I signed up for a poetry class during summer break while an undergrad at The University of the Arts (UARTs) pursuing my BFA in Photography. I had no idea what I was getting into but I went with it. The experience turned out to be one of the unexpected “bests” of my life. That summer was also the year I learned, in great detail what happened to my grandfather , a man I never knew because someone decided to take his life long before I was ever imagined.
Writing poems gave my anger and loss (can you mourn someone you never knew?) a place to rest. It was also the first time I recited a poem in public. Granted, it was a room of my classmates but for someone who honestly prefers to sit in the audience, this was big. Huge.
It’s been awhile since I’ve read her poems and this idea of a project has been a wonderful way to reacquaint myself with her and her work. It’s been, well, decades. Does her work resonate with me now as it did then? I believe this project will be an emotional journey as much as it is a learning experience. What will I discover?
I’m proposing a visualization that slightly petrifies me.
Self-learning: Python, D3, scraping data, build a corpus… oh my.
Blogs, tweets, StackOverflow, GitHub, Codecademy, Lynda … you name it, I’m searching for “How to …” often.
Learning python: New jargon. Decisions about IDEs. Pandas… they aren’t cute black and white furry animals?
Learning how to properly analyze and clean text data: Pre-processing? Does my data need to be organized in a csv file or like a massive dump in a txt file? Looking for tutorials on how to prep text with Jupyter notebook) Soooo many questions.
Learning D3 is not going to be easy though I am hopeful Amelia Wattenberger’s book, Full Stack D3 and Data Visualization will be a huge help. (She is based in Rochester, NY – cool.)
Some classmates have suggested that I use R instead of Python. I’ve gone back and forth on this. Maybe I’m crazy but I prefer to learn Python because it seems a language that crosses many disciplines for many applications. Then, there’s the fact that python was named in honor of Monty Python’s Flying Circus!
Part of this is also recognizing that I know what is good but my abilities to execute are far below what it probably takes to meet my own expectations. Ira Glass has something to say about this and I’m trying to find some comfort. One would think familiarity of this mind space would make it easier each time. But no…
Still, I have some bright sides.
Mindy McAdams, a data journalism professor at the University of Florida was helpful in getting me setup with Python. I didn’t know it at the time but miniconda was the right way to go and given the number of IDE options, I’m so glad I went with Jupyter Notebook. (Seriously Mindy I cannot thank you enough.)
The bright side wouldn’t be complete without mad props to Lenny Martinez, one of UM Interactive Media’s data journalism professors. His positivity is all goodness when I feel like I’m drowning.
And, to end on a positive note, I do know how to install packages and I know kernals and cells, virtual environments and I’m not afraid of Terminal so much anymore. Not bad. Perhaps I need to just get over the fear that I’ll break something if I type incorrect syntax and also not worry so much about creating a properly formatted text file.
It’s always inspiring to hear from people who love what they do. Our Data Visualization Studio class was fortunate to welcome three (remote) guest speakers: Luís Melgar, Julia Wolfe and Maarten Lambrechts. I want to share my notes and takeaways both as a reminder for myself and to share with anyone learning more about data visualization like me. Warning: This is a long post.
Luís Melgar: learn something new & a great way to Quiet Imposter Syndrome
What I particularly loved about Luís’s talk was the mix of professional and personal advice. It was positive and endearing, a talk that makes you feel good and ok exactly where you are.
Keep a data journal
Log your data, track your sources to maintain sanity. Truth, this. If you’ve ever felt like you were drowning in data or hunting for sources in the middle or near the end of a project, this is your life jacket.
Write down what you did with the data, how you came up with your conclusions. This is accountability.
Track your path to the very end.
Data is imperfect
I love how Luís phrased this tip: interview your data.
Questions to ask:
Where is it coming from?
How it is defined?
What questions does it bring to mind?
How was the data collected?
Who collected the data?
Get clarity. What can you really see from the data? Dig into it.
Advice about coding
Is your code reproducible?
Make it easy to track mistakes
Comment your code
Use another language to double-check your calculations
Test, test, test
Review, review, review
Check, check, check.
