refactor fraktion -> fraction
This commit is contained in:
+6
-6
@@ -6,9 +6,9 @@ find_word <- function(res, word) {
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}
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#' @export
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join_speaker <- function(tb, res, fraktion_only = F) {
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join_speaker <- function(tb, res, fraction_only = F) {
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joined <- left_join(tb, res$speaker, by=c("speaker" = "id"))
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if (fraktion_only) select(joined, "fraktion")
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if (fraction_only) select(joined, "fraction")
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else joined
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}
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@@ -30,9 +30,9 @@ party_order <- factor(c("Fraktionslos", "AfD&Fraktionslos",
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#' @export
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bar_plot_fractions <- function(tb,
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x_variable = NULL, # default is fraktion
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x_variable = NULL, # default is fraction
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y_variable = NULL, # default is n
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fill = NULL, # default is fraktion
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fill = NULL, # default is fraction
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title = NULL,
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xlab = "Fraction",
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ylab = "n",
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@@ -46,9 +46,9 @@ bar_plot_fractions <- function(tb,
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x_variable <- enexpr(x_variable)
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# set default values
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if (is.null(fill)) fill <- expr(fraktion)
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if (is.null(fill)) fill <- expr(fraction)
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if (is.null(y_variable)) y_variable <- expr(n)
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if (is.null(x_variable)) x_variable <- expr(fraktion)
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if (is.null(x_variable)) x_variable <- expr(fraction)
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# either reorder fraction factor by variable value
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if (reorder) maps <- aes(x = reorder(!!x_variable, -!!y_variable),
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@@ -42,13 +42,13 @@ read_all <- function(path="records/") {
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comments
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filter(commentsandapplause, type == "applause") %>%
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select(-type, -kommentator, -content) %>%
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mutate("CDU_CSU" = str_detect(fraktion, "CDU/CSU"),
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"SPD" = str_detect(fraktion, "SPD"),
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"FDP" = str_detect(fraktion, "FDP"),
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"DIE_LINKE" = str_detect(fraktion, "DIE LINKE"),
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"BÜNDNIS_90_DIE_GRÜNEN" = str_detect(fraktion, "BÜNDNIS 90/DIE GRÜNEN"),
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"AfD" = str_detect(fraktion, "AfD")) %>%
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select(-fraktion) ->
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mutate("CDU_CSU" = str_detect(fraction, "CDU/CSU"),
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"SPD" = str_detect(fraction, "SPD"),
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"FDP" = str_detect(fraction, "FDP"),
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"DIE_LINKE" = str_detect(fraction, "DIE LINKE"),
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"BÜNDNIS_90_DIE_GRÜNEN" = str_detect(fraction, "BÜNDNIS 90/DIE GRÜNEN"),
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"AfD" = str_detect(fraction, "AfD")) %>%
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select(-fraction) ->
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applause
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list(speaker = speaker, speeches = speeches, talks = talks, comments = comments, applause = applause)
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@@ -90,14 +90,14 @@ parse_speaker <- function(speaker_xml) {
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nm <- xml_child(speaker_xml)
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vorname <- xml_get(nm, "vorname")
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nachname <- xml_get(nm, "nachname")
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fraktion <- xml_get(nm, "fraktion")
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fraction <- xml_get(nm, "fraction")
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titel <- xml_get(nm, "titel")
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rolle <- xml_find_all(nm, "rolle")
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if (length(rolle) > 0) {
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rolle_lang <- xml_get(rolle, "rolle_lang")
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rolle_kurz <- xml_get(rolle, "rolle_kurz")
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} else rolle_kurz <- rolle_lang <- NA_character_
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c(id = speaker_id, vorname = vorname, nachname = nachname, fraktion = fraktion, titel = titel,
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c(id = speaker_id, vorname = vorname, nachname = nachname, fraction = fraction, titel = titel,
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rolle_kurz = rolle_kurz, rolle_lang = rolle_lang)
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}
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@@ -155,22 +155,22 @@ parse_speech <- function(speech_xml, date) {
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comments = comments)
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}
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fraktionspattern <- "BÜNDNIS(SES)?\\W*90/DIE\\W*GRÜNEN|CDU/CSU|AfD|SPD|DIE LINKE|FDP|LINKEN"
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fraktionsnames <- c("BÜNDNIS 90/DIE GRÜNEN", "CDU/CSU", "AfD", "SPD", "DIE LINKE", "FDP")
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fractionpattern <- "BÜNDNIS(SES)?\\W*90/DIE\\W*GRÜNEN|CDU/CSU|AfD|SPD|DIE LINKE|FDP|LINKEN"
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fractionnames <- c("BÜNDNIS 90/DIE GRÜNEN", "CDU/CSU", "AfD", "SPD", "DIE LINKE", "FDP")
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parse_comment <- function(comment, speech_id, on_speaker) {
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base <- c(speech_id = speech_id, on_speaker = on_speaker)
