refactor fraktion -> fraction

R/analyze.R

@@ -6,9 +6,9 @@ find_word <- function(res, word) {
 }
 
 #' @export
-join_speaker <- function(tb, res, fraktion_only = F) {
+join_speaker <- function(tb, res, fraction_only = F) {
     joined <- left_join(tb, res$speaker, by=c("speaker" = "id"))
-    if (fraktion_only) select(joined, "fraktion")
+    if (fraction_only) select(joined, "fraction")
     else joined
 }
 
@@ -30,9 +30,9 @@ party_order <- factor(c("Fraktionslos", "AfD&Fraktionslos",
 
 #' @export
 bar_plot_fractions <- function(tb,
-                                x_variable = NULL, # default is fraktion
+                                x_variable = NULL, # default is fraction
                                 y_variable = NULL, # default is n
-                                fill = NULL, # default is fraktion
+                                fill = NULL, # default is fraction
                                 title = NULL,
                                 xlab = "Fraction",
                                 ylab = "n",
@@ -46,9 +46,9 @@ bar_plot_fractions <- function(tb,
     x_variable <- enexpr(x_variable)
 
     # set default values
-    if (is.null(fill)) fill <- expr(fraktion)
+    if (is.null(fill)) fill <- expr(fraction)
     if (is.null(y_variable)) y_variable <- expr(n)
-    if (is.null(x_variable)) x_variable <- expr(fraktion)
+    if (is.null(x_variable)) x_variable <- expr(fraction)
 
     # either reorder fraction factor by variable value
     if (reorder) maps <- aes(x = reorder(!!x_variable, -!!y_variable),

R/parse.R

@@ -42,13 +42,13 @@ read_all <- function(path="records/") {
         comments
     filter(commentsandapplause, type == "applause") %>%
         select(-type, -kommentator, -content) %>%
-        mutate("CDU_CSU" = str_detect(fraktion, "CDU/CSU"),
-               "SPD" = str_detect(fraktion, "SPD"),
-               "FDP" = str_detect(fraktion, "FDP"),
-               "DIE_LINKE" = str_detect(fraktion, "DIE LINKE"),
-               "BÜNDNIS_90_DIE_GRÜNEN" = str_detect(fraktion, "BÜNDNIS 90/DIE GRÜNEN"),
-               "AfD" = str_detect(fraktion, "AfD")) %>%
-        select(-fraktion) ->
+        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"),
+               "BÜNDNIS_90_DIE_GRÜNEN" = str_detect(fraction, "BÜNDNIS 90/DIE GRÜNEN"),
+               "AfD" = str_detect(fraction, "AfD")) %>%
+        select(-fraction) ->
         applause
 
     list(speaker = speaker, speeches = speeches, talks = talks, comments = comments, applause = applause)
@@ -90,14 +90,14 @@ parse_speaker <- function(speaker_xml) {
     nm <- xml_child(speaker_xml)
     vorname <- xml_get(nm, "vorname")
     nachname <- xml_get(nm, "nachname")
-    fraktion <- xml_get(nm, "fraktion")
+    fraction <- xml_get(nm, "fraction")
     titel <- xml_get(nm, "titel")
     rolle <- xml_find_all(nm, "rolle")
     if (length(rolle) > 0) {
         rolle_lang <- xml_get(rolle, "rolle_lang")
         rolle_kurz <- xml_get(rolle, "rolle_kurz")
     } else rolle_kurz <- rolle_lang <- NA_character_
-    c(id = speaker_id, vorname = vorname, nachname = nachname, fraktion = fraktion, titel = titel,
+    c(id = speaker_id, vorname = vorname, nachname = nachname, fraction = fraction, titel = titel,
       rolle_kurz = rolle_kurz, rolle_lang = rolle_lang)
 }
 
@@ -155,22 +155,22 @@ parse_speech <- function(speech_xml, date) {
          comments = comments)
 }
 
