Two Books That Kept Me Going When I Wasn’t Feeling 100% in Grad School

Sometimes, ok, most of the time, life as a grad student can be overwhelming. So, you need friends who insist you need to get some fresh air or stretch and you need books to remind you that pretty much everyone else is going through something similar and you are not alone. Two books filled that need and I found myself returning to them periodically throughout the semester.

Am I Overthinking This?

This book by Michelle Rial is one of my new all-time favorites. It makes me smile and oftentimes think, “So true” or “Yep, been there”. Her book helped provide some levity when I felt like I was in the middle of analysis paralysis.

Take this chart for example. I don’t drink soda but the number of coffee cups that I collected around me while jumping from one project to the next was downright hilarious. What I would add to this would be the number of snack wrappers. I would not have survived without KIND bars.

From, Am I Overthinking This? by Martha Rial.

In my case, it would be the stove. Even the hot stove indicator light didn’t help (don’t get me started on the design of stovetops) because the LED would be on to communicate, “Hey, this is still hot” (even when off) and “Hey, this is on”. So just by looking I couldn’t tell.

The number of times I glanced at the stove trying to figure out if it was on or off as I walked out the door with my bike is countless. Or, the number of times I would feel a slight panic thinking I left it on when I had already reached campus.

It gets worse when you are sleep deprived and your cognitive abilities start to seriously decline. I put eggs in the cupboard, my hot coffee in the fridge, my cell phone in the freezer and would flat-out forget what I was going to do next.

From, Am I Overthinking This? by Martha Rial.

This last one I’ll share is one I have near my computer. Now, I know this chart just from life experience but when you are learning new things every single day and completely out of your comfort zone every single day, it is easy to forget you’ve already failed numerous times and are still successful.

The academic measures (grades) somehow also manage to cloud the main reasons why you are in school or in my case, back to school. In my lucid, non-stressed state, I know I’m not back in school for grades. But when a professor tells you, “You know, your grade will be affected by this”, it’s hard not to care; to feel like you decreased your chances for success.

From, Am I Overthinking This? by Martha Rial.

Info We Trust

I didn’t read this book from the beginning to the end. What I love about this book is that I could skip around and still am. I don’t know why but I tend to open books from the back (no idea how I picked up this quirk). For Info We Trust, by RJ Andrews, I landed in Chapter 19, “Creative Routines”. How apropos!

Here are a few highlights:

Creatives have routines

Creatives, according to RJ, did not have a similar pattern of activity but what they shared was a routine. That surprised me because I tend to think of creatives as well…creative. In my mind that is a bit of chaos and work when the mood strikes. My thinking does not come from research but most likely TV or movies. I’m happy to learn I’ve been misinformed.

Magical aha! moments are lovely when they arrive. But real creative production is about steady discipline, not waiting around for inspiration. You must create the time and space for work to happen.

RJ Andrews, Info We Trust, p. 198

Two professors, Dr. Barbara Millet and Alberto Cairo would often share how important it is to establish a routine. It also wasn’t enough to establish one but to also focus on one thing for a specific timeframe. For example: If you are going to read journal articles, you might want to set aside Fridays to read and write in the mornings from 5 am to 7 am. Multitasking, after all, is horrible for your brain.

At first, I thought I couldn’t possibly change how I work. Also, within the first semester, I learned that everyone else’s schedule can put a wrench in your best plans for a routine. Teamwork and graduate studies don’t equal routines, especially when you are the sole morning person and the rest of your team prefers to work between 7 p.m. and whatever time it takes into the wee hours of the morning. Rough. So, focus on what you can control and when.

Experimentation, Learning, Exploration — Play, is Critical

Allow your curiosity to get the upper hand

RJ Andrews, Info We Trust, p. 198

This may sound crazy coming from me but coding has become a great place to play for me. A little music and something to learn, I find I can get in a zone where I’m willing to try as much as I need to figure out a bug. Granted, I’m not coding for the critical deployment of software. Released from that pressure, coding is becoming a great source of play. I’m learning and when something works, the emotion is off the charts. Look what I made!

Compare yourself—Try Not

One consequence of [learning from many different kinds of experts] is unfairly comparing yourself to specialists. That can lead to feeling like an imposter. Do not be too hard on yourself.

RJ Andrews, Info We Trust, p. 199

That last sentence… I struggle with that one—a lot. You too, right?

All I have to say about that right now is this: It’s a humbling experience going back to school full-time in your forties. I know a lot and all of a sudden I feel like I know nothing. Starting over is tough, rough and takes a lot of persistence. It requires remembering this is for the long game. As a former colleague reminded me, “It’s a marathon; not a sprint”.

Consume as many data stories as possible

In order to be a better data journalist or data visualization designer, look at and study more charts and data stories. Review what has been done in the past as it can influence what you do in the future.

Deconstruct past work to reveal your own unique blend of technical and temporal biases.

RJ Andrews, Info We Trust, p. 202

Alberto Cairo recommended this in his introductory class. He strongly recommended that we subscribe to The New York Times or any print edition of a major newspaper so that we can discover maps, charts, diagrams and data stories. The experience of print is different from browsing online.

I did just that and while there are some days where the paper piles up like when I was a subscriber to The New Yorker, I would spend a half-hour either when I got home from a full day of classes or in the mornings with my coffee flipping through the paper.

