hateimparlament/vignettes/genderequality.Rmd

---
title: "genderequality"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{genderequality}
  %\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(rvest)
```

## 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('../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.

```{r}
extract_href <- function(sel, html) {
  html %>%
    html_node(sel) %>%
    html_attr("href")
}

first_content_p_text <- function(url) {
  res <- NA
  i <- 1
  while(is.na(res)) {
    read_html(url) %>% 
      html_node(str_glue("#mw-content-text > div.mw-parser-output > p:nth-child({i})"))  %>% 
      html_text() -> res
    i <- i + 1
  }
  res
}

abgeordneten_list_html <- read_html(
  "https://de.wikipedia.org/wiki/Liste_der_Mitglieder_des_Deutschen_Bundestages_(19._Wahlperiode)")

selectors <- str_glue("#mw-content-text > div.mw-parser-output > table:nth-child(20) > tbody > tr:nth-child({2:709}) > td:nth-child(2) > a")
link_part2 <- sapply(selectors, extract_href, abgeordneten_list_html)
link <- str_c("https://de.wikipedia.org", link_part2)

text <- sapply(link, first_content_p_text)
text %>% 
  str_extract(" ist ein.") %>% 
  str_replace(" ist eine", "female") %>% 
  str_replace(" ist ein ", "male") ->
  gender

text %>% 
  str_extract("^([:upper:]?[:lower:]+[\\s\\-]?)*") %>% 
  str_trim() -> 
  names

gender <- tibble(speaker = names,
                 gender = gender)


```