus, scalingfun = custom_scale)
## party date custom_scale
## 1 93031 201212 1.857143
## 2 93061 201212 4.000000
## 3 93981 201212 2.333333
For details on these template functions, their parameters and how to use them,
see the R documentation ?scale.
In order to better evaluate the significance of analysis results based on
scaled coded texts, Benoit, Mikhaylov, and Laver (2009) proposed to approximate
the standard errors of the scale variable by bootstrapping its distribution.
This procedure is available via the function mp_bootstrap:
data <- subset(mpds, countryname == "Albania")
mp_bootstrap(data, fun = rile)
## Source: local data frame [38 x 2]
##
## rile sd
## (dbl) (dbl)
## 1 15.929 7.496183
## 2 -11.463 4.248530
## 3 10.274 4.796508
## 4 11.111 7.926599
## 5 7.176 4.239248
## 6 17.923 6.017582
## 7 5.405 4.351554
## 8 5.882 7.190107
## 9 -7.298 4.095676
## 10 -13.540 6.968436
## .. ... ...
Note that the argument fun can be any scaling function and the returned data.frame
containes the scaled position as well as the bootstrapped standard deviations.
Also, with the additional parameters statistics, you can compute arbitrary statistics
from the bootstrapped distribution, such as variance or quantiles:
custom_scale <- function(data) {
data$per402 - data$per401
}
mp_bootstrap(data,
fun = custom_scale,
statistics = list(var, 0.025, 0.975))
## Source: local data frame [38 x 4]
##
## custom_scale var q0.025 q0.975
## (dbl) (dbl) (dbl) (dbl)
## 1 0.885 2.5923261 -2.654867 4.424779
## 2 -0.395 0.1557837 -1.185771 0.000000
## 3 0.685 1.3530588 -1.369863 3.424658
## 4 -1.111 1.1774429 -3.333333 0.000000
## 5 0.000 2.7397503 -3.349282 3.349282
## 6 0.000 1.8868771 -2.830189 2.830189
## 7 -1.081 1.0384061 -3.243243 1.081081
## 8 -5.882 17.6819034 -14.705882 0.000000
## 9 0.000 0.5075712 -1.459854 1.459854
## 10 3.125 3.0498283 0.000000 7.291667
## .. ... ... ... ...
For a more detailed reference and complete list of the functions provided
by manifestoR, see the R package reference manual on CRAN:
http://cran.r-project.org/web/packages/manifestoR/manifestoR.pdf
You can get in touch with the Manifesto Project team by e-mailing to
manifesto-communication@wzb.eu.
We are happy to receive your feedback and answer questions about the Manifesto
Corpus, including errors or obscurities in the corpus documents. In this case
please make sure to include the party id, election date and the corpus version
you were working with (accessible via mp_which_corpus_version).
For general questions about the Project and dataset, please check the
Frequently Asked Questions section
on our website first.
We welcome bug reports, feature requests or (planned) source code contributions for the
manifestoR package. For all of these, best refer to our repository on github:
https://github.com/ManifestoProject/manifestoR.
For more information, please refer to the Section “Developing” in the README file
of the github repository.
\newpage
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