Merge pull request 'add masterdata from bundestag.de and use this for genderequality' (#16 ) from genderequality-alternative into master

Merge branch 'master' into genderequality-alternative
change title of vignettes, and add install instructions for vignettes
2021-08-11 08:30:27 +02:00 · 2021-08-10 23:28:25 +02:00 · 2021-08-10 22:47:07 +02:00 · 2021-08-10 22:46:41 +02:00 · 2021-08-10 22:03:37 +02:00 · 2021-08-10 21:51:00 +02:00
50 changed files with 249910 additions and 365 deletions
@@ -2,3 +2,4 @@
 ^Meta$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$
@@ -1,10 +1,12 @@
 *.xml
 /doc/
 /Meta/
-/reports/
+/inst/reports/
-!/reports/*.pdf
+!/inst/reports/*.pdf
-!/reports/*.tex
+!/inst/reports/*.tex
-/csv/*
+/inst/csv/*
 /parlament_49_53_texts/
 .Rproj.user
 *.Rproj
 *.RData
 *.Rhistory
@@ -1,26 +1,36 @@
 Package: hateimparlament
-Title: Protocolanalysis of German Bundestag
+Title: Recordanalysis Of Bundestag
 Version: 0.0.0.9000
-Authors@R: 
+Authors@R: c(
-    person(given = "First",
+    person(given = "Leon",
-           family = "Last",
+           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",
+           email = "christian@merten.dev"))
-           comment = c(ORCID = "YOUR-ORCID-ID"))
+Description: Downloads, parses and analyses parliamentary records of the 19th legislative
-Description: Downloads, parses and analyses protocols of the current German parliament (Bundestag).
+    period of the German parliament (Bundestag).
-License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
+URL: https://git.flavigny.de/christian/hateimparlament
-    license
+BugReports: https://git.flavigny.de/christian/hateimparlament/issues
 License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.1
 Imports: 
    dplyr,
    lubridate,
    pbapply,
    purrr,
    rlang,
    rvest,
    stringr,
    tibble,
    tidyr,
    xml2
 Suggests: 
    rmarkdown,
@@ -0,0 +1,595 @@
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 and each file should have at least the “copyright” line and a pointer to
 where the full notice is found.
    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) <year>  <name of author>
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
 Also add information on how to contact you by electronic and paper mail.
 If the program does terminal interaction, make it output a short notice like this
 when it starts in an interactive mode:
    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type 'show c' for details.
 The hypothetical commands `show w` and `show c` should show the appropriate parts of
 the General Public License. Of course, your program's commands might be different;
 for a GUI interface, you would use an “about box”.
 You should also get your employer (if you work as a programmer) or school, if any, to
 sign a “copyright disclaimer” for the program, if necessary. For more
 information on this, and how to apply and follow the GNU GPL, see
 &lt;<http://www.gnu.org/licenses/>&gt;.
 The GNU General Public License does not permit incorporating your program into
 proprietary programs. If your program is a subroutine library, you may consider it
 more useful to permit linking proprietary applications with the library. If this is
 what you want to do, use the GNU Lesser General Public License instead of this
 License. But first, please read
 &lt;<http://www.gnu.org/philosophy/why-not-lgpl.html>&gt;.
@@ -1,16 +1,20 @@
 # Generated by roxygen2: do not edit by hand
 export(bar_plot_fractions)
 export(fetch_all)
 export(find_word)
-export(join_redner)
+export(join_speaker)
 export(party_colors)
 export(read_all)
 export(read_from_csv)
 export(repair)
 export(word_usage_by_date)
 export(write_to_csv)
 import(dplyr)
 import(pbapply)
 import(purrr)
 import(stringr)
 import(tibble)
 import(tidyr)
 import(utils)
 import(xml2)
@@ -1,31 +1,184 @@
 #' Count number of occurences of a given word
 #' 
 #' @param res tibble
 #' @param word character
 #'
 #' Add number of occurences of word to talks
 #' 
 #' @export
 find_word <- function(res, word) {
    is_valid_res(res)
    stopifnot("word must be of type character" = is.character(word))
    talks <- res$talks
-    mutate(talks, occurences = sapply(str_match_all(talks$content, regex(word, ignore_case = TRUE)),
+    mutate(
-           nrow))
+        talks, 
        occurences = sapply(
            str_match_all(talks$content, regex(word, ignore_case = TRUE)),
            nrow
        )
    )
 }
 #' add information from speaker table to a tibble containing speaker id
 #' 
 #' @param tb tibble
 #' @param res list of tibbles
 #' @param fraction_only if TRUE, only select fraction from the resulting joined tibble
 #' 
 #' left join speaker information from res$speaker into tb.
 #' if fraction_only, drop all columns but fraction
 #'
 #' @export
-join_redner <- function(tb, res, fraktion_only = F) {
+join_speaker <- function(tb, res, fraction_only = F) {
-    joined <- left_join(tb, res$redner, by=c("redner" = "id"))
+    is_valid_res(res)
-    if (fraktion_only) select(joined, "fraktion")
+    stopifnot("fraction_only must be of type logical" = is.logical(fraction_only))
    stopifnot("tb must be a tibble" = inherits(tb, "tbl"))
    stopifnot("tb must have a speaker column" = "speaker" %in% names(tb))
    joined <- left_join(tb, res$speaker, by=c("speaker" = "id"))
    if (fraction_only) select(joined, "fraction")
    else joined
 }
 #' lookup table for official party colors
 #'
 #' @export
 party_colors <- c(
  SPD="#DF0B25",
  "CDU/CSU"="#000000", 
  AfD="#1A9FDD",
  "AfD&Fraktionslos"="#1A9FDD",
  "DIE LINKE"="#BC3475",
  "BÜNDNIS 90 / DIE GRÜNEN"="#4A932B",
  FDP="#FEEB34",
-  Fraktionslos="#FEEB34"
+  "CDU/CSU"="#000000", 
  SPD="#DF0B25",
  "B\u00DCNDNIS 90/DIE GR\u00DCNEN"="#4A932B",
  "DIE LINKE"="#BC3475",
  "AfD&Fraktionslos"="#AAAAFF",
  Fraktionslos="#AAAAAA"
 )
 party_order <- factor(c("Fraktionslos", "AfD&Fraktionslos",
                        "DIE LINKE", "B\u00DCNDNIS 90/DIE GR\u00DCNEN", "SPD", "CDU/CSU",
                        "FDP", "AfD", NA_character_))
 #' Bar chart visualizing fraction based data
 #' 
 #' Can be configured to also visualize data not related to fractions.
 #' 
 #' @param tb tibble
 #' @param x_variable column in tb, default is fraction
 #' @param y_variable column in tb, default is n
 #' @param fill column in tb, default is fraction
 #' @param title plot title
 #' @param xlab label for x axis, default is fraction
 #' @param ylab label for y axis, default is n
 #' @param filllab default is 'Fraction'
 #' @param flipped if TRUE draw bars horizontally, else vertically. Default is TRUE
 #' @param position default is 'dodge'
 #' @param reorder Either reorder fraction factor by variable value or reorder fraction factor by party seat order in parliament (default).
 #' @param rotatelab Default is FALSE. If true turns the labels 90 degrees to the axis.
 #' 
 #' plot data from tb in the following way: for each item in x_variable show the corresponding value in y_variable.
 #' Then color the plot depending on the fill value.
 #' Give the plot a title and a label for x-axis and y-axis,
 #' color the legend according to filllab and finally
 #' improve positioning details according to position
 #' 
 #' @export
-bar_plot_fraktionen <- function(tb) {
+bar_plot_fractions <- function(tb,
-    ggplot(tb, aes(x = reorder(fraktion, -n), y = n, fill = fraktion)) +
+                               x_variable = NULL, # default is fraction
-    scale_fill_manual(values = party_colors) +
+                               y_variable = NULL, # default is n
-    geom_bar(stat = "identity")
+                               fill = NULL, # default is fraction
                               title = NULL,
                               xlab = "Fraction",
                               ylab = "n",
                               filllab = "Fraction",
                               flipped = TRUE,
                               position = "dodge",
                               reorder = FALSE,
                               rotatelab = FALSE) {
    # capture expressions in arguments
    fill <- enexpr(fill)
    y_variable <- enexpr(y_variable)
    x_variable <- enexpr(x_variable)
    # set default values
    if (is.null(fill)) fill <- expr(fraction)
    if (is.null(y_variable)) y_variable <- expr(n)
    if (is.null(x_variable)) x_variable <- expr(fraction)
    # check if variables exist
    if (!rlang::expr_text(x_variable) %in% names(tb))
        stop(paste0(rlang::expr_text(x_variable),
                    " is not a column of tb. Did you set x_variable accordingly?"),
             .call = NULL)
    if (!rlang::expr_text(y_variable) %in% names(tb))
        stop(paste0(rlang::expr_text(y_variable),
                    " is not a column of tb. Did you set y_variable accordingly?"),
             .call = NULL)
    if (!rlang::expr_text(fill) %in% names(tb))
        stop(paste0(rlang::expr_text(fill),
                    " is not a column of tb. Did you set fill accordingly?"),
             .call = NULL)
    # check argument types
    stopifnot("title has to be of type character or NULL" = is.character(title) || is.null(title))
    stopifnot("xlab has to be of type character" = is.character(xlab))
    stopifnot("ylab has to be of type character" = is.character(ylab))
    stopifnot("filllab has to be of type character" = is.character(filllab))
    stopifnot("flipped has to be of type logical" = is.logical(flipped))
    stopifnot("rotatelab has to be of type logical" = is.logical(rotatelab))
    stopifnot("reorder has to be of type logical" = is.logical(reorder))
    # either reorder fraction factor by variable value
    if (reorder) maps <- aes(x = reorder(!!x_variable, -!!y_variable),
                             y = !!y_variable,
                             fill = reorder(!!fill, -!!y_variable))
    # or reorder fraction factor by party seat order in parliament (default)
    else maps <- aes(x = factor(!!x_variable, levels = party_order),
                     y = !!y_variable,
                     fill = factor(!!fill, levels = party_order))
    # make a bar plot
    ggplot(tb, maps) +
        scale_fill_manual(values = party_colors, na.value = "#555555") +
        xlab(xlab) +
        ylab(ylab) +
        labs(fill = filllab) +
        ggtitle(title) +
        geom_bar(stat = "identity", position = position) ->
        plt
    # if rotatelab == TRUE, rotate x labels by 90 degrees
    if (rotatelab)
        plt + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) -> plt
    # if flipped == TRUE, draw bars horizontally (default TRUE)
    if (flipped) plt + coord_flip() else plt
 }
 #' Word usage summarised by date
 #'
 #' Counts how many talks do match a given pattern and summarises by date.
 #'
 #' @param res List of Tibbles to be analysed.
 #' @param patterns Words to look up.
 #' @param tidy default is FALSE.
