correct hitler_words and start analysis

vignettes/hitlercomparison.Rmd

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+---
+title: "funwithdata"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{funwithdata}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(hateimparlament)
+library(dplyr)
+library(stringr)
+library(ggplot2)
+```
+
+## Preparation of data
+
+First, you need to download all records of the current legislative period.
+```r
+fetch_all("../records/") # path to directory where records should be stored
+```
+Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
+```r
+read_all("../records/") %>% repair() -> res
+
+reden <- res$reden
+redner <- res$redner
+talks <- res$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('../csv/')
+
+comments <- tables$comments
+reden <- tables$reden
+redner <- tables$redner
+talks <- tables$talks
+```
+
+Further, we need to load a list of words that were used by Hitler but not by standard German texts.
+```{r}
+fil <- file('../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(redner, by=c(redner='id')) %>% 
+  group_by(fraktion) %>%
+  summarize(full_text=str_c(content, collapse="\n")) -> talks_by_fraktion
+talks_by_fraktion
+```
+For each party, we want to get a tibble of words with frequency.
+```{r}
+#AfD
+Worte <- str_extract_all(talks_by_fraktion$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> afd_words
+
+#AfD&Fraktionslos
+Worte <- str_extract_all(talks_by_fraktion$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> afdundfraktionslos_words
+#BÜNDNIS 90 / DIE GRÜNEN
+Worte <- str_extract_all(talks_by_fraktion$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> grüne_words
+#CDU/CSU
+Worte <- str_extract_all(talks_by_fraktion$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> cdu_words
+#DIE LINKE
+Worte <- str_extract_all(talks_by_fraktion$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> linke_words
+#FDP
+Worte <- str_extract_all(talks_by_fraktion$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> fdp_words
+#Fraktionslos
+Worte <- str_extract_all(talks_by_fraktion$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> fraktionslos_words
+#SPD
+Worte <- str_extract_all(talks_by_fraktion$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> spd_words
+#NA
+Worte <- str_extract_all(talks_by_fraktion$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
+total = length(Worte)
+tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> na_words
+#alle
+all_words <- bind_rows(afd_words, afdundfraktionslos_words, grüne_words, cdu_words, linke_words, fdp_words, fraktionslos_words, spd_words, na_words)
+total <- sum(all_words$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 `fraktion`.
+```{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
+
+```
+
+We compare these words with `hitlerwords`.
+
+```{r}
+afd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords)
+```