vignettes/genderequality.Rmd

@@ -20,7 +20,7 @@ library(dplyr)
 library(ggplot2)
 library(stringr)
 library(tidyr)
-library(rvest)
+library(xml2)
 ```
 
 ## Preparation of data
@@ -33,8 +33,7 @@ Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomp
 ```r
 read_all("../records/") %>% repair() -> res
 ```
-We also used `repair` to fix a bunch of formatting issues in the records and unpacked
+We also used `repair` to fix a bunch of formatting issues in the records.
-the result into more descriptive variables.
 
 For development purposes, we load the tables from csv files.
 ```{r}
@@ -48,53 +47,27 @@ speaker <- res$speaker
 talks <- res$talks
 ```
 
-Bevor we can do our analysis, we have to assign a gender to our politicans.
+Bevor we can do our analysis, we have to assign a gender to our politicans. We do this
+by reading the gender from the master data of all members of parliament, which is
+fetched from bundestag.de.
 
 ```{r}
-extract_href <- function(sel, html) {
+x <- read_xml("../inst/masterdata.xml")
-  html %>%
+mdbs <- xml_find_all(x, "MDB")
-    html_node(sel) %>%
+
-    html_attr("href")
+ids <- c()
+genders <- c()
+for (mdb in mdbs) {
+    xml_get(mdb, "ID") -> mdb_id
+    xml_find_first(mdb, "BIOGRAFISCHE_ANGABEN") %>%
+        xml_get("GESCHLECHT") ->
+        mdb_gender
+    ids <- c(ids, mdb_id)
+    genders <- c(genders, if (mdb_gender == "männlich") "male" else "female")
 }
 
-first_content_p_text <- function(url) {
+gender <- tibble(id = ids, gender = genders)
-  res <- NA
+speaker_with_gender <- left_join(res$speaker, gender)
-  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)
-
-speaker %>% 
-  unite("speaker", vorname, nachname, sep = " ") %>% 
-  right_join(gender, by = "speaker") -> 
-  speaker_with_gender
 ```
 
 ## Analyse
@@ -179,9 +152,8 @@ speeches %>%
   summarize(n = n()) %>%
   ungroup() %>% 
   arrange(-n) %>%
-  left_join(speaker, by=c("speaker" = "id")) %>% 
+  join_speaker(res) %>%
-  unite(name, vorname, nachname, sep = " ") %>% 
+  left_join(gender, by=c("speaker"="id")) %>% 
-  inner_join(gender, by=c("name"= "speaker")) %>% 
   group_by(gender) %>% 
   summarise(absolute=sum(n)) %>%
   filter(gender %in% c("female", "male")) %>%