CUNY Academic Commons

Hi DVGer,

We just held our first meet today and I will make the first post and share the resources and content we spread in the working group meet today. In this post, I am trying to understand data visualization from both a scientist’s and artist’s point of view. I also want to thank Dr. Lev Manovich for providing some of the resources and Connor French for revising the script.

1. Data Journalism (https://datajournalism.com/read/handbook/one/introduction/what-is-data-journalism）
This E-book is called Data Journalism, which is discussing how to get, understand, and deliver data from a data journalist’s point of view. Moreover, it introduces many classic cases showing how the new technology of data visualization has changed the way for journalists to tell story. For example, the following two cases showed different tools and methods widely used:
1. Data visualization DIY Our Top Tools (https://datajournalism.com/read/handbook/one/delivering-data/data-visualization-diy-our-top-tools);
2. Using Data Visualization to Find Insights in Data (https://datajournalism.com/read/handbook/one/understanding-data/using-data-visualization-to-find-insights-in-data).

2. TED talk Videos
Here are some great and inspiring TED talk videos about how different data artists explored simple and elegant ways to see information.
a. Visualizing ourselves with crowd-sourced data: https://www.ted.com/talks/aaron_koblin_visualizing_ourselves_with_crowd_sourced_data#t-1073789
b. Make data more human:

c. The beatify of data visualization:

d. A visual history of human knowledge

3. Websites about data visualization
a. DataKind: put data in service of the public good: http://www.datakind.org/
b. School of Data: learn data techniques for free: https://schoolofdata.org/
c. Top CS conference for visualization: http://ieeevis.org/year/2020/welcome
d. NYC: https://civichall.org/
e. Columbia Town Center for Digital Journalism: https://towcenter.columbia.edu/
f. Data Society: https://datasociety.net

4. Learn data visualization methods
There are many ways of visualizing data, including indicator, line chart, bar chart, pie chart, area chart, pivot table, scatter plot, scatter map/area map, etc.

5. Learn data visualization tools
There are many data visualization tools, including Google Charts, Data Studio (Google), Tableau, Grafana, Chartist.js, FusionCharts, Datawrapper, Infogram, ChartBlocks, and D3.js. While most software is proprietary and the license is not cheap, we prefer to use open-source tool/software/programming language.

Readings:

Which languages you should learn: https://www.freecodecamp.org/news/which-languages-should-you-learn-for-data-science-e806ba55a81f/

Trends in Analytics and Visualization (2017): https://www.kdnuggets.com/2017/05/poll-analytics-data-science-machine-learning-software-leaders.html

TIOBE Index for Oct 2020 (popularity rank of programming language): https://www.tiobe.com/tiobe-index/

Here are some options:

a) Python: an interpreted, high-level, and general-purpose programming language. Check here for installation of Python. Check out PUG from GCDI (PUG uses Learn Python 3 the Hard Way as the textbook).

b) R: a widely used programming language for data science and statistics. Check here for installation of R and RStudio (Another tutorial for installation). Check out RUG (working group) from GCDI and the recommended free Ebook: R Cookbook 2nd.

Tidyverse and ggplot2 are the primary packages used in R for data visualization. Here are two free Ebook specifically for learning Data Analysis and Data Science in R:
Exploratory Data Analysis with R – You check out chapters on visualization (chapters 5, 6) and overall is very good.

R for Data Science – this textbook is co-authored by Hadley Wickham, the developer of ggplot2 and tidyverse packages. This is particularly useful if you know R but have not yet used these packages.

If you are in humanities, check out Taylor Arnold and Lauren. Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text. Springer, 2015.

c) Datawrapper: Easy to use online software to create visualizations from prepared data. Used by many news sites and online publications. Free version is available.

d) Google Spreadsheet Charts: Google spreadsheet online tool. No coding is required. Free.

e) Microsoft Excel: Microsoft spreadsheet software. No coding is required. CUNY offers free student subscription in Microsoft Office 365.

f) Tableau: Current standard for professional data visualization and useful for data pre-exploration. You can register on their site with your student info, and they will give you Tableau Desktop for free – it’s better than Tableau Public. No coding is required for the tool and you can do anything within a nice GUI. See for example: https://public.tableau.com/en-us/s/covid-19-viz-gallery.

g) d3: Best software for best looking online interactive visualizations; requiring writing code in Javascript. It allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. For example, you can use D3 to generate an HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.

h) Observablehq: Working with and visualizing data using d3 inside an interactive notebook (developed by the same person who made d3).
Since you have so many tools, sometimes it might be confusing which tool to choose. Here are some articles to mitigate your concern:
Exploratory analysis – when to choose r, python, tableau or a combination
https://www.stoltzmaniac.com/tool-selection-python-tableau-r/

What I Learned Recreating One Chart Using 24 Tools
https://source.opennews.org/articles/what-i-learned-recreating-one-chart-using-24-tools/

Why use R when you have Tableau?

Why use R when you have Tableau? Tableau vs. R?

Excel vs Google Sheets
https://business.tutsplus.com/tutorials/google-sheets-vs-ms-excel–cms-30971

What would a professional recommend?
“What do data designers and data scientists actually use? We use a combination of tools. You need to know Excel because so much data is available in spreadsheet form, and this is the most common format for sharing data. I also recommend Tableau (at least basic knowledge because it is very good for quick data exploration. And then you need to know at least one more sophisticated visualization software to make more complex graphs. d3, ggplot2 with R or some visualization library for Python are good choices, but there are others. And finally, you need to have good knowledge of some software for manipulating data and preparing it for visualization. Excel, Google Sheets or Tableau can do some of it, but R or Python are more powerful.”

6. GCDI Resources for data visualization

Working groups:
GCDI (GC Digital Initiatives) offers different relevant working groups available for meeting every other week during the semester. These working groups will work on data visualization topics with programming languages. You can join and check their schedules.
a) R user group (RUG): http://cuny.is/rug
b) GIS Working Group: http://cuny.is/group-gis-working-group
c) Python Users Group (PUG): http://cuny.is/pug

Workshops:
GCDI offers different workshops every year and many of them are tightly related to data visualization. Check the event calendar for upcoming events and workshops:
https://gcdi.commons.gc.cuny.edu/events.

Google Doc for this article (more links in the original version): https://docs.google.com/document/d/1o1QRLs5a1IR3mxp-vEWoOQR_0txSt9ps7ejPZG-HeVw/edit?usp=sharing

Cheers,
Yuxiao Luo