organized vignettes

vignettes/explicittopic.Rmd

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+---
+title: "explicittopic"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{explicittopic}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(hateimparlament)
+library(dplyr)
+library(ggplot2)
+library(stringr)
+library(tidyr)
+```
+
+## Preparation of data
+
+First, you need to download all records of the current legislative period.
+```r
+fetch_all("../inst/records/") # path to directory where records should be stored
+```
+Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
+```r
+read_all("../inst/records/") %>% repair() -> res
+```
+We also used `repair` to fix a bunch of formatting issues in the records and unpacked
+the result into more descriptive variables.
+
+For development purposes, we load the tables from csv files.
+```{r}
+res <- read_from_csv('../inst/csv/')
+```
+and unpack our tibbles
+```{r}
+comments <- res$comments
+speeches <- res$speeches
+speaker <- res$speaker
+talks <- res$talks
+```
+
+## Analysis
+
+Now we can start analysing our parsed dataset:
+
+### Counting the occurences of a given word:
+
+```{r, fig.width=7}
+find_word(res, "Kohleausstieg") %>%
+    filter(occurences > 0) %>%
+    join_speaker(res) %>%
+    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)",
+                        ylab = "Number of uses of 'Kohleausstieg'",
+                        flipped = F)
+```
+
+### When are which topics discussed the most?
+
+```{r, fig.width=7}
+pandemic_pattern <- "(?i)virus|corona|covid|lockdown"
+climate_pattern <- "(?i)klimawandel|erderwärmung|co2|treibhaus|methan|kyoto-protokoll|klimaabkommen"
+pension_pattern <- "(?i)rente|pension|altersarmut"
+
+word_usage_by_date(res, c(pandemic = pandemic_pattern,
+                          climate = climate_pattern,
+                          pension = pension_pattern)) %>%
+    ggplot(aes(x = date, y = count, color = pattern)) +
+    xlab("date of session") +
+    ylab("occurence of word per session") +
+    labs(color = "Topic") +
+    geom_point()
+```