12 changed files with 233 additions and 127 deletions
+15 -7
View File
@@ -1,13 +1,21 @@
Package: hateimparlament
Title: Protocolanalysis of German Bundestag
Title: Recordanalysis Of Bundestag
Version: 0.0.0.9000
Authors@R:
person(given = "First",
family = "Last",
Authors@R: c(
person(given = "Leon",
family = "Burgard",
role = c("aut")),
person(given = "Josua",
family = "Kugler",
role = c("aut")),
person(given = "Christian",
family = "Merten",
role = c("aut", "cre"),
email = "first.last@example.com",
comment = c(ORCID = "YOUR-ORCID-ID"))
Description: Downloads, parses and analyses protocols of the current German parliament (Bundestag).
email = "christian@merten.dev"))
Description: Downloads, parses and analyses parliamentary records of the 19th legislative
period of the German parliament (Bundestag).
URL: https://git.flavigny.de/christian/hateimparlament
BugReports: https://git.flavigny.de/christian/hateimparlament/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
+1
View File
@@ -7,6 +7,7 @@ export(join_speaker)
export(party_colors)
export(read_all)
export(read_from_csv)
export(read_from_csv_or_fetch)
export(repair)
export(word_usage_by_date)
export(write_to_csv)
-7
View File
@@ -61,10 +61,3 @@ fetch_all <- function(download_dir="inst/records/", create=FALSE) {
# if successful, set progressbar to 100%
setTimerProgressBar(pb, 250)
}
stop_dir_not_creatable <- function(cond) {
# currently this has call: dir.create(download_dir)
# do we want to change this to fetch_all(...) ?
cond$message <- "Directory does not exist and can't be created. Probably because the path is not writeable."
stop(cond)
}
+7
View File
@@ -19,6 +19,13 @@ check_directory <- function(path, create=F) {
}
}
stop_dir_not_creatable <- function(cond) {
# currently this has call: dir.create(download_dir)
# do we want to change this to fetch_all(...) ?
cond$message <- "Directory does not exist and can't be created. Probably because the path is not writeable."
stop(cond)
}
# appends a file seperator at end of path if needed
make_directory_path <- function(path) {
if (!str_ends(path, .Platform$file.sep)) str_c(path, .Platform$file.sep)
+30 -1
View File
@@ -233,6 +233,11 @@ parse_speakerlist <- function(speakerliste_xml) {
#'
#' @export
write_to_csv <- function(tables, path="inst/csv/", create=F) {
is_valid_res(tables)
stopifnot("path must be of type character" = is.character(path))
stopifnot("create must be of type logical" = is.logical(create))
path <- make_directory_path(path)
check_directory(path, create)
write.table(tables$speaker, str_c(path, "speaker.csv"))
write.table(tables$speeches, str_c(path, "speeches.csv"))
@@ -250,6 +255,9 @@ write_to_csv <- function(tables, path="inst/csv/", create=F) {
#'
#' @export
read_from_csv <- function(path="inst/csv/") {
stopifnot("path must be of type character" = is.character(path))
path <- make_directory_path(path)
list(speaker = read.table(str_c(path, "speaker.csv")) %>%
tibble() %>%
mutate(id = as.character(id)),
@@ -259,5 +267,26 @@ read_from_csv <- function(path="inst/csv/") {
date = as.Date(date)),
talks = tibble %$% read.table(str_c(path, "talks.csv")),
comments = tibble %$% read.table(str_c(path, "comments.csv")),
applause = tibble %$% read.table(str_c(path, "applause.csv")))
applause = tibble %$% read.table(str_c(path, "applause.csv"))) -> res
is_valid_res(res)
res
}
#' @param path directory of csv files to read
#' read data from csv files if they exist already
#' otherwise fetch protocols and then write the data into csv files
#'
#' @export
read_from_csv_or_fetch <- function(path="inst/") {
path <- make_directory_path(path)
res <- tryCatch(read_from_csv(str_c(path, "csv/")),
error = function(c) NULL)
if (!is.null(res)) return(res)
fetch_all(str_c(path, "records/"), create=T)
read_all(str_c(path, "records/")) %>%
repair() ->
res
write_to_csv(res, str_c(path, "csv/"), create=T)
res
}
+137 -91
View File
@@ -1,19 +1,147 @@
# How to develop
# 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:
```r
remotes::install_url("https://git.flavigny.de/christian/hateimparlament/archive/master.zip")
```
If you want to build the vignettes, pass `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`:
```r
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 `tibble`s with
```r
res_raw <- read_all("records/") # path to directory where records are stored
```
`res_raw` is a named list with 5 `tibble`s:
### 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`:
```r
# everything works with devtools (loads some other packages too)
library(devtools)
# reload all package functions
```
When you changed something or added some functionality, you can reload all package functions with
```r
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:
#write to CSV files to speed up loading
```
tables <- read_all()
tables <- repair(tables)
write_to_csv(tables)
write_to_csv(tables, "path/to/csv/")
```
We NEVER use source(...), etc.! Also NEVER use library(...).
But to add new packages (as dependency), use:
Then later you can use
```r
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:
```r
use_package("my-good-old-package")
```
@@ -27,87 +155,5 @@ document()
Build vignettes
```r
rmarkdown::render("vignettes/bla.Rmd")
rmarkdown::render("vignettes/test.Rmd")
```
# Download
Before parsing, fetch.R must be run to download all protocols.
```r
fetch_all("../inst/records/") # path to directory where records should be stored
```
# Parsing
## tables
parse.R parses all downloaded logs and creates 5 tibbles.
repair.R then cleans up the errors in these tibbles.
