hateimparlament/README.md

Description

R package to analyze parliamentary records of the 19th legislative period of the Bundestag, the German parliament.

Installation

Using the remotes package, this is easily installed via:

remotes::install_url("https://git.flavigny.de/christian/hateimparlament/archive/master.zip")

Since the fetching and reading is very slow and depends on an internet connection, all vignettes use read_from_csv to read already parsed tibbles from .csv files.

That's why, if you want to build the vignettes yourself, you need to download the source code, e.g. on Linux

git clone https://git.flavigny.de/christian/hateimparlament
cd hateimparlament

then start R and do

devtools::load_all()
fetch_all(create = TRUE)
read_all() %>% repair() -> res
write_to_csv(res, create = TRUE)

Then finally, do:

devtools::install(build_vignettes = TRUE)

Features

The package mainly supplies 4 functionalities:

Download records

To analyze records, they need to be downloaded. This is done with fetch_all:

fetch_all("records/", create = TRUE) # path to directory where records should be stored

This downloads all parliamentary records and stores them as .xml files in the given directory.

Parse records

To use the records in R, they are converted to tibbles with

res_raw <- read_all("records/") # path to directory where records are stored

res_raw is a named list with 5 tibbles:

Speaker

Table of all speakers of this legislative period.

Fields:

• id: Unique speaker id
• prename: Prename
• lastname: Surname
• fraction: Name of fraction if the speaker is member of parliament.
• title: Title, e.g. ,,Prof’’
• role_short: Short name of role, e.g. ,,Bundeskanzlerin’’
• role_long: Long name of role

Speeches

Table of all speeches given during this legislative period.

Fields:

• id: Unique speech id
• speaker: Principal speaker (the person standing behind the lectern during the speech).
• date: Date of session

Talks

Within a speech, there can be multiple talks by different people. Mostly this is the main speech by the principal speaker, but usually there are questions by other members of parliament or order calls by the president of the Bundestag.

Fields:

• speech_id: Speech in which this talk has been given
• speaker: Person that actually talks
• content: Spoken content

Comments

These are the interjections that appear during the speeches.

Fields:

• speech_id: The speech that was interrupted
• on_speaker: The speaker who was interrupted
• fraction: The fraction of the commenter
• commenter: The person who interrupted the speech
• comment: The content of the comment

Applause

Table containing all the rounds of applause that happened during this legislative period.

Fields:

• speech_id: Speech during which was applauded
• on_speaker: Speaker who was applauded

And then logical fields CDU_CSU, SPD, FDP, DIE_LINKE, BUENDNIS_90_DIE_GRUENEN, AfD for every fraction in the Bundestag, signifying whether this fraction applauded.

Repair records

The parliamentary records usually contain some major and minor formatting issues. These are mostly resolved by using

res <- repair(res_raw)

By passing lookup_speaker = TRUE, even commenters in res_raw$comments are matched with their respective speaker id.

Analysis

Also some functions are provided to analyze the parliamentary records and draw some plots:

• bar_plot_fractions
• find_word
• join_speaker
• word_usage_by_date

See their usage with the ? operator.

In the vignettes you can find different analyses of the protocols, for example:

• “Who talks the most?”
• “Which party gives the most speeches?”
• “Which party comments the most on which parties?”
• “When are which topics discussed the most?”
• ...

Contributing

Developing works the easiest with devtools:

library(devtools)

When you changed something or added some functionality, you can reload all package functions with

load_all()

If you want to avoid reading all records every time you start a new R session, you can write your parsed tibbles to CSV files:

tables <- read_all()
tables <- repair(tables)
write_to_csv(tables, "path/to/csv/")

Then later you can use

res <- read_from_csv("path/to/csv/")

to load your stored tibbles very fast.

NEVER use source(...), etc.! Also NEVER use library(...). To add new packages (as dependency), use:

use_package("my-good-old-package")

To make package imports available, you have to add them to R/hateimparlament-package.R as @import <package>.

To reload / create documentation (calls roxygen)

document()

Build vignettes

rmarkdown::render("vignettes/test.Rmd")