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# classify comment
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if(str_detect(comment, "Beifall")) {
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str_extract_all(comment, fraktionspattern) %>%
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str_extract_all(comment, fractionpattern) %>%
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`[[`(1) %>%
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sapply(partial(flip(head), 1) %.% agrep, x=fraktionsnames, max=0.2, value=T) %>%
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sapply(partial(flip(head), 1) %.% agrep, x=fractionnames, max=0.2, value=T) %>%
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str_c(collapse=",") ->
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by
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c(base, type = "applause", fraktion = by, kommentator = NA_character_, content = comment)
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c(base, type = "applause", fraction = by, kommentator = NA_character_, content = comment)
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} else {
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ps <- str_match(comment, "(.*) \\[(.*?)\\]: (.*)")[1,]
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c(base, type = "comment", fraktion = ps[3], kommentator = ps[2], content = ps[4])
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c(base, type = "comment", fraction = ps[3], kommentator = ps[2], content = ps[4])
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}
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}
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@@ -188,7 +188,7 @@ parse_speechlist <- function(speechlist_xml, date) {
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comments = tibble(speech_id = comments["speech_id",],
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on_speaker = comments["on_speaker",],
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type = comments["type",],
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fraktion = comments["fraktion",],
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fraction = comments["fraction",],
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kommentator = comments["kommentator",],
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content = comments["content", ]))
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}
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@@ -199,7 +199,7 @@ parse_speakerliste <- function(speakerliste_xml) {
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tibble(id = d["id",],
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vorname = d["vorname",],
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nachname = d["nachname",],
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fraktion = d["fraktion",],
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fraction = d["fraction",],
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titel = d["titel",],
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rolle_kurz = d["rolle_kurz",],
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rolle_lang = d["rolle_lang",])
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+7
-7
@@ -1,4 +1,4 @@
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fraktionen <- c("AFD" = "AfD",
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fractions <- c("AFD" = "AfD",
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"BÜNDNIS90/" = "BÜNDNIS 90 / DIE GRÜNEN",
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"BÜNDNIS90/DIEGRÜNEN" = "BÜNDNIS 90 / DIE GRÜNEN",
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"FRAKTIONSLOS" = "Fraktionslos",
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@@ -7,9 +7,9 @@ fraktionen <- c("AFD" = "AfD",
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"CDU/CSU" = "CDU/CSU",
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"FDP" = "FDP")
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repair_fraktion <- function(fraktion) {
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cleaned <- str_to_upper %$% str_replace_all(fraktion, "\\s", "")
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fraktionen[cleaned]
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repair_fraction <- function(fraction) {
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cleaned <- str_to_upper %$% str_replace_all(fraction, "\\s", "")
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fractions[cleaned]
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}
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# takes vector of titel and keeps longest
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@@ -26,17 +26,17 @@ repair_speaker <- function(speaker) {
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if (nrow(speaker) == 0) return(speaker)
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speaker %>%
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filter(id != "10000") %>% # invalid id's
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mutate(fraktion = Vectorize(repair_fraktion)(fraktion)) %>% # fix fraktion
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mutate(fraction = Vectorize(repair_fraction)(fraction)) %>% # fix fraction
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group_by(id) %>%
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summarize(vorname = head(vorname, 1),
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nachname = head(nachname, 1),
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fraktion = collect_unique(fraktion),
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fraction = collect_unique(fraction),
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titel = longest_titel(titel),
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rolle_kurz = collect_unique(str_squish(rolle_kurz)),
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rolle_lang = collect_unique(str_squish(rolle_lang))) %>%
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ungroup() #%>%
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# arrange(id) %>%
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# distinct(vorname, nachname, fraktion, titel)
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# distinct(vorname, nachname, fraction, titel)
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}
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repair_speeches <- function(speeches) {
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@@ -44,7 +44,7 @@ parse.R parsed einzelne Protokolle und erstellt 3 Tibbles
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### Redner
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Struktur: `id` , `vorname` , `nachname` , `fraktion` , `titel` , `rolle_kurz`, `rolle_lang`
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Struktur: `id` , `vorname` , `nachname` , `fraction` , `titel` , `rolle_kurz`, `rolle_lang`
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Die Rollen sind beispielsweise "Bundeskanzlerin". Leider gegendert und deshalb wahrscheinlich
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nervig zu analysieren.
