Page Comparison

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• Chroma.js– Javascript library for dealing with colors
• D3.js– Javascript library with modules for dealing with colors
• HCL Wizard– Tool for viewing, manipulating, and choosing HCL color palettes
• I Want Hue– Tool for generating and refining palettes of optimally distinct colors
• Colorbrewer– Tool for finding sequential, diverging, and qualitative color palettes
• Color Picker for Data– Tool for picking color palettes
• Accessible Color Matrix– Tool for building accessible color palettes
• Contrast Finder– Tool for finding good contrasts between two colors
• Chromaticity– Guidance for accessible visualization color design
• Color Oracle– Free color blindness simulator for Window, Mac and Linux

Data Literacy Resources (Fall semester 2019)

Slides

Day 1: https://docs.google.com/presentation/d/e/2PACX-1vSq7ytkZLPS-Nbs5JYbeKG8EuYotCxRgUewmbgWWWCqGIV-KUX4AXCa-_5bbdYNilRd46n6p3F9IbmT/pub?start=false&loop=false&delayms=3000

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• Theory Part I

General / good reads

• «The world’s most valuable resource is no longer oil, but data» vs. «Data is not the new oil»

• Data Scientist: The Sexiest Job of the 21st Century

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• Data Analysis with R: Recent and good introduction, no prior knowledge needed
• rddj.info: Resources for doing data journalism with R
• Datacamp.com: Good, beginner-friendly and interactive courses on R (and Python). Very recommended - also check out my own course :-)

Exercises

KEV

Kontext:

https://www.srf.ch/news/schweiz/abstimmungen/abstimmungen/energiestrategie/die-profiteure-der-energiewende

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Probiert folgende Auswertungen mit den Daten anzustellen?

Welche Schritte braucht es dafür?

Welche Abstriche muss man bei der Auswertung machen?

1. Durchschnittliche Vertragslänge (von Inbetriebnahme bis Vertragsende)
2. Gesamtstromproduktion nach Kanton
3. Gesamtsumme der Geldbezüge für einerseits Herren, andererseits Frauen
4. Gesamtsumme der jeweils ersten Zahl in der Strassennummer, nach Kanton
5. Karte mit Standorten der KEV-Bezüger (= ein Punkt-Symbol für jeden genauen Standort, ähnlich wie in diesem Artikel)

Exercise solutions day 1

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1. Sometimes, data is accessible via an API
2. The preferred data format of APIs is JSON
3. JSON can be converted into CSV
4. The preferred way of talking to an API is with code

1. Browse https://developer.spotify.com/console/get-search-item/ to get his ID: 7uxtLjuqkJ3cnjQQuW6Cul
2. Browse https://developer.spotify.com/console/get-artist-top-tracks/, fill in values and get JSON data
3. Copy and paste into https://json-csv.com/
4. Download as Excel - do the math (36.60705 min)
5. Take-Aways:

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1. Browse https://www.tecson-data.ch/zurich/tiefenbrunnen/index.php (as probably shown on Google)
2. Select “windchill”, 2.11.2018/7.11.2018 and “all values” at the very bottom
3. Copy stuff into Excel by hand and calculate median

1. Browse https://tecdottir.herokuapp.com/docs/#/measurements
2. Enter parameters
3. Copy curl string and pipe into a file
4. Upload JSON and paste into  https://json-csv.com/ (bonus: use matrix style)
5. Download CSV, open in Excel and calculate median (don’t forget to filter unneeded dates)

1. Copying and pasting stuff from HTML tables should be avoided
2. Always look out for an API
3. Try out different settings of your tools - they might bring you better results (“matrix style”)
4. Get to know the terminal
5. Excel / LibreOffice / OpenOffice have some good filters: get to know how to use them
6. If you run out of queries, delete cookies

1. First approach: Scraping data
2. Second approach: Open Data Zürich / API:
3. Take-Aways:

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1. Many interesting data are buried in PDFs
2. Use proprietary software or Tabula to extract the data

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