# Data Dojo Würzburg 10

## March 2022
- **When:** Thursday, March 10^th, 2022 **at 18:00pm**
- **Where:** Zoom
- **Zoom:**
- [Zoom Link](https://uni-wuerzburg.zoom.us/j/98930032711?pwd=bW9Gdmo3NzlRTHJ1TW5YQ3ZTM050QT09)
- Meeting ID: 989 3003 2711
- Password: 299718
- **Info:** [DataDojo Website](https://ddojo.github.io/), [Repo](https://github.com/ddojo/ddojo.github.io)

## Participants
> Please add your name to the list (click the pen icon at the top left to edit) if you plan to come. And please remove it if you can not make it. Feel free to add your preferred tool or programming language.
- Markus (R or julia)
- *»add your name here«*

## Dataset

Local results of the German Federal Election from Würzburg ([Stadt](https://www.wuerzburg.de/wahlen/Wahl-2021-09-26/09663000/praesentation/opendata.html) and [Landkreis](https://okvote.osrz-akdb.de/OK.VOTE_UF/BTW21/09679000/praesentation/index.html)) together with demographic information (e.g. age structure): [Stadt](https://opendata.wuerzburg.de/explore/dataset/stadtbezirke_hauptwohnsitz_altersgruppen_2019/export/), [Landkreis](https://www.statistik.bayern.de/statistik/gebiet_bevoelkerung/demographischer_wandel/gemeinden/index.html)

This time the data will be provided as a pre-processed single tidy table.

### Specific task for today

1. Plot the distribution of mean age across Locations
2. Is there a correlation between mean age and votes for any party?
3. Independent of point 2 😜 if you weigh each vote by age, which party has the lowest/highest mean age?
4. *»Add additional ideas here«*

Question Pool:
- Generic
- What kind of information is stored in the table(s)?
- How much data is missing?
- Is the dataset clean or are there any clear outliers?
- How can the different datasets be combined?
- How to visualize the results in a suitable way?
- Specific
- Overview of voting behavior: how does voting behavior vary by location? (General trends, total variability, ...)
- Overview of demographic info: how does age/gender distribution vary by location? (General trends, total variability, ...)
- Which party has the strongest (positive/negative) correlation with age?
- Which party has the strongest (positive/negative) correlation with gender?
- Can we predict voting behavior from age/gender distribution? (or vice-versa)
- **Add your own questions**
- Further Ideas
- Show results with district resolution on an interactive map (e.g using [these shapes](https://gadm.org/index.html))
- **Add your own ideas**

## Collaborative Tools and Workflow

For Notebooks (R, python, julia, js, ...) with real time collaboration [CoCalc](https://cocalc.com) seems to be the best option right now. It worked great the last couple of times so we'll stick to it for now. You need to register an account there (it is free).

## Future Suggestions
> Add your suggestions to the list and :+1: to the end of a line you are interested in

### Data Sets
- Results of the [Bundestagswahl 2021](https://www.bundeswahlleiter.de/bundestagswahlen/2021/ergebnisse/opendata.html)
- Weather data throughout Germany over time (incl. temperature, precipitation, ...): https://www.dwd.de/DE/leistungen/cdc_portal/cdc_portal.html
- German [Mikrozensus](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Haushalte-Familien/Methoden/mikrozensus.html)
- Kaggle [Titanic](https://www.kaggle.com/c/titanic) or [Tabular Playground](https://www.kaggle.com/competitions?hostSegmentIdFilter=8) or [Meta Kaggle](https://www.kaggle.com/kaggle/meta-kaggle)
- World Trade Data ([Open Trade Statistics](https://tradestatistics.io))
- [Open Citation Data](http://opencitations.net/download#coci)
- [Top 100 charts + Audio Features](https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-09-14/readme.md)
- [Emoji Usage :hugging_face::heart::laughing:](https://observablehq.com/@jenniferdaniel/unicode-emoji-mirror)

### Tools/Languages
- R/tidyverse
- python
- [Power BI](https://www.microsoft.com/en-US/download/details.aspx?id=58494)
- [Tableau](https://www.tableau.com)
- [KNIME](https://www.knime.com/)
- javascript
- julia

### Skills
- interactive maps
- dashboards
- animations

### Data Sources
> all data types are welcome, including tables, images, videos, sounds, DNA, ...

- [TidyTuesday](https://github.com/rfordatascience/tidytuesday)
- [Our World in Data](https://ourworldindata.org/) (R package: [owidR](https://github.com/piersyork/owidR)), [Sustainable Development Goals](https://sdg-tracker.org/)
- Open Data Initiatives ([Würzburg](https://opendata.wuerzburg.de/), [Germany](https://www.govdata.de/), [Statistisches Bundesamt](https://www.destatis.de/), [Europe](https://data.europa.eu/en), [APIs](https://bund.dev/))
- [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets)
- [Kaggle Datasets](https://www.kaggle.com/datasets) or [Competitions](https://kaggle.com/competitions), e.g. [SLICED](https://www.kaggle.com/search?q=Sliced+in%3Acompetitions)
- [tsibbledata](https://tsibbledata.tidyverts.org/reference/index.html): Time Series Datasets
- [R-text-data](https://github.com/EmilHvitfeldt/R-text-data): Text Datasets, ready to use in R
- [data.world](https://data.world/)
- [Statista](https://de.statista.com/) - the University of Würzburg has a campus license
- [Open Legal Data](https://de.openlegaldata.io/)
- [Bundestag Data](https://github.com/bundestag) (e.g. poll results, deputies, wahl-o-mat, [inspirational blog post](https://jollydata.blog/posts/2021-03-14-bundestag-part-iii/))
- [Deutsche Digitale Bibliothek](https://www.deutsche-digitale-bibliothek.de/newspaper) ([API](https://labs.deutsche-digitale-bibliothek.de/app/ddbapi/), old newspapers from Germany)
- [Earth Observation: Satellite Image Time Series](https://e-sensing.github.io/sitsbook)
- [Machine Learning Datasets](https://paperswithcode.com/datasets)
- Internation (Student) Assessment Data ([TIMSS, PIRLS, PISA, ...](https://pirls.bc.edu/databases-landing.html))
- [(Medical) Imaging Datasets](https://radiopaedia.org/articles/imaging-data-sets-artificial-intelligence), [MedMNIST](https://medmnist.com/)
- [Inspirational Notebooks on Observable](https://observablehq.com/@tomlarkworthy/notebooks2021)

## Cross Links
- [previous pad](https://hackmd.io/RzaiSq4QTHWTTSQSSVrmyg)
- [next pad](https://hackmd.io/okjtsFj2RDSLZ3ylW_C72w)