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+23
-23
@@ -52,7 +52,7 @@ talks <- res$talks
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Now we can start analysing our parsed dataset, e.g. find out which party gives the most talks:
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```{r, fig.width=7}
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join_speaker(res$speeches, res) %>%
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group_by(fraktion) %>%
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group_by(fraction) %>%
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summarize(n = n()) %>%
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arrange(n) %>%
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bar_plot_fractions(title="Number of speeches given by fraction",
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@@ -65,9 +65,9 @@ or counting the occurences of a given word:
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find_word(res, "Kohleausstieg") %>%
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filter(occurences > 0) %>%
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join_speaker(res) %>%
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select(content, fraktion) %>%
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filter(!is.na(fraktion)) %>%
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group_by(fraktion) %>%
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select(content, fraction) %>%
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filter(!is.na(fraction)) %>%
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group_by(fraction) %>%
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summarize(n = n()) %>%
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arrange(desc(n)) %>%
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bar_plot_fractions(title = "Parties using the word 'Kohleausstieg' the most (absolutely)",
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@@ -103,9 +103,9 @@ res$talks %>%
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```{r}
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res$applause %>%
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left_join(res$speaker, by=c("on_speaker" = "id")) %>%
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select(on_fraktion = fraktion, where(is.logical)) %>%
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group_by(on_fraktion) %>%
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arrange(on_fraktion) %>%
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select(on_fraction = fraction, where(is.logical)) %>%
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group_by(on_fraction) %>%
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arrange(on_fraction) %>%
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summarize("AfD" = sum(`AfD`),
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"BÜNDNIS 90 / DIE GRÜNEN" = sum(`BÜNDNIS_90_DIE_GRÜNEN`),
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"CDU/CSU" = sum(`CDU_CSU`),
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@@ -117,11 +117,11 @@ res$applause %>%
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For plotting our results we reorganize them a bit and produce a bar plot:
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```{r, fig.width=7}
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pivot_longer(tb, where(is.numeric), "by_fraktion", "count") %>%
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filter(!is.na(on_fraktion)) %>%
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bar_plot_fractions(x_variable = on_fraktion,
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pivot_longer(tb, where(is.numeric), "by_fraction", "count") %>%
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filter(!is.na(on_fraction)) %>%
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bar_plot_fractions(x_variable = on_fraction,
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y_variable = value,
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fill = by_fraktion,
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fill = by_fraction,
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title = "Number of rounds of applauses from fractions to fractions",
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xlab = "Applauded fraction",
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ylab = "Rounds of applauses",
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@@ -135,23 +135,23 @@ pivot_longer(tb, where(is.numeric), "by_fraktion", "count") %>%
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```{r}
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res$comments %>%
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left_join(res$speaker, by=c("on_speaker" = "id")) %>%
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select(by_fraktion = fraktion.x, on_fraktion = fraktion.y) %>%
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group_by(on_fraktion) %>%
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summarize(`AfD` = sum(str_detect(by_fraktion, "AfD"), na.rm=T),
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`BÜNDNIS 90 / DIE GRÜNEN` = sum(str_detect(by_fraktion, "BÜNDNIS 90/DIE GRÜNEN"), na.rm=T),
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`CDU/CSU` = sum(str_detect(by_fraktion, "CDU/CSU"), na.rm = T),
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`DIE LINKE` = sum(str_detect(by_fraktion, "DIE LINKE"), na.rm=T),
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`FDP` = sum(str_detect(by_fraktion, "FDP"), na.rm=T),
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`SPD` = sum(str_detect(by_fraktion, "SPD"), na.rm=T)) -> tb
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select(by_fraction = fraction.x, on_fraction = fraction.y) %>%
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group_by(on_fraction) %>%
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summarize(`AfD` = sum(str_detect(by_fraction, "AfD"), na.rm=T),
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`BÜNDNIS 90 / DIE GRÜNEN` = sum(str_detect(by_fraction, "BÜNDNIS 90/DIE GRÜNEN"), na.rm=T),
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`CDU/CSU` = sum(str_detect(by_fraction, "CDU/CSU"), na.rm = T),
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`DIE LINKE` = sum(str_detect(by_fraction, "DIE LINKE"), na.rm=T),
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`FDP` = sum(str_detect(by_fraction, "FDP"), na.rm=T),
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`SPD` = sum(str_detect(by_fraction, "SPD"), na.rm=T)) -> tb
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```
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Analogously we plot the results:
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```{r, fig.width=7}
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pivot_longer(tb, where(is.numeric), "by_fraktion", "count") %>%
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filter(!is.na(on_fraktion)) %>%
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bar_plot_fractions(x_variable = on_fraktion,
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pivot_longer(tb, where(is.numeric), "by_fraction", "count") %>%