 #'
 #' @export
 word_usage_by_date <- function(res, patterns, tidy=F) {
    is_valid_res(res)
    stopifnot("patterns must be of type character" = is.character(patterns))
    stopifnot("tidy must be of type logical" = is.logical(tidy))
    tb <- res$talks
    nms <- names(patterns)
    for (i in seq_along(patterns)) {
        if (!is.null(nms)) name <- nms[[i]]
        else name <- patterns[[i]]
        tb <- mutate(tb, {{name}} := str_count(content, patterns[[i]]))
    }
    left_join(tb, res$speeches, by=c("speech_id" = "id")) %>%
        group_by(date) %>%
        summarize(across(where(is.numeric), sum)) %>%
        arrange(date) -> tb
    if (!tidy) pivot_longer(tb, where(is.numeric) , names_to = "pattern", values_to="count")
    else tb
 }
@@ -36,12 +36,14 @@ fetch_batch <- function(offset, download_dir) {
 #' This fetches all available records of the 19th legislative period of the german Bundestag.
 #'
 #' @param download_dir character
 #' @param create bool
 #' 
 #' if create is TRUE, the directory given in download_dir is created
 #'
 #' @export
-fetch_all <- function(download_dir="records/", create=FALSE) {
+fetch_all <- function(download_dir="inst/records/", create=FALSE) {
-    # check if download_dir path is a directory path
+    # append file separator if needed
-    if (str_sub(download_dir, -1) != .Platform$file.sep)
+    download_dir <- make_directory_path(download_dir)
        download_dir <- str_c(download_dir, .Platform$file.sep)
    check_directory(download_dir, create)
    cat("Fetching all available records from bundestag.de. This may take a while ...\n")
@@ -59,10 +61,3 @@ fetch_all <- function(download_dir="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$message <- "Directory does not exist and can't be created. Probably because the path is not writeable."
    stop(cond)
 }
@@ -6,6 +6,7 @@
 #' @import stringr
 #' @import xml2
 #' @import utils
 #' @import tidyr
 #' @import purrr
 #' @keywords internal
 "_PACKAGE"
@@ -18,3 +18,51 @@ check_directory <- function(path, create=F) {
        stop("Directory exists, but is not writeable.")
    }
 }
 stop_dir_not_creatable <- function(cond) {
    # currently this has call: dir.create(download_dir)
    # do we want to change this to fetch_all(...) ?
    cond$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)
    else path
 }
 # check if res is of expected format
 is_valid_res <- function(res) {
    stopifnot("Data is missing relevant tables. Is this a return value of read_all or repair?"
              = all(c("speaker", "speeches", "talks", "comments", "applause") %in% names(res)))
    stopifnot("Some entries of res are no tibbles."
              = all(sapply(res, typeof) == "list" & "tbl" %in% sapply(res, class)))
    stopifnot("Speaker table is of wrong format."
              = all(c("id", "prename", "lastname", "fraction", "title", "role_short", "role_long")
                    %in% names(res$speaker)) &&
                all(sapply(res$speaker, is.character)))
    stopifnot("Speeches table is of wrong format."
              = all(c("id", "speaker", "date") %in% names(res$speeches)) &&
                is.character(res$speeches$id) &&
                is.character(res$speeches$speaker) &&
                lubridate::is.Date(res$speeches$date))
    stopifnot("Talks table is of wrong format."
              = all(c("speech_id", "speaker", "content") %in% names(res$talks)) &&
                all(sapply(res$talks, is.character)))
    stopifnot("Comments table is of wrong format."
              = all(c("speech_id", "on_speaker", "fraction", "commenter", "content")
                    %in% names(res$comments)) &&
                all(sapply(res$comments, is.character)))
    stopifnot("Applause table is of wrong format."
              = all(c("speech_id", "on_speaker", "CDU_CSU", "SPD", "FDP", "DIE_LINKE", "BUENDNIS_90_DIE_GRUENEN", "AfD")
                    %in% names(res$applause)) &&
                is.character(res$applause$speech_id) &&
                is.character(res$applause$on_speaker) &&
                is.logical(res$applause$`CDU_CSU`) &&
                is.logical(res$applause$`SPD`) &&
                is.logical(res$applause$`FDP`) &&
                is.logical(res$applause$`DIE_LINKE`) &&
                is.logical(res$applause$`AfD`) &&
                is.logical(res$applause$`BUENDNIS_90_DIE_GRUENEN`))
 }
@@ -1,27 +1,40 @@
 # for usage see the example at the end
 #' Parse xml records
 #'
 #' Creates a list of tibbles containing relevant information from all records
 #' stored in the input directory.
 #'
-#' @param path character
+#' @param path path to records directory
 #' @param pattern search pattern to find records in directory
 #'
 #' @export
-read_all <- function(path="records/") {
+read_all <- function(path="inst/records/", pattern="-data\\.xml") {
    # append file separator if needed
    path <- make_directory_path(path)
    cat("Reading all records from", path, "\n")
    available_protocols <- list.files(path)
    res <- pblapply(available_protocols, read_one, path=path)
-    lapply(res, `[[`, "redner") %>%
+    # list all files in directory and filter by search pattern
    fs <- list.files(path)
    available_protocols <- fs[str_detect(fs, pattern)]
    if (length(available_protocols) == 0)
        stop(paste0("The given directory does not exist or does not contain files matching \"",
                    pattern,
                    "\"."))
    # parse records one by one and remove null entries
    res <- compact %$% pblapply(available_protocols, read_one, path=path)
    if (length(res) == 0) stop("No valid records found. Did you fetch successfully?")
    lapply(res, `[[`, "speaker") %>%
        bind_rows() %>%
        distinct() ->
-        redner
+        speaker
-    lapply(res, `[[`, "reden") %>%
+    lapply(res, `[[`, "speeches") %>%
        bind_rows() %>%
-        distinct() ->
+        distinct() %>%
-        reden
+        mutate(date = as.Date(date, format="%d.%m.%Y")) ->
        speeches
    lapply(res, `[[`, "talks") %>%
        bind_rows() %>%
@@ -31,33 +44,55 @@ read_all <- function(path="records/") {
    lapply(res, `[[`, "comments") %>%
        bind_rows() %>%
        distinct() ->
-        comments
+        commentsandapplause
-    if (length(available_protocols) == 0)
+    filter(commentsandapplause, type == "comment") %>%
-        warning("The given directory is empty or does not exist.")
+        select(-type) ->
-    list(redner = redner, reden = reden, talks = talks, comments = comments)
+        comments
    filter(commentsandapplause, type == "applause") %>%
        select(-type, -commenter, -content) %>%
        mutate("CDU_CSU" = str_detect(fraction, "CDU/CSU"),
               "SPD" = str_detect(fraction, "SPD"),
               "FDP" = str_detect(fraction, "FDP"),
               "DIE_LINKE" = str_detect(fraction, "DIE LINKE"),
               "BUENDNIS_90_DIE_GRUENEN" = str_detect(fraction, "B\u00DCNDNIS 90/DIE GR\u00DCNEN"),
               "AfD" = str_detect(fraction, "AfD")) %>%
        select(-fraction) ->
        applause
    list(speaker = speaker, speeches = speeches, talks = talks, comments = comments, applause = applause)
 }
 # this reads all currently parseable data from one xml
 read_one <- function(name, path) {
    x <- tryCatch(read_xml(paste0(path, name)),
-                  error = function(c) NULL)
+                  error = function(c) NULL,
                  warning = function(c) NULL)
    if (is.null(x)) return(NULL)
    # extract date of session
    date <- xml_attr(x, "sitzung-datum")
    cs <- xml_children(x)
    verlauf <- xml_find_first(x, "sitzungsverlauf")
-    rednerl <- xml_find_first(x, "rednerliste")
+    speakerl <- xml_find_first(x, "rednerliste")
-    xml_children(rednerl) %>%
+    # check if record is invalid or empty (every record should have at least
-        parse_rednerliste() ->
+    # one speech, a speaker and a date
-        redner
+    if (is.na(date) || length(verlauf) == 0 || length(speakerl) == 0) {
        warning("Invalid record found. Skipping.")
        return(NULL)
    }
    xml_children(speakerl) %>%
        parse_speakerlist() ->
        speaker
    xml_children(verlauf) %>%
        xml_find_all("rede") %>%
-        parse_redenliste() ->
+        parse_speechlist(date) ->
        res
-    list(redner = redner, reden = res$reden, talks = res$talks, comments = res$comments)
+    list(speaker = speaker, speeches = res$speeches, talks = res$talks, comments = res$comments)
 }
 xml_get <- function(node, name) {
@@ -66,158 +101,173 @@ xml_get <- function(node, name) {
    else res
 }
-# parse one redner
+# parse one speaker
-parse_redner <- function(redner_xml) {
+parse_speaker <- function(speaker_xml) {
-    redner_id <- xml_attr(redner_xml, "id")
+    speaker_id <- xml_attr(speaker_xml, "id")
-    nm <- xml_child(redner_xml)
+    nm <- xml_child(speaker_xml)
-    vorname <- xml_get(nm, "vorname")
+    prename <- xml_get(nm, "vorname")
-    nachname <- xml_get(nm, "nachname")
+    lastname <- xml_get(nm, "nachname")
-    fraktion <- xml_get(nm, "fraktion")
+    fraction <- xml_get(nm, "fraktion")
-    titel <- xml_get(nm, "titel")
+    title <- xml_get(nm, "titel")
-    rolle <- xml_find_all(nm, "rolle")
+    role <- xml_find_all(nm, "rolle")
-    if (length(rolle) > 0) {
+    if (length(role) > 0) {
-        rolle_lang <- xml_get(rolle, "rolle_lang")
+        role_long <- xml_get(role, "rolle_lang")
-        rolle_kurz <- xml_get(rolle, "rolle_kurz")
+        role_short <- xml_get(role, "rolle_kurz")
-    } else rolle_kurz <- rolle_lang <- NA_character_
+    } else role_short <- role_long <- NA_character_
-    c(id = redner_id, vorname = vorname, nachname = nachname, fraktion = fraktion, titel = titel,
+    c(id = speaker_id, prename = prename, lastname = lastname, fraction = fraction, title = title,
-      rolle_kurz = rolle_kurz, rolle_lang = rolle_lang)
+      role_short = role_short, role_long = role_long)
 }
-# parse one rede
+# parse one speech
-# returns: - a rede (with rede id and redner id)
+# returns: - a speech (with speech id and speaker id)
-#          - all talks appearing in the rede (with corresponding content)
+#          - all talks appearing in the speech (with corresponding content)
-parse_rede <- function(rede_xml) {
+parse_speech <- function(speech_xml, date) {
-    rede_id <- xml_attr(rede_xml, "id")
+    speech_id <- xml_attr(speech_xml, "id")
-    cs <- xml_children(rede_xml)
+    cs <- xml_children(speech_xml)
-    cur_redner <- NA_character_
+    cur_speaker <- NA_character_
-    principal_redner <- NA_character_
+    principal_speaker <- NA_character_
    cur_content <- ""
-    reden <- list()
+    speeches <- list()
    comments <- list()
    for (node in cs) {
        if (xml_name(node) == "p" || xml_name(node) == "name") {
            klasse <- xml_attr(node, "klasse")
            if ((!is.na(klasse) && klasse == "redner") || xml_name(node) == "name") {
-                if (!is.na(cur_redner)) {
+                if (!is.na(cur_speaker)) {
-                    rede <- c(rede_id = rede_id,
+                    speech <- c(speech_id = speech_id,
-                              redner = cur_redner,
+                              speaker = cur_speaker,
                              content = cur_content)
-                    reden <- c(reden, list(rede))
+                    speeches <- c(speeches, list(speech))
                    cur_content <- ""
                } 
-                if (is.na(principal_redner) && xml_name(node) != "name") {