Rinse and repeat.
Learn something new
Pick a project, set a goal. Do it.
Expect the unexpected
Some people may give you data in a PNG. No, really.
Coding is hard. So, for those who aren’t natural coders, it’s OK if you can’t execute what you have in your mind. Luís shared his experience working on his Master’s capstone and some of the hurdles he came across and how he learned to adapt and work around his own limitations.
Know your technical limitations
Different companies and organizations use a wide range of publishing tools. Understand what is possible within those constraints.
Always keep your audience in mind
Make your visualizations responsive and usable on mobile. This is a must.
Don’t ask for too much advice.
This tip made me think of some photographers I know who seem to ask too many people to give them feedback about their work. Let me clarify: I think it is OK to ask as many people as you want but take advice from just a few— the people you trust most. It was nice to hear Luis same the same. Basically everyone has an opinion but whom do you trust?
Love your Classmates
Yes indeed. Luís shared a funny photo of some of his classmates (with some random dude cute out – lol) and how he continues to stay in touch. He shared a couple of memories about music and food which was sweet and funny.
Life is about relationships. The relationships you build or burn in graduate school will have a big impact on your life moving forward. Take the time to learn something new about the people you see every day. They will enrich your life in so many ways.
Keep a notebook of success stories
This. I was surprised to hear him talk about imposter syndrome and it was refreshing to hear someone with so much more experience share that even he suffers from it from time-to-time. He shared how women and minorities tend to experience it more. Interesting.
His solution? Keep a notebook close by where you write down your success stories —what you have learned or accomplished. Big or small. When you are feeling like an imposter, or perhaps just feeling a bit low or unsure, refer to your notebook. It’ll make you feel better; that you are making progress; that you are learning; that you are exactly where you need to be and where you are supposed to be.
Thank you Luís. It was wonderful and inspiring to hear from a graduate of our program.
Her advice aligns with much of what Alberto has shared as well as what I’ve read in books and online. It helps to hear similar advice but in a different way.
Analyze data: Find the story. What are your questions?
Be skeptical of the data
Talk with a subject matter expert or know enough about the subject to either spot something surprising or identify a problem.
Understand basic design sensibilities: Color, hierarchy, white space, alignment, typography.
Edit and proof: Layer your workflow and establish a process for self-editing.
Comment your code so you can remember it later. Save every step.
Keep your data organized
Do rigorous spot checking
Make sure your source aligns with your visualizations
Check your outliers. Are they reflected in the source?
Provide documentation. As you work, make notes or comments about what you found and how. You may need it to back up your work, your conclusions.
Bring context to data and moments.
Learn how to get and scrape data. What kind of unique data can you get; something that no one else has?
Freedom of Information Act (FOIA). Luís Melgar mentioned this as well. Understand how to file a FOIA request. (See links below)
Excel. Ramp up your skills.
Reporting. Hit the streets if needed.
Interview. Yes, talk to people!
Understand state laws. What are you legally within right to request and what are institutions required to give you? I loved this tip. I learned state laws and policy is important secondary research for product design, too.
Understand different data formats: PDF? Excel? I took this to also mean, understand how to read them and how to extract data from PDFs (Tabula to the rescue …)
I’m glad he did because he made a story I would probably pass over into a story I wanted to read. Even more, I wished for a follow-up. What more could I learn? For example, when he shared a brief look at his sketches and a sentiment analysis he had done, I wanted to learn more. What patterns are developing in Europe?
Details from Why Budapest, Warsaw, and Lithuania Split Themselves into Two
Honestly, Maarten’s talk was a good inside look at what it takes to be a data journalist/ data visualization designer. It was a bit of a wake up call, too. It takes a certain level of tenacity, a doggedness, if you will. Perhaps this is why I latched on to a quote he made during an interview with Open Belgium 2016:
If you want to become a data journalist, you should start thinking like a programmer.