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filter(!is.na(on_fraction)) %>%
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bar_plot_fractions(x_variable = on_fraction,
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y_variable = value,
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fill = by_fraktion,
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fill = by_fraction,
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title = "Number of comments from fractions to fractions",
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xlab = "Commented fraction",
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ylab = "Number of comments",
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@@ -61,53 +61,53 @@ Now we extract the words that were used with higher frequency by one party and c
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```{r}
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talks %>%
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left_join(speaker, by=c(speaker='id')) %>%
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group_by(fraktion) %>%
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summarize(full_text=str_c(content, collapse="\n")) -> talks_by_fraktion
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group_by(fraction) %>%
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summarize(full_text=str_c(content, collapse="\n")) -> talks_by_fraction
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```
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For each party, we want to get a tibble of words with frequency.
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```{r}
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#AfD
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Worte <- str_extract_all(talks_by_fraktion$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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afdtotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/afdtotal) -> afd_words
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#AfD&Fraktionslos
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Worte <- str_extract_all(talks_by_fraktion$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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afdundfraktionslostotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/afdundfraktionslostotal) -> afdundfraktionslos_words
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#BÜNDNIS 90 / DIE GRÜNEN
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Worte <- str_extract_all(talks_by_fraktion$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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grünetotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/grünetotal) -> grüne_words
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#CDU/CSU
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Worte <- str_extract_all(talks_by_fraktion$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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cdutotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/cdutotal) -> cdu_words
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#DIE LINKE
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Worte <- str_extract_all(talks_by_fraktion$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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linketotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/linketotal) -> linke_words
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#FDP
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Worte <- str_extract_all(talks_by_fraktion$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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fdptotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/fdptotal) -> fdp_words
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#Fraktionslos
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Worte <- str_extract_all(talks_by_fraktion$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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fraktionslostotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/fraktionslostotal) -> fraktionslos_words
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#SPD
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Worte <- str_extract_all(talks_by_fraktion$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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spdtotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/spdtotal) -> spd_words
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#NA
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Worte <- str_extract_all(talks_by_fraktion$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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Worte <- str_extract_all(talks_by_fraction$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
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natotal = length(Worte)
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tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/natotal) -> na_words
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@@ -117,34 +117,34 @@ total <- sum(all_words$n)
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all_words %>% group_by(Worte) %>% summarize(n = sum(n), part= sum(n)/total) -> all_words
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```
|
||||
|
||||
Now we want to extract the words that are more frequently used by a specific `fraktion`.
|
||||
Now we want to extract the words that are more frequently used by a specific fraction.
|
||||
```{r}
|
||||
afd_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> afd_high_frequent
|
||||
select(afd_high_frequent, fraktion_n, total_n, abs_quotient, rel_quotient) %>% filter(total_n > 80)
|
||||
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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> afdundfraktionslos_high_frequent
|
||||
select(afdundfraktionslos_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> grüne_high_frequent
|
||||
select(grüne_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> cdu_high_frequent
|
||||
select(cdu_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> linke_high_frequent
|
||||
select(linke_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> fdp_high_frequent
|
||||
select(fdp_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> fraktionslos_high_frequent
|
||||
select(fraktionslos_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> spd_high_frequent
|
||||
select(spd_high_frequent, fraktion_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, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> na_high_frequent
|
||||
select(na_high_frequent, fraktion_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`.
|
||||
@@ -161,7 +161,7 @@ spd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerw
|
||||
na_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> na_hitler_comparison
|
||||
|
||||
#not unique
|
||||
tibble(fraktion = c("AfD", "AfD&Fraktionslos", "BÜNDNIS 90 / DIE GRÜNEN", "CDU/CSU", "DIE LINKE", "FDP", "Fraktionslos", "SPD"),
|
||||
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
|
||||
|
||||
Reference in New Issue
Block a user