+                if (is.na(principal_speaker) && xml_name(node) != "name") {
-                    principal_redner <- xml_child(node) %>% xml_attr("id")
+                    principal_speaker <- xml_child(node) %>% xml_attr("id")
                }
                if (xml_name(node) == "name") {
-                    cur_redner <- "BTP"
+                    cur_speaker <- "BTP"
                } else {
-                    cur_redner <- xml_child(node) %>% xml_attr("id")
+                    cur_speaker <- xml_child(node) %>% xml_attr("id")
                }
            } else {
                cur_content <- paste0(cur_content, xml_text(node), sep="\n")
            }
        } else if (xml_name(node) == "kommentar") {
            # comments are of the form
-            # <kommentar>(blabla [Fraktion] – blabla liasdf – bla)</kommentar>
+            # <kommentar>(blabla [Fraktion] \u2013 blabla liasdf \u2013 bla)</kommentar>
            xml_text(node) %>%
                str_sub(2, -2) %>%
-                str_split("–") %>%
+                str_split("\u2013") %>%
                `[[`(1) %>%
-                lapply(parse_comment, rede_id = rede_id, on_redner = cur_redner) ->
+                lapply(parse_comment, speech_id = speech_id, on_speaker = cur_speaker) ->
                cs
            comments <- c(comments, cs)
        }
    }
-    rede <- c(rede_id = rede_id,
+    speech <- c(speech_id = speech_id,
-              redner = cur_redner,
+              speaker = cur_speaker,
              content = cur_content)
-    reden <- c(reden, list(rede))
+    speeches <- c(speeches, list(speech))
-    list(rede = c(id = rede_id, redner = principal_redner),
+    list(speech = c(id = speech_id, speaker = principal_speaker, date = date),
-         parts = reden,
+         parts = speeches,
         comments = comments)
 }
-fraktionspattern <- "BÜNDNIS(SES)?\\W*90/DIE\\W*GRÜNEN|CDU/CSU|AfD|SPD|DIE LINKE|FDP|LINKEN"
+fractionpattern <- "B\u00DCNDNIS(SES)?\\W*90/DIE\\W*GR\u00DCNEN|CDU/CSU|AfD|SPD|DIE LINKE|FDP|LINKEN"
-fraktionsnames <- c("BÜNDNIS 90/DIE GRÜNEN", "CDU/CSU", "AfD", "SPD", "DIE LINKE", "FDP")
+fractionnames <- c("B\u00DCNDNIS 90/DIE GR\u00DCNEN", "CDU/CSU", "AfD", "SPD", "DIE LINKE", "FDP",
                   "Fraktionslos")
-parse_comment <- function(comment, rede_id, on_redner) {
+parse_comment <- function(comment, speech_id, on_speaker) {
-    base <- c(rede_id = rede_id, on_redner = on_redner)
+    base <- c(speech_id = speech_id, on_speaker = on_speaker)
-    str_extract_all(comment, fraktionspattern) %>%
+    # classify comment
    if(str_detect(comment, "Beifall")) {
        str_extract_all(comment, fractionpattern) %>%
            `[[`(1) %>%
-        sapply(partial(flip(head), 1) %.% agrep, x=fraktionsnames, max=0.2, value=T) %>%
+            sapply(partial(flip(head), 1) %.% agrep, x=fractionnames, max=0.2, value=T) %>%
            str_c(collapse=",") ->
            by
-    # classify comment
+        c(base, type = "applause", fraction = by, commenter = NA_character_, content = comment)
    # TODO:
    # - actually separate content properly
    # - differentiate between [AfD] and AfD in by
    if(str_detect(comment, "Beifall")) {
        c(base, type = "applause", fraktion = by, kommentator = NA_character_, content = comment)
    } else {
        ps <- str_match(comment, "(.*) \\[(.*?)\\]: (.*)")[1,]
-        c(base, type = "comment", fraktion = ps[3], kommentator = ps[2], content = ps[4])
+        fraction <- agrep(ps[3], fractionnames, max=0.2, value=T)
        if (all(is.na(fraction)) || length(fraction) == 0) fraction <- NA_character_
        c(base, type = "comment", fraction = fraction, commenter = ps[2], content = ps[4])
    }
 }
-# creates a tibble of reden and a tibble of talks from a list of xml nodes representing reden
+# creates a tibble of speeches and a tibble of talks from a list of xml nodes representing speeches
-parse_redenliste <- function(redenliste_xml) {
+parse_speechlist <- function(speechlist_xml, date) {
-    d <- sapply(redenliste_xml, parse_rede)
+    d <- sapply(speechlist_xml, parse_speech, date = date)
-    reden <- simplify2array(d["rede", ])
+    speeches <- simplify2array(d["speech", ])
    parts <- simplify2array %$% unlist(d["parts", ], recursive=FALSE)
    comments <- simplify2array %$% unlist(d["comments", ], recursive=FALSE)
-    list(reden = tibble(id = reden["id",], redner = reden["redner",]),
+    list(speeches = tibble(id = speeches["id",], speaker = speeches["speaker",],
-         talks = tibble(rede_id = parts["rede_id", ],
+                        date = speeches["date",]),
-                        redner = parts["redner", ],
+         talks = tibble(speech_id = parts["speech_id", ],
                        speaker = parts["speaker", ],
                        content = parts["content", ]),
-         comments = tibble(rede_id = comments["rede_id",],
+         comments = tibble(speech_id = comments["speech_id",],
-                           on_redner = comments["on_redner",],
+                           on_speaker = comments["on_speaker",],
                           type = comments["type",],
-                           fraktion = comments["fraktion",],
+                           fraction = comments["fraction",],
-                           kommentator = comments["kommentator",],
+                           commenter = comments["commenter",],
                           content = comments["content", ]))
 }
-# create a tibble of redner from a list of xml nodes representing redner
+# create a tibble of speaker from a list of xml nodes representing speaker
-parse_rednerliste <- function(rednerliste_xml) {
+parse_speakerlist <- function(speakerliste_xml) {
-    d <- sapply(rednerliste_xml, parse_redner)
+    d <- sapply(speakerliste_xml, parse_speaker)
    tibble(id = d["id",],
-           vorname = d["vorname",],
+           prename = d["prename",],
-           nachname = d["nachname",],
+           lastname = d["lastname",],
-           fraktion = d["fraktion",],
+           fraction = d["fraction",],
-           titel = d["titel",],
+           title = d["title",],
-           rolle_kurz = d["rolle_kurz",],
+           role_short = d["role_short",],
-           rolle_lang = d["rolle_lang",])
+           role_long = d["role_long",])
 }
 #' Write the parsed and repaired results into separate csv files
 #' 
 #' @param tables list of tables to convert into a csv files.
 #' @param path where to put the csv files.
 #' @param create set TRUE if the path does not exist yet and you want to create it
 #' 
 #' @export
-write_to_csv <- function(tables, path="csv/", create=F) {
+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$redner, str_c(path, "redner.csv"))
+    write.table(tables$speaker, str_c(path, "speaker.csv"))
-    write.table(tables$reden, str_c(path, "reden.csv"))
+    write.table(tables$speeches, str_c(path, "speeches.csv"))
    write.table(tables$talks, str_c(path, "talks.csv"))
    write.table(tables$comments, str_c(path, "comments.csv"))
    write.table(tables$applause, str_c(path, "applause.csv"))
 }
 #' create a tibble from the csv file
 #'
 #' @param path directory to read files from
 #'
 #' reading the tables from a csv is way faster than reading and repairing the data every single time
 #' 
 #' @export
-read_from_csv <- function(path="csv/") {
+read_from_csv <- function(path="inst/csv/") {
-    list(redner = read.table(str_c(path, "redner.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)),
-         reden = read.table(str_c(path, "reden.csv")) %>%
+         speeches = read.table(str_c(path, "speeches.csv")) %>%
             tibble() %>%
-             mutate(redner = as.character(redner)),
+             mutate(speaker = as.character(speaker),
                    date = as.Date(date)),
         talks = tibble %$% read.table(str_c(path, "talks.csv")),
-         comments = tibble %$% read.table(str_c(path, "comments.csv")))
+         comments = tibble %$% read.table(str_c(path, "comments.csv")),
         applause = tibble %$% read.table(str_c(path, "applause.csv"))) -> res
    is_valid_res(res)
    res
 }
 # -------------------------------
 # EXAMPLE USE
 # make sure data ist downloaded via fetch.R
 # res <- read_one("records/19126-data.xml")
 # 
 # res$redner
 # res$reden
 # res$talks
 # -------------------------------
@@ -1,15 +1,16 @@
-fraktionen <- c("AFD" = "AfD",
+fractions <- c("AFD" = "AfD",
-                "BÜNDNIS90/" = "BÜNDNIS 90 / DIE GRÜNEN",
+                "AFD&FRAKTIONSLOS" = "AfD&Fraktionslos",
-                "BÜNDNIS90/DIEGRÜNEN" = "BÜNDNIS 90 / DIE GRÜNEN",
+                "B\u00DCNDNIS90/" = "B\u00DCNDNIS 90/DIE GR\u00DCNEN",
                "B\u00DCNDNIS90/DIEGR\u00DCNEN" = "B\u00DCNDNIS 90/DIE GR\u00DCNEN",
                "FRAKTIONSLOS" = "Fraktionslos",
                "DIELINKE" = "DIE LINKE",
                "SPD" = "SPD",
                "CDU/CSU" = "CDU/CSU",
                "FDP" = "FDP")
-repair_fraktion <- function(fraktion) {
+repair_fraction <- function(fraction) {
-    cleaned <- str_to_upper %$% str_replace_all(fraktion, "\\s", "")
+    cleaned <- str_to_upper %$% str_replace_all(fraction, "\\s", "")
-    fraktionen[cleaned]
+    fractions[cleaned]
 }
 # takes vector of titel and keeps longest
@@ -21,44 +22,51 @@ longest_titel <- function(titel) {
 # takes character vector, removes duplicates and collapses
 collect_unique <- function(xs) xs %>% clear_na() %>% unique() %>% str_c(collapse="&") %>% na_if("")
-# expects a tibble of redner and repairs
+# expects a tibble of speaker and repairs
-repair_redner <- function(redner) {
+repair_speaker <- function(speaker) {
-    if (nrow(redner) == 0) return(redner)
+    if (nrow(speaker) == 0) return(speaker)
-    redner %>%
+    speaker %>%
        filter(id != "10000") %>% # invalid id's
-        mutate(fraktion = Vectorize(repair_fraktion)(fraktion)) %>% # fix fraktion
+        mutate(fraction = Vectorize(repair_fraction)(fraction)) %>% # fix fraction
        group_by(id) %>%
-        summarize(vorname = head(vorname, 1),
+        summarize(prename = head(prename, 1),
-                  nachname = head(nachname, 1),
+                  lastname = head(lastname, 1),
-                  fraktion = collect_unique(fraktion),
+                  fraction = collect_unique(fraction),
-                  titel = longest_titel(titel),
+                  title = longest_titel(title),
-                  rolle_kurz = collect_unique(str_squish(rolle_kurz)),
+                  role_short = collect_unique(str_squish(role_short)),
-                  rolle_lang = collect_unique(str_squish(rolle_lang))) %>%
+                  role_long = collect_unique(str_squish(role_long))) %>%
        ungroup() #%>%
        # arrange(id) %>%
        # distinct(vorname, nachname, fraktion, titel)
 }
-repair_reden <- function(reden) {
+repair_speeches <- function(speeches) {
-    if (nrow(reden) == 0) return(reden)
+    if (nrow(speeches) == 0) return(speeches)
    # TODO: fill with content
-    reden
+    speeches
 }
 repair_talks <- function(talks) {
    if (nrow(talks) == 0) return(talks)
-    # TODO: fill with content
+    # ignore all talks which have empty content
-    talks
+    filter(talks, str_length(content) > 0)
 }
-# tries to find the correct redner id given a name
+#' Lookup name in speakers table
-# this is sufficient since every prename lastname combination in the bundestag is
+#'
-# unique (luckily :D)
+#' Tries to find the correct speaker id given a name.
