---
title: "funwithdata"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{funwithdata}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(hateimparlament)
library(dplyr)
library(ggplot2)
```

## 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

reden <- res$reden
redner <- res$redner
talks <- res$talks
```
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}
tables <- read_from_csv('../csv/')

comments <- tables$comments
reden <- tables$reden
redner <- tables$redner
talks <- tables$talks
```

## Analysis

Now we can start analysing our parsed dataset, e.g. find out which party gives the most talks:
```{r}
left_join(reden, redner, by=c("redner" = "id")) %>%
    group_by(fraktion) %>%
    summarize(n = n()) %>%
    ggplot(aes(x = fraktion, y = n)) +
    geom_bar(stat = "identity")
```

### Count a word occurence

```{r}
find_word(res, "hitler") %>%
    filter(occurences > 0) %>%
    join_redner(res) %>%
    select(content, fraktion) %>%
    group_by(fraktion) %>%
    summarize(n = n()) %>%
    arrange(desc(n))
```