```r
read_all("../inst/records/") %>% repair()
```
### Speaker
structure: `id` , `first_name` , `last_name` , `fraction` , `title` , `role_short`, `role_long`.
Obtained from the `<speaker list>` entry at the end of the transcripts.
### Speeches
Structure: `id` , `speaker`
The speeches `id` is specified in the protocol and is unique.A speech is a `<speech>` entry in the session history. A speech always has a main speaker (the one standing at the front of the lectern).
Within a speech, there can be different speech entries:
- Comments: Applause, interjections, etc.
- Speeches: Typically mainly the main speaker, but also interjections.
These are stored in the talks, comments and applause tables when parsing.
### Talks
Structure: `speech_id` , `speaker` , `content`.
These are the actual talk entries that appear within _speeches_.
- `speech_id`: the speech in which the contribution appears.
- `speaker`: The speaker of the speech entry.
- `content`: The content of the speech.
###comments
These are the interjections that appear during the speeches.
They have the following structure:
- `speech_id`: The speech that was interrupted.
- `on_speaker`: The speaker who was interrupted.
- `fraction`
- `commenter`: The person who interrupted the speech.
- `comment`: The content of the comment.
###applause
The logical table shows which party applauded for which speaker with explicit speech and which did not.
structure: `speech_id`, `on_speaker`, `CDU_CSU`, `SPD`, `FDP`, `DIE_LINKE`, `BUENDNIS_90_DIE_GRUENEN`, `AfD`
# Analysis
analysis.R provides some functions to analyze the "Plenarprotokolle" and to create plots.
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?"
- ...
+18 -3
View File
@@ -4,15 +4,30 @@
\name{hateimparlament-package}
\alias{hateimparlament}
\alias{hateimparlament-package}
\title{hateimparlament: Protocolanalysis of German Bundestag}
\title{hateimparlament: Recordanalysis Of Bundestag}
\description{
Downloads, parses and analyses protocols of the current German parliament (Bundestag).
Downloads, parses and analyses parliamentary records of the 19th legislative
period of the German parliament (Bundestag).
}
\details{
hateimparlament ist ein großartiges Paket!
}
\seealso{
Useful links:
\itemize{
\item \url{https://git.flavigny.de/christian/hateimparlament}
\item Report bugs at \url{https://git.flavigny.de/christian/hateimparlament/issues}
}
}
\author{
\strong{Maintainer}: First Last \email{first.last@example.com} (\href{https://orcid.org/YOUR-ORCID-ID}{ORCID})
\strong{Maintainer}: Christian Merten \email{christian@merten.dev}
Authors:
\itemize{
\item Leon Burgard
\item Josua Kugler
}
}
\keyword{internal}
+5 -4
View File
@@ -1,8 +1,8 @@
---
title: "explicittopic"
title: "Analysis of covered topics"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{explicittopic}
%\VignetteIndexEntry{Analysis of covered topics}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
@@ -34,9 +34,10 @@ read_all("../inst/records/") %>% repair() -> res
```
We also used `repair` to fix a bunch of formatting issues in the records.
For development purposes, we load the tables from csv files.
For development purposes, we only fetch records if they are not already
stored as csv files:
```{r}
res <- read_from_csv('../inst/csv/')
res <- read_from_csv_or_fetch('../inst/')
```
## Analysis
+7 -4
View File
@@ -1,8 +1,8 @@
---
title: "genderequality"
title: "Differences in gender"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{genderequality}
%\VignetteIndexEntry{Differences in gender}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
@@ -35,10 +35,13 @@ read_all("../records/") %>% repair() -> res
```
We also used `repair` to fix a bunch of formatting issues in the records.
For development purposes, we load the tables from csv files.
For development purposes, we only fetch records if they are not already
stored as csv files:
```{r}
res <- read_from_csv('../inst/csv/')
res <- read_from_csv_or_fetch('../inst/')
```
and unpack our tibbles
```{r}
comments <- res$comments
+5 -4
View File
@@ -1,8 +1,8 @@
---
title: "generalquestions"
title: "General questions"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{generalquestions}
%\VignetteIndexEntry{General questions}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
@@ -34,9 +34,10 @@ read_all("../inst/records/") %>% repair() -> res
```
We also used `repair` to fix a bunch of formatting issues in the records.
For development purposes, we load the tables from csv files.
For development purposes, we only fetch records if they are not already
stored as csv files:
```{r}
res <- read_from_csv('../inst/csv/')
res <- read_from_csv_or_fetch('../inst/')
```
## Analysis
+3 -2
View File
@@ -38,9 +38,10 @@ talks <- res$talks
We also used `repair` to fix a bunch of formatting issues in the records and unpacked
the result into more descriptive variables.
For development purposes, we load the tables from csv files.
For development purposes, we only fetch records if they are not already
stored as csv files:
```{r}
tables <- read_from_csv('../inst/csv/')
tables <- read_from_csv_or_fetch('../inst/')
comments <- tables$comments
speeches <- tables$speeches
+5 -4
View File
@@ -1,8 +1,8 @@
---
title: "interaction"
title: "Interaction between fractions"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{interaction}
%\VignetteIndexEntry{Interaction between fractions}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
@@ -34,9 +34,10 @@ read_all("../inst/records/") %>% repair() -> res
```
We also used `repair` to fix a bunch of formatting issues in the records.
For development purposes, we load the tables from csv files.
For development purposes, we only fetch records if they are not already
stored as csv files:
```{r}
res <- read_from_csv('../inst/csv/')
res <- read_from_csv_or_fetch('../inst/')
```
## Analysis