-# returns a lookup table
+#' This is sufficient since every prename lastname combination in the bundestag is
-lookup_redner <- function(comments, redner) {
+#' unique (luckily :D)
-    tobereplaced <- "[-–—‑- ]"
+#' 
-    redner %>%
+#' @param tb tibble
-        unite(name, vorname, nachname, sep=".*") %>%
+#' @param speaker tibble
 #' @param name_variable name
 #' 
 #' Tries to match the name_variable column with speaker names
 #' 
 #' returns a lookup table
 lookup_speaker <- function(tb, speaker, name_variable) {
    tobereplaced <- "[\u002D\u2013\u2014\u2011\u00AD ]"
    speaker %>%
        unite(name, prename, lastname, sep=".*") %>%
        mutate(name = str_replace_all(name, tobereplaced, ".*")) ->
        rs
    find_match <- function(komm) {
@@ -68,28 +76,44 @@ lookup_redner <- function(comments, redner) {
        if (length(matches) == 0) return(NA_character_)
        rs[head(matches, 1), ]$id
    }
-    comments %>%
+    tb %>%
-        distinct(kommentator) %>%
+        distinct({{name_variable}}) %>%
-        mutate(redner = Vectorize(find_match)(str_replace_all(kommentator, tobereplaced, "")))
+        mutate(speaker = Vectorize(find_match)(str_replace_all({{name_variable}}, tobereplaced, "")))
 }
-repair_comments <- function(comments, redner) {
+repair_comments <- function(comments, speaker, lookup_speaker=F) {
    # try to find a redner id for each actual comment
    comments %>%
-        filter(!is.na(kommentator)) %>%
+        filter(!is.na(commenter) | !is.na(content) | !is.na(fraction)) ->
-        lookup_redner(redner) %>%
+        tb
-        left_join(comments, ., by="kommentator") %>%
+    if (lookup_speaker) {
-        select(-kommentator)
+        cat(paste0("Looking up speaker id's for names in comments. This may take a while ...\n",
                   "Use repair(, lookup_speaker = FALSE) to skip this.\n"))
        # try to find a speaker id for each actual comment
        tb %>%
            filter(!is.na(commenter)) %>%
            lookup_speaker(speaker, commenter) %>%
            left_join(tb, ., by="commenter")
    } else tb
 }
 #' Repair parsed tables
 #'
 #' @param parse_output tibble
 #' @param lookup_speaker bool
 #' 
 #' If lookup_speaker is TRUE, members of the parliament mentioned in comments are looked up in speaker table.
 #'
 #' Possible test: check identical(repair(res), repair(repair(res))) == TRUE
 #' Since repaired tables should be a fixpoint of repair.
 #' @export
-repair <- function(parse_output) {
+repair <- function(parse_output, lookup_speaker = FALSE) {
-    list(redner = repair_redner(parse_output$redner),
+    is_valid_res(parse_output)
-         reden = repair_reden(parse_output$reden),
+    stopifnot("lookup_speaker must be of type logical" = is.logical(lookup_speaker))
    list(speaker = repair_speaker(parse_output$speaker),
         speeches = repair_speeches(parse_output$speeches),
         talks = repair_talks(parse_output$talks),
-         #comments = repair_comments(parse_output$comments)
+         comments = repair_comments(parse_output$comments,
-         comments = parse_output$comments
+                                    parse_output$speaker,
-         )
+                                    lookup_speaker),
         applause = parse_output$applause)
 }
@@ -1,90 +1,178 @@
-# How to develop
+# Description
-Wie kann man entwickeln?
+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
-# alles geht mit devtools (laedt auch noch ein paar andere pakete)
+remotes::install_url("https://git.flavigny.de/christian/hateimparlament/archive/master.zip")
-library(devtools)
+```
 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.
-# neu laden aller paket funktionen
+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
 ```r
 devtools::load_all()
 fetch_all(create = TRUE)
 read_all() %>% repair() -> res
 write_to_csv(res, create = TRUE)
 ```
 Then finally, do:
 ```r
 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`:
 ```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
 library(devtools)
 ```
 When you changed something or added some functionality, you can reload all package functions with
 ```r
 load_all()
 ```
-Wir verwenden NIEMALS source, etc.! Außerdem NIEMALD library(...) verwenden, sondern
+If you want to avoid reading all records every time you start a new R session, you can
-um neue pakete hinzuzufuegen (als dependency), verwende:
+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
 ```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")
 ```
-Um paket imports verfuegbar zu machen, muss man diese in `R/hateimparlament-package.R`
+To make package imports available, you have to add them to `R/hateimparlament-package.R`
-als `@import <package>` hinzufuegen.
+as `@import <package>`.
-Um dokumentationen neu zu laden / zu erstellen (ruft roxgen auf)
+To reload / create documentation (calls roxygen)
 ```r
 document()
 ```
-Baue vignetten
+Build vignettes
 ```r
-rmarkdown::render("vignettes/bla.Rmd")
+rmarkdown::render("vignettes/test.Rmd")
 ```
 # Herunterladen
 Bevor analysiert werden kann, muss fetch.R ausgeführt werden, um alle Protokolle herunterzuladen.
 # Parsing
 ## Tabellen
 parse.R parsed einzelne Protokolle und erstellt 3 Tibbles
 ### Redner
 Struktur: `id` , `vorname` , `nachname` , `fraktion` , `titel` , `rolle_kurz`, `rolle_lang`
 Die Rollen sind beispielsweise "Bundeskanzlerin". Leider gegendert und deshalb wahrscheinlich
 nervig zu analysieren.
 Wird gewonnnen aus dem `<rednerliste>` Eintrag am Ende der Protokolle.
 ### Reden
 Struktur: `id` , `redner`
 Die Reden `id` wird im Protokoll festgelegt und ist eindeutig. Eine Rede ist ein
 `<rede>` Eintrag im Sitzungsverlauf. Eine Rede hat immer einen Hauptredner
 (der der vorne am Pult steht).
 Innerhalb einer Rede kann es verschieden Redebeiträge geben:
 - Kommentare: Beifall, Zwischenrufe, etc.
 - Redebeiträge: Typischerweise hauptsächlich der Hauptredner, aber auch Zwischenfragen. Diese werden
 beim parsen in der Tabelle Talks gespeichert.
 ### Talks
 Struktur: `rede_id` , `redner` , `content`
 Das sind die eigentlichen Redebeiträge, die innerhalb von _rede_ Einträgen auftauchen. Dabei gilt:
 - `rede_id`: Die Rede in dem der Beitrag auftaucht
 - `redner`: Der Sprecher des Redebeitrags
 - `content`: Der Inhalt der Rede (__wichtig__: Aktuell werden die Ordnungskommentare des
 Bundestagspräsidenten nicht herausgefiltert, tauchen also im Inhalt auf, obwohl sie nicht vom
 `redner` gesprochen werden. To be fixed -> Issues!)
 ## Noch zu parsen: Alles kann, nichts muss.
 - Kommentare (aktuell werden nur `<p>`'s in Reden gesammelt). Hier ist zu überlegen, wie diese
 gesammelt werden sollten.
 - Meta Daten? Diese sind teilweise in den `rede_id`'s encoded.
 ## Kombinieren der Tabellen der Protokolle 
 - Alle Tabellen sollten schlussendlich kombiniert werden zu großen Tabellen über
 alle Protokolle.
 # Analyse
 - Schnittmenge AfD Vokabular und Hitler's Reden?
 - Redeanteile nach Geschlecht (dazu gibt es leider keine Daten in der Rednerliste), Fraktion, etc.
 - Ideen, Ideen, Ideen ...
@@ -1,8 +0,0 @@
 import os
 words = []
 for i in range(1, 7):
    with open(f'hitler_rede_{i}') as f:
        lines = f.readlines()
        for line in lines:
            words.extend(line.split(sep=" "))
@@ -0,0 +1,63 @@
 import re
 german_words = []
 with open('/home/josua/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-words.txt') as f:
    lines = f.readlines()
    for line in lines:
        #print(line.split(sep="\t"))
        index, word, count = line.split(sep="\t")
        if int(index) > 100 and int(count) > 5:
            german_words.append(word.lower())
 with open('/home/josua/deu_mixed-typical_2011_1M/deu_news_1995_1M-words.txt') as f:
    lines = f.readlines()
    for line in lines:
        #print(line.split(sep="\t"))
        index, word, count = line.split(sep="\t")
        if int(index) > 100 and int(count) > 5:# only words that are used more than 5 times
            german_words.append(word.lower())
 def get_words_from_line(line):
    words = line.split(sep=" ")
    ret_list = []
    for word in words:
        word = re.sub("[^a-zA-ZüöäÜÖÄßẞ]", "", word)
        ret_list.append(word.lower())
    return ret_list
 hitler_words = []
 for i in range(1, 7):
    with open(f'hitler_rede_{i}') as f:
        lines = f.readlines()
        for line in lines:
            hitler_words.extend(get_words_from_line(line))
 with open(f'goebbels_sportpalast') as f:
    lines = f.readlines()
    for line in lines:
        hitler_words.extend(get_words_from_line(line))
 with open(f'mein_kampf') as f:
    lines = f.readlines()
    for line in lines:
        hitler_words.extend(get_words_from_line(line))
 german_words = set(german_words)
 hitler_words = set(hitler_words) #unique
 #filter_words = hitler_words.intersection(set(german_words))
 only_hitler_words = list(hitler_words.difference(german_words))
 print(only_hitler_words)
 with open("german_words", "w") as f:
    for word in german_words:
        word += "\n"
        f.write(word)
 with open("hitler_words", "w") as f:
    for word in only_hitler_words:
        word += "\n"
        f.write(word)
@@ -0,0 +1,16 @@
 \documentclass{article}
 \usepackage[top=2.5cm, bottom=2.5cm]{geometry}
 \begin{document}
    \section*{Projektbeschreibung}
    Wir haben zunächst die Plenarprotokolle der 19. Wahlperiode von der Website automatisiert herunterladen lassen.
