83 lines
1.9 KiB
Plaintext
83 lines
1.9 KiB
Plaintext
---
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title: "General questions"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{General questions}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>"
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)
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```
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```{r setup}
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library(hateimparlament)
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library(dplyr)
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library(ggplot2)
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library(stringr)
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library(tidyr)
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```
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## Preparation of data
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First, you need to download all records of the current legislative period.
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```r
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fetch_all("../inst/records/") # path to directory where records should be stored
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```
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Second, those `.xml` files, need to be parsed into `R` `tibbles`. This is accomplished by:
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```r
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read_all("../inst/records/") %>% repair() -> res
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```
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We also used `repair` to fix a bunch of formatting issues in the records.
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For development purposes, we load the tables from csv files.
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```{r}
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res <- read_from_csv('../inst/csv/')
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```
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## Analysis
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Now we can start analysing our parsed dataset:
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### Which party gives the most talks?
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```{r, fig.width=7}
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join_speaker(res$speeches, res) %>%
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group_by(fraction) %>%
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summarize(n = n()) %>%
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arrange(n) %>%
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bar_plot_fractions(title="Number of speeches given by fraction",
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ylab="Number of speeches")
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```
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Note that `NA` signifies speeches given by speakers who are not members of parliament.
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### Who gives the most speeches?
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```{r}
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res$speeches %>%
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group_by(speaker) %>%
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summarize(n = n()) %>%
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arrange(-n) %>%
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left_join(res$speaker, by=c("speaker" = "id")) %>%
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head(10)
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```
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### Who talks the longest?
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Calculate the average character length of talks given by speakers:
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```{r}
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res$talks %>%
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mutate(content_len = str_length(content)) %>%
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group_by(speaker) %>%
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summarize(avg_content_len = mean(content_len)) %>%
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arrange(-avg_content_len) %>%
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left_join(res$speaker, by=c("speaker" = "id")) %>%
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head(10)
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```
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