-fraktionspattern <- "BÜNDNIS(SES)?\\W*90/DIE\\W*GRÜNEN|CDU/CSU|AfD|SPD|DIE LINKE|FDP|LINKEN"
-fraktionsnames <- c("BÜNDNIS 90/DIE GRÜNEN", "CDU/CSU", "AfD", "SPD", "DIE LINKE", "FDP")
+fractionpattern <- "BÜNDNIS(SES)?\\W*90/DIE\\W*GRÜNEN|CDU/CSU|AfD|SPD|DIE LINKE|FDP|LINKEN"
+fractionnames <- c("BÜNDNIS 90/DIE GRÜNEN", "CDU/CSU", "AfD", "SPD", "DIE LINKE", "FDP")
 
 parse_comment <- function(comment, speech_id, on_speaker) {
     base <- c(speech_id = speech_id, on_speaker = on_speaker)
     # classify comment
     if(str_detect(comment, "Beifall")) {
-        str_extract_all(comment, fraktionspattern) %>%
+        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
-        c(base, type = "applause", fraktion = by, kommentator = NA_character_, content = comment)
+        c(base, type = "applause", fraction = 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])
+        c(base, type = "comment", fraction = ps[3], kommentator = ps[2], content = ps[4])
     }
 }
 
@@ -188,7 +188,7 @@ parse_speechlist <- function(speechlist_xml, date) {
          comments = tibble(speech_id = comments["speech_id",],
                            on_speaker = comments["on_speaker",],
                            type = comments["type",],
-                           fraktion = comments["fraktion",],
+                           fraction = comments["fraction",],
                            kommentator = comments["kommentator",],
                            content = comments["content", ]))
 }
@@ -199,7 +199,7 @@ parse_speakerliste <- function(speakerliste_xml) {
     tibble(id = d["id",],
            vorname = d["vorname",],
            nachname = d["nachname",],
-           fraktion = d["fraktion",],
+           fraction = d["fraction",],
            titel = d["titel",],
            rolle_kurz = d["rolle_kurz",],
            rolle_lang = d["rolle_lang",])

R/repair.R

@@ -1,4 +1,4 @@
-fraktionen <- c("AFD" = "AfD",
+fractions <- c("AFD" = "AfD",
                 "BÜNDNIS90/" = "BÜNDNIS 90 / DIE GRÜNEN",
                 "BÜNDNIS90/DIEGRÜNEN" = "BÜNDNIS 90 / DIE GRÜNEN",
                 "FRAKTIONSLOS" = "Fraktionslos",
@@ -7,9 +7,9 @@ fraktionen <- c("AFD" = "AfD",
                 "CDU/CSU" = "CDU/CSU",
                 "FDP" = "FDP")
 
-repair_fraktion <- function(fraktion) {
-    cleaned <- str_to_upper %$% str_replace_all(fraktion, "\\s", "")
-    fraktionen[cleaned]
+repair_fraction <- function(fraction) {
+    cleaned <- str_to_upper %$% str_replace_all(fraction, "\\s", "")
+    fractions[cleaned]
 }
 
 # takes vector of titel and keeps longest
@@ -26,17 +26,17 @@ repair_speaker <- function(speaker) {
     if (nrow(speaker) == 0) return(speaker)
     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),
                   nachname = head(nachname, 1),
-                  fraktion = collect_unique(fraktion),
+                  fraction = collect_unique(fraction),
                   titel = longest_titel(titel),
                   rolle_kurz = collect_unique(str_squish(rolle_kurz)),
                   rolle_lang = collect_unique(str_squish(rolle_lang))) %>%
         ungroup() #%>%
         # arrange(id) %>%
-        # distinct(vorname, nachname, fraktion, titel)
+        # distinct(vorname, nachname, fraction, titel)
 }
 
 repair_speeches <- function(speeches) {

README.md

@@ -44,7 +44,7 @@ parse.R parsed einzelne Protokolle und erstellt 3 Tibbles
 
 ### Redner
 
-Struktur: `id` , `vorname` , `nachname` , `fraktion` , `titel` , `rolle_kurz`, `rolle_lang`
+Struktur: `id` , `vorname` , `nachname` , `fraction` , `titel` , `rolle_kurz`, `rolle_lang`
 
 Die Rollen sind beispielsweise "Bundeskanzlerin". Leider gegendert und deshalb wahrscheinlich
 nervig zu analysieren.