    Als nächstes haben wir die Daten in ein für die Analyse sinnvolles Format gebracht, d.h. 5 Tibbles  und Fehler ausgebessert.
    Daraufhin konnten wir mit der Analyse beginnen.
    Insbesondere 
    \section*{Werkzeuge aus der Vorlesung}
    Wir haben, da es hauptsächlich um Datenanalyse ging, sehr viel mit tidyverse gearbeitet.
    Ganz zu Beginn haben wir fürs fetchen der Protokolle rvest verwendet.
    Für die Visualisierung haben wir ggplot2 sowie vignettes genutzt.
    \section*{Organisation des Teams}
    \section*{Meine Beteiligung}
 \end{document}
@@ -0,0 +1,171 @@
 \documentclass{beamer}
 \usepackage[utf8]{inputenc}
 \usepackage{listings}
 \lstdefinestyle{mystyle}{
    commentstyle=\color{gray},
    keywordstyle=\color{black},
    numberstyle=\tiny\color{gray},
    stringstyle=\color{black},
    basicstyle=\ttfamily\footnotesize,
    breakatwhitespace=false,         
    breaklines=true,                 
    captionpos=b,                    
    keepspaces=true,                 
    numbers=left,                    
    numbersep=5pt,                  
    showspaces=false,                
    showstringspaces=false,
    showtabs=false,                  
    tabsize=2
 }
 \lstset{style=mystyle}
 \begin{document}
 \begin{frame}
    \frametitle{Implementierung}
    \tableofcontents
 \end{frame}
 \section{Herunterladen der Protokolle}
 \begin{frame}
 \frametitle{Herunterladen der Protokolle}
 Funktion: \lstinline{fetch_all(download_dir)}
 \begin{itemize}[<+->]
    \item Protokolle als XML-Dateien von \url{bundestag.de} herunterladen und
        in \lstinline{download_dir} speichern.
    \item Problem: Maschinenunfreundliche Webseite
    \item Lösung: Source Code von \url{bundestag.de} nach Schnittstelle durchsuchen
 \end{itemize}
 \end{frame}
 \section{Konvertierung der XML-Dateien in tibbles}
 \begin{frame}
    \frametitle{Konvertierung der XML-Dateien in tibbles}
    Funktion: \lstinline{read_all(filepath)}
    \begin{itemize}[<+->]
        \item Liest jede XML-Datei in angegebenem Dateipfad einzeln
        \item Extrahiert Sitzungsdatum, Rednerliste und Sitzungsverlauf
        \item Konvertiert Rednerliste in eine R Liste.
        \item Iteriert durch den Sitzungsverlauf, extrahiert Reden,
            Redebeiträge, Kommentare und Beifall
        \item Kombiniert alle Redner, Reden, Redebeiträge, Kommentare und Beifall
            zu 5 tibbles und gibt benannte Liste zurück.
    \end{itemize}
 \end{frame}
 \begin{frame}[fragile]
    \frametitle{Tabellen}
    Ergebnis der Konvertierung ist eine benannte Liste \lstinline{res} mit tibbles:
    \pause
    \begin{lstlisting}[language=R,basicstyle=\tiny\ttfamily]
 > res$speaker
 # A tibble: 1,025 x 7
  id      prename            lastname   fraction title role_short   role_long
  <chr>   <chr>              <chr>      <chr>    <chr> <chr>        <chr>
 1 110021  Alterspraesident D Otto Solms NA       NA    Alterspraesi Alterspraesi
 2 110032  Carsten            Schneider  SPD      NA    NA           NA
 # with 1,023 more rows
 \end{lstlisting}
 \pause
    \begin{lstlisting}[language=R,basicstyle=\tiny\ttfamily]
 > res$speeches
 # A tibble: 25,068 x 3
  id         speaker  date
  <chr>      <chr>    <date>
 1 ID19100100 11002190 2017-10-24
 2 ID19100200 11002190 2017-10-24
 # with 25,066 more rows
 \end{lstlisting}
 \pause
    \begin{lstlisting}[language=R,basicstyle=\tiny\ttfamily]
 > res$talks
 # A tibble: 63,663 x 3
  speech_id  speaker  content
  <chr>      <chr>    <chr>
 1 ID19100100 11002190 "Guten Morgen, liebe Kolleginnen und Kollegen! Nehmen Sie
 2 ID19100300 11003218 "Sehr geehrter Herr Praesident! Sehr geehrte Kolleginnen u
 # with 63,661 more rows
 \end{lstlisting}
 \end{frame}
 \begin{frame}[fragile]
    \begin{lstlisting}[language=R,basicstyle=\tiny\ttfamily]
 > res$comments
 # A tibble: 83,649 x 5
  speech_id  on_speaker fraction      commenter       content
  <chr>      <chr>      <chr>         <chr>           <chr>
 1 ID19100300 11003218   BUENDNIS 90/D Katrin Goering  Was?
 2 ID19100300 11003218   CDU/CSU       Volker Kauder   Warum habt ihr das bei Ge
 # with 83,647 more rows
 \end{lstlisting}
 \pause
    \begin{lstlisting}[language=R,basicstyle=\tiny\ttfamily]
 > res$applause
 # A tibble: 89,586 x 8
  speech_id  on_speaker CDU_CSU SPD   FDP   DIE_LINKE BUENDNIS_90_DIE_GRU  AfD
  <chr>      <chr>      <lgl>   <lgl> <lgl> <lgl>     <lgl>                <lgl>
 1 ID19100300 11003218   FALSE   TRUE  FALSE TRUE      TRUE                 FALSE
 2 ID19100300 11003218   FALSE   TRUE  TRUE  TRUE      FALSE                FALSE
 # with 89,584 more rows\end{lstlisting}
 \end{frame}
 \section{Reparieren von Fehlern}
 \begin{frame}
    \frametitle{Reparieren von Fehlern}
    Problem: Uneinheitliche Schreibweisen / Fehler in den Rednerlisten.
    \pause
    Lösung: Funktion: \lstinline{repair_speaker(speakers)}
    \pause
    \begin{itemize}[<+->]
        \item Erhält \lstinline{tibble} von Rednern
        \item Entfernt Redner mit ungültigen, doppelt vergebenen IDs
        \item Vereinheitlicht Schreibweisen der Fraktionen, Namen und Titel der Redner
    \end{itemize}
 \end{frame}
 \begin{frame}
    \frametitle{Reparieren von Fehlern}
    Problem: Namen in Kommentaren Rednern aus Rednertabelle zuordnen
    \pause
    Lösung: Funktion \lstinline{repair_comments(comments, speakers)}
    \begin{itemize}
        \item Erstellt für jeden Redner einen Regulären Ausdruck aus dem Namen
        \item Sucht für jeden Kommentar nach dem entsprechenden Eintrag in der
            Rednertabelle
    \end{itemize}
 \end{frame}
 \begin{frame}
    \frametitle{Reparieren von Fehlern}
    Beide Reparaturschritte werden in der Funktion \lstinline{repair} zusammengefasst.
 \end{frame}
 \section{Analyse}
 \begin{frame}
    \frametitle{Analyse}
    Stelle Hilfsfunktionen zur Analyse der Daten zur Verfügung:
    \begin{itemize}[<+->]
        \item \lstinline{bar_plot_fractions}: Erstellt ein Balkendiagramm aus einer Tabelle
            mit Fraktionsdaten
        \item \lstinline{find_word}: Fügt in der Redebeiträgetabelle zu jedem Redebeitrag
            die Häufigkeit eines Regulären Ausdrucks hinzu.
        \item \lstinline{word_usage_by_date}: Zählt an welchen Daten (Tagen) ein regulärer Ausdruck
            wie oft verwendet wird.
        \item \lstinline{join_speaker}: Fügt einer Tabelle mit Spalte \lstinline{speaker} die
            enstprechenden Informationen aus der Rednertabelle hinzu.
    \end{itemize}
 \end{frame}
 \end{document}
@@ -0,0 +1,55 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/analyze.R
 \name{bar_plot_fractions}
 \alias{bar_plot_fractions}
 \title{Bar chart visualizing fraction based data}
 \usage{
 bar_plot_fractions(
  tb,
  x_variable = NULL,
  y_variable = NULL,
  fill = NULL,
  title = NULL,
  xlab = "Fraction",
  ylab = "n",
  filllab = "Fraction",
  flipped = TRUE,
  position = "dodge",
  reorder = FALSE,
  rotatelab = FALSE
 )
 }
 \arguments{
 \item{tb}{tibble}
 \item{x_variable}{column in tb, default is fraction}
 \item{y_variable}{column in tb, default is n}
 \item{fill}{column in tb, default is fraction}
 \item{title}{plot title}
 \item{xlab}{label for x axis, default is fraction}
 \item{ylab}{label for y axis, default is n}
 \item{filllab}{default is 'Fraction'}
 \item{flipped}{if TRUE draw bars horizontally, else vertically. Default is TRUE}
 \item{position}{default is 'dodge'}
 \item{reorder}{Either reorder fraction factor by variable value or reorder fraction factor by party seat order in parliament (default).}
 \item{rotatelab}{Default is FALSE. If true turns the labels 90 degrees to the axis.
 plot data from tb in the following way: for each item in x_variable show the corresponding value in y_variable.
 Then color the plot depending on the fill value.
 Give the plot a title and a label for x-axis and y-axis,
 color the legend according to filllab and finally
 improve positioning details according to position}
 }
 \description{
 Can be configured to also visualize data not related to fractions.
 }
@@ -4,10 +4,14 @@
 \alias{fetch_all}
 \title{Download available records}
 \usage{
-fetch_all(download_dir = "records/", create = FALSE)
+fetch_all(download_dir = "inst/records/", create = FALSE)
 }
 \arguments{
 \item{download_dir}{character}
 \item{create}{bool
 if create is TRUE, the directory given in download_dir is created}
 }
 \description{
 This fetches all available records of the 19th legislative period of the german Bundestag.
@@ -0,0 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/analyze.R
 \name{find_word}
 \alias{find_word}
 \title{Count number of occurences of a given word}
 \usage{
 find_word(res, word)
 }
 \arguments{
 \item{res}{tibble}
 \item{word}{character
 Add number of occurences of word to talks}
 }
 \description{
 Count number of occurences of a given word
 }
@@ -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}
@@ -0,0 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/analyze.R
 \name{join_speaker}
 \alias{join_speaker}
 \title{add information from speaker table to a tibble containing speaker id}
 \usage{
 join_speaker(tb, res, fraction_only = F)
 }
 \arguments{
 \item{tb}{tibble}
 \item{res}{list of tibbles}
 \item{fraction_only}{if TRUE, only select fraction from the resulting joined tibble
 left join speaker information from res$speaker into tb.
 if fraction_only, drop all columns but fraction}
 }
 \description{
 add information from speaker table to a tibble containing speaker id
 }
@@ -0,0 +1,24 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/repair.R
 \name{lookup_speaker}
 \alias{lookup_speaker}
 \title{Lookup name in speakers table}
 \usage{
 lookup_speaker(tb, speaker, name_variable)
 }
 \arguments{
 \item{tb}{tibble}
 \item{speaker}{tibble}
 \item{name_variable}{name
 Tries to match the name_variable column with speaker names
 returns a lookup table}
 }
 \description{
 Tries to find the correct speaker id given a name.
