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hateimparlament/vignettes/generalquestions.Rmd
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---
title: "General questions"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{General questions}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
library(hateimparlament)
library(dplyr)
library(ggplot2)
library(stringr)
library(tidyr)
```
## Preparation of data
First, you need to download all records of the current legislative period.
```r
fetch_all("../inst/records/") # path to directory where records should be stored
```
Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
```r
read_all("../inst/records/") %>% repair() -> res
```
We also used `repair` to fix a bunch of formatting issues in the records.
For development purposes, we load the tables from csv files.
```{r}
res <- read_from_csv('../inst/csv/')
```
## Analysis
Now we can start analysing our parsed dataset:
### Which party gives the most talks?
```{r, fig.width=7}
join_speaker(res$speeches, res) %>%
group_by(fraction) %>%
summarize(n = n()) %>%
arrange(n) %>%
bar_plot_fractions(title="Number of speeches given by fraction",
ylab="Number of speeches")
```
Note that `NA` signifies speeches given by speakers who are not members of parliament.
### 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?
Calculate the average character length of talks given by speakers:
```{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)
```