hateimparlament/vignettes/generalquestions.Rmd

---
title: "generalquestions"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{generalquestions}
  %\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:

### Which partie gives the most talkes?

```{r, fig.width=7}
join_speaker(res$speeches, res) %>%
    group_by(fraction) %>%
    summarize(n = n()) %>%
    arrange(n) %>%
    bar_plot_fractions(title="Number of speeches given by fraction",
                        ylab="Number of speeches")
```

### Who gives the most speeches?

```{r}
res$speeches %>%
    group_by(speaker) %>%
    summarize(n = n()) %>%
    arrange(-n) %>%
    left_join(res$speaker, by=c("speaker" = "id")) %>%
    head(10)
```

### Who talks the longest?

```{r}
res$talks %>%
    mutate(content_len = str_length(content)) %>%
    group_by(speaker) %>%
    summarize(avg_content_len = mean(content_len)) %>%
    arrange(-avg_content_len) %>%
    left_join(res$speaker, by=c("speaker" = "id")) %>%
    head(10)
```