 This is sufficient since every prename lastname combination in the bundestag is
 unique (luckily :D)
 }
@@ -0,0 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/analyze.R
 \docType{data}
 \name{party_colors}
 \alias{party_colors}
 \title{lookup table for official party colors}
 \format{
 An object of class \code{character} of length 8.
 }
 \usage{
 party_colors
 }
 \description{
 lookup table for official party colors
 }
 \keyword{datasets}
@@ -4,10 +4,12 @@
 \alias{read_all}
 \title{Parse xml records}
 \usage{
-read_all(path = "records/")
+read_all(path = "inst/records/", pattern = "-data\\\\.xml")
 }
 \arguments{
-\item{path}{character}
+\item{path}{path to records directory}
 \item{pattern}{search pattern to find records in directory}
 }
 \description{
 Creates a list of tibbles containing relevant information from all records
@@ -0,0 +1,16 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/parse.R
 \name{read_from_csv}
 \alias{read_from_csv}
 \title{create a tibble from the csv file}
 \usage{
 read_from_csv(path = "inst/csv/")
 }
 \arguments{
 \item{path}{directory to read files from
 reading the tables from a csv is way faster than reading and repairing the data every single time}
 }
 \description{
 create a tibble from the csv file
 }
@@ -4,7 +4,17 @@
 \alias{repair}
 \title{Repair parsed tables}
 \usage{
-repair(parse_output)
+repair(parse_output, lookup_speaker = FALSE)
 }
 \arguments{
 \item{parse_output}{tibble}
 \item{lookup_speaker}{bool
 If lookup_speaker is TRUE, members of the parliament mentioned in comments are looked up in speaker table.
 Possible test: check identical(repair(res), repair(repair(res))) == TRUE
 Since repaired tables should be a fixpoint of repair.}
 }
 \description{
 Repair parsed tables
@@ -0,0 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/analyze.R
 \name{word_usage_by_date}
 \alias{word_usage_by_date}
 \title{Word usage summarised by date}
 \usage{
 word_usage_by_date(res, patterns, tidy = F)
 }
 \arguments{
 \item{res}{List of Tibbles to be analysed.}
 \item{patterns}{Words to look up.}
 \item{tidy}{default is FALSE.}
 }
 \description{
 Counts how many talks do match a given pattern and summarises by date.
 }
@@ -0,0 +1,18 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/parse.R
 \name{write_to_csv}
 \alias{write_to_csv}
 \title{Write the parsed and repaired results into separate csv files}
 \usage{
 write_to_csv(tables, path = "inst/csv/", create = F)
 }
 \arguments{
 \item{tables}{list of tables to convert into a csv files.}
 \item{path}{where to put the csv files.}
 \item{create}{set TRUE if the path does not exist yet and you want to create it}
 }
 \description{
 Write the parsed and repaired results into separate csv files
 }
@@ -0,0 +1,83 @@
 ---
 title: "Analysis of covered topics"
 output: rmarkdown::html_vignette
 vignette: >
  %\VignetteIndexEntry{Analysis of covered topics}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
 )
 ```
 ```{r setup}
 library(hateimparlament)
 library(dplyr)
 library(ggplot2)
 library(stringr)
 library(tidyr)
 ```
 ## Preparation of data
 First, you need to download all records of the current legislative period.
 ```r
 fetch_all("../inst/records/") # path to directory where records should be stored
 ```
 Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
 ```r
 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.
 ```{r}
 res <- read_from_csv('../inst/csv/')
 ```
 ## Analysis
 Now we can start analysing our parsed dataset:
 ### Counting the occurences of a given word:
 ```{r, fig.width=7, fig.height=7}
 find_word(res, "Kohleausstieg") %>%
    filter(occurences > 0) %>%
    join_speaker(res) %>%
    select(content, fraction) %>%
    filter(!is.na(fraction)) %>%
    group_by(fraction) %>%
    summarize(n = n()) %>%
    arrange(desc(n)) %>%
    bar_plot_fractions(title = "Parties using the word 'Kohleausstieg' the most (absolutely)",
                       ylab = "Number of uses of 'Kohleausstieg'",
                       flipped = F,
                       rotatelab = T)
 ```
 ### When are which topics discussed the most?
 First we define some search patterns, according to some common political topics.
 ```{r}
 pandemic_pattern <- "(?i)virus|corona|covid|lockdown"
 climate_pattern <- "(?i)klimawandel|erderwärmung|co2|treibhaus|methan|kyoto-protokoll|klimaabkommen"
 pension_pattern <- "(?i)rente|pension|altersarmut"
 ```
 Then we use the analysis helper `word_usage_by_date` to generate a tibble counting the
 occurences of our search patterns per date. We can then plot the results:
 ```{r, fig.width=7, fig.height=6}
 word_usage_by_date(res, c(pandemic = pandemic_pattern,
                          climate = climate_pattern,
                          pension = pension_pattern)) %>%
    ggplot(aes(x = date, y = count, color = pattern)) +
    xlab("date of session") +
    ylab("occurence of word per session") +
    labs(color = "Topic") +
    geom_point()
 ```
@@ -1,70 +0,0 @@
 ---
 title: "funwithdata"
 output: rmarkdown::html_vignette
 vignette: >
  %\VignetteIndexEntry{funwithdata}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
 )
 ```
 ```{r setup}
 library(hateimparlament)
 library(dplyr)
 library(ggplot2)
 ```
 ## Preparation of data
 First, you need to download all records of the current legislative period.
 ```r
 fetch_all("../records/") # path to directory where records should be stored
 ```
 Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
 ```r
 read_all("../records/") %>% repair() -> res
 ```
 We also used `repair` to fix a bunch of formatting issues in the records and unpacked
 the result into more descriptive variables.
 For development purposes, we load the tables from csv files.
 ```{r}
 res <- read_from_csv('../csv/')
 ```
 and unpack our tibbles
 ```{r}
 comments <- res$comments
 reden <- res$reden
 redner <- res$redner
 talks <- res$talks
 ```
 ## Analysis
 Now we can start analysing our parsed dataset, e.g. find out which party gives the most talks:
 ```{r, fig.width=10}
 join_redner(reden, res) %>%
    group_by(fraktion) %>%
    summarize(n = n()) %>%
    arrange(n) %>%
    bar_plot_fraktionen()
 ```
 ### Count a word occurence
 ```{r, fig.width=10}
 find_word(res, "hitler") %>%
    filter(occurences > 0) %>%
    join_redner(res) %>%
    select(content, fraktion) %>%
    group_by(fraktion) %>%
    summarize(n = n()) %>%
    arrange(desc(n)) %>%
    bar_plot_fraktionen()
 ```
@@ -0,0 +1,195 @@
 ---
 title: "Differences in gender"
 output: rmarkdown::html_vignette
 vignette: >
  %\VignetteIndexEntry{Differences in gender}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
 )
 ```
 ```{r setup}
 library(hateimparlament)
 library(dplyr)
 library(ggplot2)
 library(stringr)
 library(tidyr)
 library(xml2)
 ```
 ## Preparation of data
 First, you need to download all records of the current legislative period.
 ```r
 fetch_all("../records/") # path to directory where records should be stored
 ```
 Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
 ```r
 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.
 ```{r}
 res <- read_from_csv('../inst/csv/')
 ```
 and unpack our tibbles
 ```{r}
 comments <- res$comments
 speeches <- res$speeches
 speaker <- res$speaker
 talks <- res$talks
 ```
 Bevor we can do our analysis, we have to assign a gender to our politicans. We do this
 by reading the gender from the master data of all members of parliament, which is
 fetched from bundestag.de.
 ```{r}
 xml_get <- function(node, name) {
    res <- xml_text(xml_find_all(node, name))
    if (length(res) == 0) NA_character_
    else res
 }
 x <- read_xml("../inst/masterdata.xml")
 mdbs <- xml_find_all(x, "MDB")
 ids <- c()
 genders <- c()
 for (mdb in mdbs) {
    xml_get(mdb, "ID") -> mdb_id
    xml_find_first(mdb, "BIOGRAFISCHE_ANGABEN") %>%
        xml_get("GESCHLECHT") ->
        mdb_gender
    ids <- c(ids, mdb_id)
    genders <- c(genders, if (mdb_gender == "männlich") "male" else "female")
 }
 gender <- tibble(id = ids, gender = genders)
 speaker_with_gender <- left_join(res$speaker, gender)
 ```
 ## Analyse
 First, let's look at the relative distribution of the sexes throughout the whole Bundestag.
 ```{r}
 speaker_with_gender %>% 
  select(gender) %>%
  group_by(gender) %>% 
  summarise("count" = n()) %>% 
  filter(gender %in% c("male", "female")) %>% 
  mutate(portion = 100*count/sum(count)) -> 
  plot1
 bp <- ggplot(plot1, aes(x = "", y = portion, fill = gender))+
  geom_bar(width = 1, stat = "identity")
 pie <- bp + coord_polar("y", start=0)
 pie + 
  scale_fill_manual(values=c("pink", "blue")) +
  ggtitle("Relative distribution of sexes") +
  xlab("") +
  ylab("")
 ```
 Next, we look at the individual distributions between men and women in the different fractions.
 ```{r, fig.width=7}
 speaker_with_gender %>%
  group_by(fraction) %>%
  summarize(n = n()) ->
  fraction_size
  speaker_with_gender %>% 
  filter(gender=="female") %>% 
  group_by(fraction) %>% 
  summarize(n_female = n()) %>% 
  left_join(fraction_size) %>% 
  mutate(q = n_female/n) -> women_per_fraction
  bar_plot_fractions(women_per_fraction, x_variable=fraction, y_variable=q, title="Frauenanteil nach Partei")
 ```
 Prepared with this knowledge, we can now analyse the relative amount of speeches by gender and fraction.
 ```{r, fig.width=7}
 speaker_with_gender %>% transmute(speaker_id = id, gender, fraction) -> simple_speaker_with_gender
 speeches %>% 
  transmute(id, speaker_id = speaker) %>% 
  inner_join(simple_speaker_with_gender) %>%
  group_by(fraction) %>%
  summarize(speeches=n()) -> 
  fraction_speeches_size
 speeches %>% 
  transmute(id, speaker_id = speaker) %>% 
  inner_join(simple_speaker_with_gender) %>%
  filter(gender=='female') %>%
  group_by(fraction) %>%
  summarize(female_speeches=n()) %>%
  left_join(fraction_speeches_size) %>%
  left_join(women_per_fraction) %>%
  mutate(q_speeches = female_speeches/speeches) -> speech_distribution
  #bar_plot_fractions(speech_distribution, x_variable=fraction, y_variable=q_speeches, title="Redeanteil Frauen nach Partei")
 party_order <- factor(c("Fraktionslos", "AfD&Fraktionslos",
                        "DIE LINKE", "BÜNDNIS 90/DIE GRÜNEN", "SPD", "CDU/CSU",
                        "FDP", "AfD", NA_character_))
 speech_distribution %>% 
  mutate("Frauenanteil" = q, "Redenanteil Frauen" = q_speeches) %>% 
  pivot_longer(c(Frauenanteil, "Redenanteil Frauen"), "type") %>% 
  ggplot(aes(x=factor(fraction, levels = party_order), y=value, fill=factor(type, levels = factor(c("Frauenanteil", "Redenanteil Frauen"))))) + scale_fill_manual(values= c("Frauenanteil"="gray", "Redenanteil Frauen"="red")) + coord_flip() + geom_bar(stat="identity", position="dodge") + labs(fill="Kategorie")
 ```
 For comparison, let's analyze the total differences in the amount of speeches given.