There’s just something about programming that is empowering. Perhaps because it forces you to look at a problem in many different ways. It changes how you think about things, look and interact with the world. Hmm…
Like many other professions, what most people see is the finished, polished work. Viewers/readers don’t see what goes on behind the curtain. That’s what I enjoyed most about Maarten’s talk. He showed us his sketches, his thinking; what worked, what didn’t. It was a comfort to see the mess.
Thank you Maarten for the incredible resources and sharing your story with us. Consider me a new fan of your blog.
Wow, you made it to the bottom. Nice.
Thank you data visualization community for your generosity. Ya’ll are icing on the cupcake.
Missing magazines show up—yay!—but left unopened for weeks.
Finally open the envelope, glance at the covers and they sit for another week. I was beginning to feel that read-your-New-Yorker-magazine-pressure every time I passed them.
But just yesterday when I sat down to write a blog post about a yield curve I saw (saving that for later), I noticed a small cover line on the August 2019 issue of Net Magazine.
Learn from a Data-Viz Whizz
Who is the Data-Viz Whizz?
Flip, flip, flip … Shirley Wu.
Shirley Wu, one half of the popular Data Sketches project, creates highly interactive, beautiful data visualizations. Here, she gives us a look behind the scenes and shares the lessons she’s learned.
Intrigued, I immediately sat down to read about Shirley Wu.
It was an inspiring read. I love stories about people who make changes; that she switched from being a front-end software engineer to become a data visualization designer is cupcake.
So, do you recall periods in your life when you are planning your next step or struggling or hoping for something but not sure what and the universe sends you a person or a moment or a sign to help you take that one small step forward?
This interview with Shirley is one of those moments. Noticing that small cover line near the UPC symbol is nearly impossible but I did and that led me to discovering Beautiful for the first time. Why is this important? Because I’m planning to tackle my first interactive data visualization of poems (Pablo Neruda? Anne Sexton? Maya Angelou? — I need to decide) and it helps to see what other data viz designers have done using text. Part of learning is seeing and understanding what is possible. It may take me some time to reach Shirley’s level of talent but her work and her words were a spark.
Correction: In my excitement, I had flipped Shirley Wu and Nadieh Bremmer’s work in my mind. My sincere apologies to both women. Nadieh is the designer behind Beautiful and Shirley is the designer of Explore Adventure (below), an equally beautiful (see what I did there?) and fun visualization about the travel search connections between countries, seasons, attractions, and more.
Lesson learned? Give yourself enough time to triple check your work, what you read, and own up to your mistakes.
What I love about Beautiful is the overall simplicity, the subtle animations and the surprising level of detail and information which isn’t obvious at first. It’s fun and interesting. My only wish: a little more feedback during my interaction with the top 10 words per language.
Legends is just stunning and ok, I admit, I have a thing for that color palette. The cool factor is huge. Immediately I wished for Legends to be realized into a physical space that I could walk in and around (VR anyone?) with additional layers of information as I interact with each crystal.
I also admire the fact that she shares her knowledge about D3.js with others through workshops (with live coding!), user groups and online courses. She mentions her process and a lot of the tools she uses to clean, understand, explore, prototype, and design. It is a list I’m definitely planning to check out as I begin my journey learning D3.js through Coursera and making my first interactive visualizations.
So, thank you, Shirley Wu for making feel even more excited about data visualization, sharing lessons you’ve learned, your take on tools, and showing me what is possible with D3.js.
I cannot wait to see more of what you create.
PS: It’s always nice to read about people you know, especially when you are also learning from the same. She mentions Alberto twice. The first when he invited her to dive into data that would result in Beautiful and the second when she mentions how teaching forced her to learn so she read a few books. The Functional Art, she says, is “one of my favourites”. Cool.
Professor Cairo has a voracious appetite for reading and he thankfully likes to share books and articles. One article he included, Finding the Best Free Fonts for Numbers was an interesting read as I have a thing for type and fonts. I get picky and can spend probably too much time selecting a font that I feel works well. I’ve also taught typography classes so while I am not a type expert, I am knowledgeable about typography.
