---
title: "Analysis of covered topics"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Analysis of covered topics}
  %\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.

For development purposes, we only fetch records if they are not already
stored as csv files:
```{r}
res <- read_from_csv_or_fetch('../inst/')
```

## Analysis

Now we can start analysing our parsed dataset:

### Counting the occurences of a given word:

```{r, fig.width=7, fig.height=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,
                       rotatelab = T)
```

### When are which topics discussed the most?

First we define some search patterns, according to some common political topics.
```{r}
pandemic_pattern <- "(?i)virus|corona|covid|lockdown"
climate_pattern <- "(?i)klimawandel|erderwärmung|co2|treibhaus|methan|kyoto-protokoll|klimaabkommen"
pension_pattern <- "(?i)rente|pension|altersarmut"
```
Then we use the analysis helper `word_usage_by_date` to generate a tibble counting the
occurences of our search patterns per date. We can then plot the results:
```{r, fig.width=7, fig.height=6}
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()
```