 ```{r}
 speeches %>%
  group_by(speaker) %>%
  summarize(n = n()) %>%
  ungroup() %>% 
  arrange(-n) %>%
  join_speaker(res) %>%
  left_join(gender, by=c("speaker"="id")) %>% 
  group_by(gender) %>% 
  summarise(absolute=sum(n)) %>%
  filter(gender %in% c("female", "male")) %>%
  mutate(absolute2=absolute/sum(absolute)) %>% 
  mutate(portion=c(0.32, 0.68)) %>% 
  mutate(relative=absolute*(1-portion)) %>% 
  mutate(relative2=relative/sum(relative)) -> 
  plot3
 ```
 At first lets take a look at the absolute difference in the amount of speeches by the two sexes.
 ```{r,fig.width=7}
 barplot(plot3$absolute2,
        ylab = "amount of speeches",
        main = "Absolute comparison of speech shares",
        las = 1, 
        names.arg = c("women", "men"), 
        col = c("pink", "darkblue"),
        font.main = 4,
        cex.axis = 0.7)
 ```
 Since there are more men represented in the German Bundestag, we now consider the relative proportions of speeches, depending on the ratio of men and women.
 ```{r, fig.width=7}
 barplot(plot3$relative2,
        ylab = "amount of speeches",
        main = "Relative comparison of speech shares",
        las = 1, 
        names.arg = c("women", "men"), 
        col = c("pink", "darkblue"),
        font.main = 4,
        cex.axis = 0.7)
 ```
@@ -0,0 +1,82 @@
 ---
 title: "General questions"
 output: rmarkdown::html_vignette
 vignette: >
  %\VignetteIndexEntry{General questions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
 )
 ```
 ```{r setup}
 library(hateimparlament)
 library(dplyr)
 library(ggplot2)
 library(stringr)
 library(tidyr)
 ```
 ## Preparation of data
 First, you need to download all records of the current legislative period.
 ```r
 fetch_all("../inst/records/") # path to directory where records should be stored
 ```
 Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
 ```r
 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.
 ```{r}
 res <- read_from_csv('../inst/csv/')
 ```
 ## Analysis
 Now we can start analysing our parsed dataset:
 ### Which party gives the most talks?
 ```{r, fig.width=7}
 join_speaker(res$speeches, res) %>%
    group_by(fraction) %>%
    summarize(n = n()) %>%
    arrange(n) %>%
    bar_plot_fractions(title="Number of speeches given by fraction",
                       ylab="Number of speeches")
 ```
 Note that `NA` signifies speeches given by speakers who are not members of parliament.
 ### Who gives the most speeches?
 ```{r}
 res$speeches %>%
    group_by(speaker) %>%
    summarize(n = n()) %>%
    arrange(-n) %>%
    left_join(res$speaker, by=c("speaker" = "id")) %>%
    head(10)
 ```
 ### Who talks the longest?
 Calculate the average character length of talks given by speakers:
 ```{r}
 res$talks %>%
    mutate(content_len = str_length(content)) %>%
    group_by(speaker) %>%
    summarize(avg_content_len = mean(content_len)) %>%
    arrange(-avg_content_len) %>%
    left_join(res$speaker, by=c("speaker" = "id")) %>%
    head(10)
 ```
@@ -0,0 +1,187 @@
 ---
 title: "Analysis of vocabulary"
 output: rmarkdown::html_vignette
 vignette: >
  %\VignetteIndexEntry{Analysis of vocabulary}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
 )
 ```
 ```{r setup}
 library(hateimparlament)
 library(dplyr)
 library(stringr)
 library(ggplot2)
 ```
 ## Preparation of data
 First, you need to download all records of the current legislative period.
 ```r
 fetch_all("../inst/records/") # path to directory where records should be stored
 ```
 Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
 ```r
 read_all("../inst/records/") %>% repair() -> res
 speeches <- res$speeches
 speaker <- res$speaker
 talks <- res$talks
 ```
 We also used `repair` to fix a bunch of formatting issues in the records and unpacked
 the result into more descriptive variables.
 For development purposes, we load the tables from csv files.
 ```{r}
 tables <- read_from_csv('../inst/csv/')
 comments <- tables$comments
 speeches <- tables$speeches
 speaker <- tables$speaker
 talks <- tables$talks
 ```
 Further, we need to load a list of words that were used by Hitler but not by standard German texts.
 ```{r}
 fil <- file('../inst/hitler_texts/hitler_words')
 Worte <- readLines(fil)
 hitlerwords <- tibble(Worte)
 ```
 ## Analysis
 Now we extract the words that were used with higher frequency by one party and compare them with `hitlerwords`.
 ```{r}
 talks %>% 
  left_join(speaker, by=c(speaker='id')) %>% 
  group_by(fraction) %>%
  summarize(full_text=str_c(content, collapse="\n")) -> talks_by_fraction
 ```
 For each party, we want to get a tibble of words with frequency.
 ```{r}
 #AfD
 Worte <- str_extract_all(talks_by_fraction$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 afdtotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/afdtotal) -> afd_words
 #AfD&Fraktionslos
 Worte <- str_extract_all(talks_by_fraction$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 afdundfraktionslostotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/afdundfraktionslostotal) -> afdundfraktionslos_words
 #BÜNDNIS 90 / DIE GRÜNEN
 Worte <- str_extract_all(talks_by_fraction$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 grünetotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/grünetotal) -> grüne_words
 #CDU/CSU
 Worte <- str_extract_all(talks_by_fraction$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 cdutotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/cdutotal) -> cdu_words
 #DIE LINKE
 Worte <- str_extract_all(talks_by_fraction$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 linketotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/linketotal) -> linke_words
 #FDP
 Worte <- str_extract_all(talks_by_fraction$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 fdptotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/fdptotal) -> fdp_words
 #Fraktionslos
 Worte <- str_extract_all(talks_by_fraction$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 fraktionslostotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/fraktionslostotal) -> fraktionslos_words
 #SPD
 Worte <- str_extract_all(talks_by_fraction$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 spdtotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/spdtotal) -> spd_words
 #NA
 Worte <- str_extract_all(talks_by_fraction$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 natotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/natotal) -> na_words
 #alle
 all_words <- bind_rows(afd_words, afdundfraktionslos_words, grüne_words, cdu_words, linke_words, fdp_words, fraktionslos_words, spd_words, na_words)
 total <- sum(all_words$n)
 all_words %>% group_by(Worte) %>% summarize(n = sum(n), part= sum(n)/total) -> all_words
 ```
 Now we want to extract the words that are more frequently used by a specific fraction.
 ```{r}
 afd_words %>% 
  transmute(freq, fraction_n = n) %>%
  left_join(all_words) %>%
  transmute(
    fraction_freq = freq, 
    total_freq = part, 
    fraction_n, 
    total_n = n, 
    rel_quotient = fraction_freq/total_freq, 
    abs_quotient = fraction_n/total_n) %>% 
  arrange(-abs_quotient, -fraction_n) %>% 
  filter(rel_quotient > 1) -> 
  afd_high_frequent
 select(afd_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% 
  filter(total_n > 80)
 afdundfraktionslos_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> afdundfraktionslos_high_frequent
 select(afdundfraktionslos_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 grüne_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> grüne_high_frequent
 select(grüne_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 cdu_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> cdu_high_frequent
 select(cdu_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 linke_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> linke_high_frequent
 select(linke_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 fdp_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> fdp_high_frequent
 select(fdp_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 fraktionslos_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> fraktionslos_high_frequent
 select(fraktionslos_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 spd_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> spd_high_frequent
 select(spd_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 na_words %>% transmute(freq, fraction_n = n) %>% left_join(all_words) %>% transmute(fraction_freq = freq, total_freq = part, fraction_n, total_n = n, rel_quotient = fraction_freq/total_freq, abs_quotient = fraction_n/total_n) %>% arrange(-abs_quotient, -fraction_n) %>% filter(rel_quotient > 1) -> na_high_frequent
 select(na_high_frequent, fraction_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
 ```
 We compare these words with `hitlerwords`.
 ```{r}
 afd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> afd_hitler_comparison
 afdundfraktionslos_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> afdundfraktionslos_hitler_comparison
 grüne_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> grüne_hitler_comparison
 cdu_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> cdu_hitler_comparison
 linke_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> linke_hitler_comparison
 fdp_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> fdp_hitler_comparison
 fraktionslos_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> fraktionslos_hitler_comparison
 spd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> spd_hitler_comparison
 na_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> na_hitler_comparison
 #not unique
 tibble(fraction = c("AfD", "AfD&Fraktionslos", "BÜNDNIS 90 / DIE GRÜNEN", "CDU/CSU", "DIE LINKE", "FDP", "Fraktionslos", "SPD"),
       absolute = c(nrow(afd_hitler_comparison), nrow(afdundfraktionslos_hitler_comparison), nrow(grüne_hitler_comparison), nrow(cdu_hitler_comparison), nrow(linke_hitler_comparison), nrow(fdp_hitler_comparison), nrow(fraktionslos_hitler_comparison), nrow(spd_hitler_comparison)),
       total = c(nrow(afd_words), nrow(afdundfraktionslos_words), nrow(grüne_words), nrow(cdu_words), nrow(linke_words), nrow(fdp_words), nrow(fraktionslos_words), nrow(spd_words))
      ) %>% mutate(percent = 100*absolute/total) -> hitler_comparison
 hitler_comparison
 ```
 Finally, we want to plot our results:
 ```{r, fig.width=7}
 bar_plot_fractions(hitler_comparison, y_variable = percent, title="Coincidence of party vocabulary with nazi vocabulary", ylab="unique 'nazi' words per total (unique) fraction words [%]")
 ```
@@ -0,0 +1,107 @@
 ---
 title: "Interaction between fractions"
 output: rmarkdown::html_vignette
 vignette: >
  %\VignetteIndexEntry{Interaction between fractions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
 )
 ```
 ```{r setup}
 library(hateimparlament)
 library(dplyr)
 library(ggplot2)
 library(stringr)
 library(tidyr)
 ```
 ## Preparation of data
 First, you need to download all records of the current legislative period.
 ```r
 fetch_all("../inst/records/") # path to directory where records should be stored
 ```
 Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
 ```r
 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.