vignettes/funwithdata.Rmd

@@ -52,7 +52,7 @@ talks <- res$talks
 Now we can start analysing our parsed dataset, e.g. find out which party gives the most talks:
 ```{r, fig.width=7}
 join_speaker(res$speeches, res) %>%
-    group_by(fraktion) %>%
+    group_by(fraction) %>%
     summarize(n = n()) %>%
     arrange(n) %>%
     bar_plot_fractions(title="Number of speeches given by fraction",
@@ -65,9 +65,9 @@ or counting the occurences of a given word:
 find_word(res, "Kohleausstieg") %>%
     filter(occurences > 0) %>%
     join_speaker(res) %>%
-    select(content, fraktion) %>%
-    filter(!is.na(fraktion)) %>%
-    group_by(fraktion) %>%
+    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)",
@@ -103,9 +103,9 @@ res$talks %>%
 ```{r}
 res$applause %>%
     left_join(res$speaker, by=c("on_speaker" = "id")) %>%
-    select(on_fraktion = fraktion, where(is.logical)) %>%
-    group_by(on_fraktion) %>%
-    arrange(on_fraktion) %>%
+    select(on_fraction = fraction, where(is.logical)) %>%
+    group_by(on_fraction) %>%
+    arrange(on_fraction) %>%
     summarize("AfD" = sum(`AfD`),
               "BÜNDNIS 90 / DIE GRÜNEN" = sum(`BÜNDNIS_90_DIE_GRÜNEN`),
               "CDU/CSU" = sum(`CDU_CSU`),
@@ -117,11 +117,11 @@ res$applause %>%
 For plotting our results we reorganize them a bit and produce a bar plot:
 
 ```{r, fig.width=7}
-pivot_longer(tb, where(is.numeric), "by_fraktion", "count") %>%
-    filter(!is.na(on_fraktion)) %>%
-    bar_plot_fractions(x_variable = on_fraktion,
+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_fraktion,
+                        fill = by_fraction,
                         title = "Number of rounds of applauses from fractions to fractions",
                         xlab = "Applauded fraction",
                         ylab = "Rounds of applauses",
@@ -135,23 +135,23 @@ pivot_longer(tb, where(is.numeric), "by_fraktion", "count") %>%
 ```{r}
 res$comments %>%
     left_join(res$speaker, by=c("on_speaker" = "id")) %>%
-    select(by_fraktion = fraktion.x, on_fraktion = fraktion.y) %>%
-    group_by(on_fraktion) %>%
-    summarize(`AfD` = sum(str_detect(by_fraktion, "AfD"), na.rm=T),
-              `BÜNDNIS 90 / DIE GRÜNEN` = sum(str_detect(by_fraktion, "BÜNDNIS 90/DIE GRÜNEN"), na.rm=T),
-              `CDU/CSU` = sum(str_detect(by_fraktion, "CDU/CSU"), na.rm = T),
-              `DIE LINKE` = sum(str_detect(by_fraktion, "DIE LINKE"), na.rm=T),
-              `FDP` = sum(str_detect(by_fraktion, "FDP"), na.rm=T),
-              `SPD` = sum(str_detect(by_fraktion, "SPD"), na.rm=T)) -> tb
+    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}
-pivot_longer(tb, where(is.numeric), "by_fraktion", "count") %>%
-    filter(!is.na(on_fraktion)) %>%
-    bar_plot_fractions(x_variable = on_fraktion,
+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_fraktion,
+                        fill = by_fraction,
                         title = "Number of comments from fractions to fractions",
                         xlab = "Commented fraction",
                         ylab = "Number of comments",

vignettes/hitlercomparison.Rmd

@@ -61,53 +61,53 @@ Now we extract the words that were used with higher frequency by one party and c
 ```{r}
 talks %>% 
   left_join(speaker, by=c(speaker='id')) %>% 
-  group_by(fraktion) %>%
-  summarize(full_text=str_c(content, collapse="\n")) -> talks_by_fraktion
+  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_fraktion$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$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_fraktion$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+Worte <- str_extract_all(talks_by_fraction$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
 natotal = length(Worte)
 tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/natotal) -> na_words
 
@@ -117,34 +117,34 @@ 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 `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