hateimparlament/vignettes/hitlercomparison.Rmd

---
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)
```