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Mini-Hackathon4.Rmd
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Mini-Hackathon4.Rmd
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---
layout: tutorial
title: 'Mini-Hackathon 4: Times-Series Part I'
author: "Mariken van der Velden & Kasper Welbers"
output:
github_document:
toc: yes
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
## include this at top of your RMarkdown file for pretty output
## make sure to have the printr package installed: install.packages('printr')
knitr::opts_chunk$set(echo = TRUE)
library(printr)
library(rmarkdown)
```
Mini-Hackathons are performed and submitted in pairs of two.
You must hand in your assignment on Canvas the next week before **Tuesday Midnight**.
Use this RMarkdown template on the canvas page for this mini-hackathon to complete your hackathon.
When you are finished, knit the file into a pdf with the knit button in the toolbar (or using Ctrl+Shift+K).
For this you need to have the `knitr` and `printr` packages installed, and all your code needs to work (see the R course companion for more instructions).
If you cannot knit the `.Rmd` file, there is probably an error in your R code, therefore add `eval=FALSE` to the code chunk: `{r, eval = FALSE}`, so you are still able to knit and upload the file.
# This Mini-Hackathon
This mini-hackathon builds upon the tutorial on [times-series analysis](https://github.com/MarikenvdVelden/Replication-Hackathons/blob/main/tutorial_times_series_part1.md).
For this hackathon, you can (and are strongly recommended to) use code from this week's tutorial as well as provided here.
If you aim to conduct additional analyses in R, we of course encourage this.
Nevertheless, it is important to not do additional analysis just for the sake of running more code chunks.
For that reason, please provide a justification for these additional analyses.
Also, we recommend to use parameters for RMarkdown codeblocks, in particular the `cache = TRUE` parameter for codeblocks that take long to compute (e.g., downloading data from AmCAT).
A brief explanation of some usefull parameters to make a `.Rmd` file pretty is given in the [first tutorial](https://github.com/MarikenvdVelden/Replication-Hackathons/blob/main/Intro-to-rmd-and-data-retrieval.md).
Additionally, you can use [this cheat sheet](https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf).
Important to take into account is that this week we will build upon codes of last weeks.
### Hackathon Challenges
#### Challenge 1
Choose a data set to measure public opinion on a political topic that could have driven the popularity of the US presidential candidates.
You can either work with the:
- AmCAT data set with newspaper articles on the US presidential candidates in 2020;
- Google trends data
#### Challenge 2
Choose an objective indicator for the economy, as described in [the tutorial](https://github.com/MarikenvdVelden/Replication-Hackathons/blob/main/tutorial_times_series_part1.md).
Merge this data with the public opinion data.
#### Challenge 3
Combine the data created in Challenge 1 and 2 with polling data, as described in [the tutorial](https://github.com/MarikenvdVelden/Replication-Hackathons/blob/main/tutorial_times_series_part1.md).
#### Challenge 4
Visualize and descripe the trends of public opinion, the objective state of the economy, and support for the presidential candidates over time.
#### Challenge 5
Does public opinion or the subjective state of the economy forecast a change in polling for Trump or Biden?
To answer the question, run a correlation and interpret the value.
What does this mean?