In general, I agree with the list Samantha recommends. Not everyone can afford some of the best designed super families out in the wild. I also agree that free fonts aren’t always the best choice. Most are poorly designed and more importantly were probably created for the most generic of applications. So, again, she has compiled a thoughtful list.
I do not think it would function well for data visualizations where type sizes are below possibly 14 points and that might be generous. Why? Old Standard TT can be quite interesting at large sizes; however, at smaller sizes, it starts to fall apart.
I need reading glasses to be able to read Old Standard TT at 14 pts. Even with it set in black on a white background (great contrast between figure and ground), it is quite challenging to read. Imagine if it is set in a color also on a colored background. Personally, if it is hard to read, I won’t. In my mind, that is the worst possible user experience.
My recommendation: If you want to use Old Standard TT, use it for display copy—headlines, subheads, or instances where you want to set a numeral in a particularly large size.
Oldstyle or Lining
Samantha’s recommendation for lining and tabular is a good base; however, this should not be a hard and fast rule. Why? Because there is a purpose for Oldstyle figures. Oldstyle figures work well when used with running text. They don’t interrupt the flow of reading because they share the same x-height as their lowercase character companions. Lining numbers in contrast stand out when sharing the same baseline as lowercase characters in running text.
Oldstyle figures can be used for data visualizations especially in places where numbers share the same baseline as text. For example, annotations. They are also readable in tables and other data visualizations purposes. Oldstyle figures can also be tabular so please, don’t rule out a typeface because they have oldstyle figures.
OpenType and Investing in Typefaces
With OpenType fonts, you get the best of all worlds, usually. For figures, OpenType give you the flexibility of setting figures in tabular and lining and tabular and oldstyle. Usually a designer can also set type as proportional as well. This is one of the perks of OpenType fonts and investing in building a library of high-quality typefaces. (Use a font manager such as Extensis’s Suitcase Fusion). Many free fonts are not OpenType.
My Favorite Fonts for Data Visualizations So Far…
Below is a short list of sans serif typefaces I use over and over again. Many are large families so you also have a choice of many styles: thin, light, italic, regular, bold, etc.
Many of the fonts above are available through fonts.adobe.com (formerly TypeKit). It’s one of the perks of having an Adobe Creative Cloud subscription. If you are interested in others, try a search for sans serif with a large x-height. (A larger x-height usually means greater readability at smaller sizes.)
If you want to learn more about typography, I highly recommend Ellen Lupton’s website and book, Thinking with Type. I also have plenty of books which I’ll try to share soon. The great part about owning high-quality typefaces: you don’t need many. This is what makes OpenType super families so appealing.
Early in the semester, Professor Cairo introduced us to Data Illustrator, an open source tool that was designed to create data visualizations and infographics without programming.
My first graphic using Data Illustrator:
My second graphic using Data Illustrator:
What I Love and Hate
Love: The seeming flexibility.
Even though I don’t really feel anywhere near comfortable using DI, I can see from the examples that it is very flexible in terms of the type of visualizations that can be created. Plus, I was able to create the heat map above with Data Illustrator which I could not figure out how to do with any other tool in my student tool kit.
Hate: The hurdles of learning its flexibility
I am a beginner with visualization and Data Illustrator but as someone who is learning about user research and user experience, the UX could be greatly improved for novice users. I have no idea how experts in data viz feel about Data Illustrator but from a novice point-of-view the usability — efficiency, effectiveness, and satisfaction — is low.
Love: Downloading the files as SVG
The ability to download SVG files and modify them in Illustrator is very cool. The files are relatively clean (compared to Flourish) and works great.
Hate: Saving projects
Ok, it would be nice if I could save the project name within Data Illustrator rather than having to rename an Untitled DI file after I it downloads to my desktop. This is just so counter-intuitive. Still, at least when you re-open a saved DI file, the web-based tool actually recognizes it and it works so you can continue to modify as desired.
I wish and hope…
A usability test will be done if not already with Data Illustrator to improve it. It think it has a lot of promise but from a usability standpoint, it really needs refinement. Some user testing and UI improvements and improvements to the Help and Documentation especially for novice uses would help make Data Illustrator really shine.