 ```{r}
 res <- read_from_csv('../inst/csv/')
 ```
 ## Analysis
 Now we can start analysing our parsed dataset:
 ### Which party gives the most applause to which parties?
 ```{r}
 res$applause %>%
    left_join(res$speaker, by=c("on_speaker" = "id")) %>%
    select(on_fraction = fraction, where(is.logical)) %>%
    group_by(on_fraction) %>%
    arrange(on_fraction) %>%
    summarize("AfD" = sum(`AfD`),
              "BÜNDNIS 90/DIE GRÜNEN" = sum(`BUENDNIS_90_DIE_GRUENEN`),
              "CDU/CSU" = sum(`CDU_CSU`),
              "DIE LINKE" = sum(`DIE_LINKE`),
              "FDP" = sum(`FDP`),
              "SPD" = sum(`SPD`)) -> tb
 ```
 For plotting our results we reorganize them a bit and produce a bar plot:
 ```{r, fig.width=7, fig.height=6}
 pivot_longer(tb, where(is.numeric), "by_fraction", "count") %>%
    filter(!is.na(on_fraction)) %>%
    bar_plot_fractions(x_variable = on_fraction,
                        y_variable = value,
                        fill = by_fraction,
                        title = "Number of rounds of applauses from fractions to fractions",
                        xlab = "Applauded fraction",
                        ylab = "Rounds of applauses",
                        filllab = "Applauding fraction",
                        flipped = FALSE,
                        rotatelab = TRUE)
 ```
 ### Which party comments the most on which parties?
 ```{r}
 res$comments %>%
    left_join(res$speaker, by=c("on_speaker" = "id")) %>%
    select(by_fraction = fraction.x, on_fraction = fraction.y) %>%
    group_by(on_fraction) %>%
    summarize(`AfD` = sum(str_detect(by_fraction, "AfD"), na.rm=T),
              `BÜNDNIS 90/DIE GRÜNEN` = sum(str_detect(by_fraction, "BÜNDNIS 90/DIE GRÜNEN"), na.rm=T),
              `CDU/CSU` = sum(str_detect(by_fraction, "CDU/CSU"), na.rm = T),
              `DIE LINKE` = sum(str_detect(by_fraction, "DIE LINKE"), na.rm=T),
              `FDP` = sum(str_detect(by_fraction, "FDP"), na.rm=T),
              `SPD` = sum(str_detect(by_fraction, "SPD"), na.rm=T)) -> tb
 ```
 Analogously we plot the results:
 ```{r, fig.width=7, fig.height=6}
 pivot_longer(tb, where(is.numeric), "by_fraction", "count") %>%
    filter(!is.na(on_fraction)) %>%
    bar_plot_fractions(x_variable = on_fraction,
                        y_variable = value,
                        fill = by_fraction,
                        title = "Number of comments from fractions to fractions",
                        xlab = "Commented fraction",
                        ylab = "Number of comments",
                        filllab = "Commenting fraction",
                        flipped = FALSE,
                        rotatelab = TRUE)
 ```
Author	SHA1	Message	Date
christian	9d456bfa60	Merge pull request 'add masterdata from bundestag.de and use this for genderequality' (#16 ) from genderequality-alternative into master	2021-08-11 08:30:27 +02:00
christian	62fe9d497d	Merge branch 'master' into genderequality-alternative	2021-08-10 23:28:25 +02:00
christian	29a7974941	change title of vignettes, and add install instructions for vignettes	2021-08-10 22:47:07 +02:00
christian	e31ccabf18	change title in genderequality	2021-08-10 22:46:41 +02:00
christian	622fd4db07	Merge branch 'master' into genderequality-alternative	2021-08-10 22:03:37 +02:00
christian	6aa80534f8	add checks to read/write csv and refactor	2021-08-10 21:51:00 +02:00
christian	605e5e976a	Merge branch 'master' into genderequality-alternative	2021-08-10 21:27:10 +02:00
christian	8e691e5d11	update package meta data	2021-08-10 21:24:39 +02:00
christian	6b1f8a64b2	add installation directives	2021-08-10 21:14:52 +02:00
christian	7e304d12bb	fix formatting in readme and improve analysis section	2021-08-10 20:58:29 +02:00
christian	864c0016cc	update readme	2021-08-10 20:54:37 +02:00
christian	489254dadf	Merge branch 'master' into genderequality-alternative	2021-08-10 20:19:02 +02:00
christian	05755f9737	Merge branch 'master' of gitea:christian/hateimparlament	2021-08-10 20:18:33 +02:00
christian	01ec0de76f	replace non ascii characters by unicode points	2021-08-10 20:18:23 +02:00
JosuaKugler	fbfbed3900	abschlussbericht erster entwurf	2021-08-10 19:48:14 +02:00
christian	b09742815a	Merge branch 'master' into genderequality-alternative	2021-08-10 17:54:04 +02:00
christian	b11289e093	replace Ü with unicode equivalent	2021-08-10 17:53:45 +02:00
christian	7daf9e553c	Merge branch 'master' into genderequality-alternative	2021-08-10 17:34:29 +02:00
christian	d91403f0c8	add gpl3 license	2021-08-10 17:34:18 +02:00
christian	d657ca3fbe	Merge branch 'master' into genderequality-alternative	2021-08-10 17:26:49 +02:00
christian	c53d842a1e	add missing function to genderequality	2021-08-10 17:26:05 +02:00
christian	12452df224	update man	2021-08-10 17:05:19 +02:00
christian	f753920d34	add masterdata from bundestag.de and use this for genderequality	2021-08-10 17:04:43 +02:00
christian	77f79f0d86	change spelling of greens fraction, fix parsing issue in comments table	2021-08-10 15:40:21 +02:00
christian	c80ec6259e	add additional parameter checks for analyze methods	2021-08-10 14:39:35 +02:00
christian	8e2a9bd399	refactor bar_plot_fractions and add error handling	2021-08-10 14:08:09 +02:00
christian	e038d86339	add check for valid structure of tables	2021-08-10 12:42:41 +02:00
christian	4093cb603c	add further checks for invalid records	2021-08-10 11:22:29 +02:00
christian	1152115a0f	add record existence checks in read_all	2021-08-09 18:38:56 +02:00
christian	4dbf90127a	remove unused name argument in word_usage_by_date	2021-08-09 17:08:37 +02:00
christian	2a9035e630	convert chr to date when reading from csv	2021-08-09 16:08:56 +02:00
christian	5490f9fed6	remove funwithdata, add some text, improve some fig heights	2021-08-09 16:08:33 +02:00
christian	4649658fa7	rename some columns to english	2021-08-09 15:31:20 +02:00
christian	b02ab91c31	Merge branch 'master' of gitea:christian/hateimparlament	2021-08-09 15:10:32 +02:00
christian	03f8ca0813	add implementation beamer slides	2021-08-09 15:10:23 +02:00
Leon Burgard	4d00f4d1b0	update README.md	2021-08-09 11:13:49 +02:00
Leon Burgard	545e5c9bab	add rotatelab to bar_plot_fractions	2021-08-09 11:00:14 +02:00
JosuaKugler	1db9e3f59a	add plots in genderequality and clean up hitlercomparison	2021-08-08 22:35:08 +02:00
Leon Burgard	1546f84d80	update README	2021-08-08 20:32:28 +02:00
JosuaKugler	0597f68694	change to bar_plot_fractions	2021-08-08 15:41:26 +02:00
Leon Burgard	d5f74c5bf3	organized vignettes	2021-08-08 00:49:26 +02:00
Leon Burgard	0f07b002e6	Improve genderequality	2021-08-08 00:16:05 +02:00
Leon Burgard	789bf57756	Analyse in genderequality	2021-08-07 23:38:15 +02:00
JosuaKugler	502dc45781	refactor again because of check complaining	2021-08-07 02:00:09 +02:00
JosuaKugler	4b3e85be62	adapting genderequality csv dir to new project structure	2021-08-07 01:40:37 +02:00
JosuaKugler	feed583fa9	Merge branch 'master' of https://git.flavigny.de/christian/hateimparlament	2021-08-07 01:36:06 +02:00
JosuaKugler	53fdb7530b	refactor project structure	2021-08-07 01:16:56 +02:00
JosuaKugler	bf30511678	add documentation to read_from_csv and write_to_csv	2021-08-07 00:37:23 +02:00
JosuaKugler	5e9f70627d	fix errors from refactoring and add documentation to all remaining functions in analyze.R	2021-08-07 00:31:56 +02:00
Leon Burgard	d5da636020	add documentations	2021-08-06 19:07:35 +02:00
Leon Burgard	1433159e09	create genderequality, add documentation read_from_csv	2021-08-05 13:44:19 +02:00
Leon Burgard	c22bc0b91b	add Documentation for write_to_csv	2021-08-04 16:47:06 +02:00
JosuaKugler	5f9343bf7f	Merge branch 'master' of https://git.flavigny.de/christian/hateimparlament	2021-08-03 18:28:59 +02:00
JosuaKugler	a0df02dbed	refactor fraktion -> fraction	2021-08-03 17:11:11 +02:00
JosuaKugler	7315dd8793	refactor rede -> speech, redner -> speaker	2021-08-03 17:05:07 +02:00
christian	5dc308c16e	generalize lookup_redner helper to allow lookup of names in arbitrary tables, add info text	2021-08-03 13:40:02 +02:00
christian	7771b2ebd8	add option to lookup redner in repair and fix fraction list to ensure repaired tables are fixpoints of repair(, repair_comments = FALSE)	2021-08-03 13:26:39 +02:00
christian	f91456116f	remove outdated example code	2021-08-03 12:48:32 +02:00
christian	64ede5c757	replace Ü with UE in colnames in applause, move check of empty directory to beginning in read_all	2021-08-03 12:48:01 +02:00
JosuaKugler	09f5e5da0d	adapt barplot syntax	2021-08-03 11:54:21 +02:00
christian	610982427a	fix index entry in hiltercomparison vignette	2021-08-03 11:33:36 +02:00
christian	e2caf1ff4e	improve bar plot fraktionen helper	2021-08-03 11:33:24 +02:00
JosuaKugler	354d18050b	adapt bar plot syntax, improve labels, improve fraction specific vocabulary selection	2021-08-03 00:33:50 +02:00
JosuaKugler	7dd136b869	flip plot direction and extend README	2021-08-02 23:26:08 +02:00
JosuaKugler	834a840734	Merge branch 'master' of https://git.flavigny.de/christian/hateimparlament	2021-08-02 22:36:33 +02:00
JosuaKugler	0a97674b51	add 10 most used specific words of fraktionen	2021-08-02 22:36:30 +02:00
JosuaKugler	ef29269d45	improve german word selection	2021-08-02 22:35:02 +02:00
christian	6e8e942030	Merge branch 'master' of gitea:christian/hateimparlament	2021-08-02 22:06:50 +02:00
christian	ccb0649cb9	fix funwithdata vignette, generalize bar plot helper	2021-08-02 22:06:34 +02:00
JosuaKugler	7067877584	Merge branch 'master' of https://git.flavigny.de/christian/hateimparlament	2021-08-02 19:01:41 +02:00
JosuaKugler	002ee56853	changes	2021-08-02 16:23:57 +02:00
christian	4fe45feec9	solve most of predefined challenges	2021-08-02 16:12:18 +02:00
JosuaKugler	75042d7128	correct hitler_words and start analysis	2021-08-02 15:02:25 +02:00
JosuaKugler	88a62a22d7	parse german words and extract hitler words	2021-08-02 12:39:34 +02:00