The pile of newspapers I’ve clipped and saved is embarrassing but I discovered a lot of stories that could become data-driven stories. I’m hoping to make them personal projects so I can keep practicing what I’ve learned.


I wrote briefly about networks before at the end of this post and RJ has to say this about connections:

Creativity is all about making new mappings between previously unconnected things.

RJ Andrews, Info We Trust, p. 200

This. This place of “mental fireworks” is what makes going back to school worth the sometimes unbearable feelings of frustration and insecurity. I felt these fireworks twice this semester and I cannot describe how magical it truly feels.

For me, the addition of an Artificial Intelligence class brought many concepts and ideas together from the current and previous semesters plus the research I had been reading and writing about as a GA in the UX Lab. My neurons were firing at a rapid place and the buzz was noted.

Be Active and Get Sleep

So this made me laugh out loud. Being active I could do because I rode my bike nearly every day to campus. The rides to and from campus reminded me of the beauty of mornings and a way to decompress after hours of classes and work. Sleep on the other hand…

Sleep is essential for health, but it also a productive creative tool. Taking a nap or sleeping on it overnight creates a natural space for the brain to ingest new information.

RJ Andrews, Info We Trust, p. 201

RJ has some great quotes in this section and I completely agree with this idea of “loading data overnight” but as a student sleep becomes a rare and cherished state.

Still, I did choose sleep a few times (even just 3-4 hours) over pushing through the night without when I could so that I could let what either felt like a big hurdle or a complex interaction marinate a bit. It definitely worked. With fresh eyes, I was more productive and more often than not found a solution.

Learning Takes Time and Sometimes Making Tough Choices

I had to make what still feels like major sacrifices this semester. This fact was hard to reconcile in my mind. Either I spend the time to write a blog post about a book I read or I read the peer-reviewed papers in order to write a very important literature review for a job for which I am getting paid (UX Lab). Either I spend the time learning how to code to complete my project or I spend the time creating a visual style guide and running all of my colors through WebAim for documentation.

All were important. How do you choose? I chose to be a responsible employee so that my boss and her professional endeavors and schedule aren’t compromised. I chose to code over visual styles because it is a skill I have not mastered. Those decisions may not have been right but those were the choices I made and I have no regrets. I learned plenty of skills and I learned and continue to learn a great deal about myself. Most of all, I’m proud of the work I created.

These two books helped me get some perspective and keep my sanity. I’m certain I’ll refer back to them many more times in the near future. Thank you RJ Andrews and Michelle Rial.

Data Viz: My First Year Anniversary is Coming Up

It’s almost January 2020. It’s almost the one year anniversary since I began my data visualization journey. My guide: Alberto Cairo.

Those three facts have such meaning as I write this post some 30,000 feet somewhere over the Eastern seaboard between Florida and Washington, DC after the most intense semester yet. I’m exhausted. I’m full of emotion. I’m grateful, once again, for the experience. (I published this after arriving home and some much-needed sleep.)

January 2020 means the upcoming spring semester will be my last as a graduate student.

I started courses in the Interactive Media program in the Fall of 2018 at the University of Miami within the School of Communication. I’ll graduate in the spring and have a Master of Fine Arts. Yes, I can predict the future!

It also means I have one more semester to take advantage of any class that will help me to learn more skills to continue to practice data visualization. I’m limited to three and here is what I chose:

Advanced Infographics

The title is misleading and D3.js will be the focus of this course in addition to workflow best practices and of course, storytelling. I’m looking forward to taking what I was able to learn from data viz studio to this next course. I’m also certain additional online courses will be necessary. I’m eager to finish going through Amelia Wattenberger’s book, Full Stack D3 and Data Visualization and go through this Udemy course (Black Friday deals on courses FTW) and perhaps this course about React and D3. We’ll see.


Another one of my classes will be a WebGIS class in the Department of Geology and Regional Studies at the University of Miami. Now, had I known I would have fallen in love with data visualization, I would have pursued the Certificate in Geospatial Technology while simultaneously pursuing my M.F.A. Alas, a missed opportunity and I’ll take the one course I can while I have the chance.

One of the great bennies of being a student again are the unbelievable resources available to you. The Otto G. Richter Library has an incredible collection of maps. Abe Parrish is also the resident GIS Services Librarian and a few weeks ago, he was kind enough to set me up with a GIS account to access various ESRI GIS software and learning videos so I can prep for my class next semester. I’m excited. Maps bring back fond memories of being in the car with my dad and being the official family road trip navigator.

My first data viz project about why seniors are struggling in Florida.

My Capstone Project

After a lot of back and forth on a capstone focus, I decided to focus on exploring and understanding a photography competition archive. Yes, more unstructured data. This was after a more product-based focus to design a solution specifically for women managing menopause or even perimenopause. I was advised to not go down that route and I’ll write more about that later.

Circling back to my capstone, it is an archive of more than 40,000 images with a lot of other data types associated with it. My December will be spent getting to know what data I have from the competition and also what data from other sources could supplement it.

This is going to be fun as I’ve spent most of my career working very closely with photographers in newsrooms, hiring them, collaborating with them on books and lastly, helping them navigate marketing when social media started to become the norm and websites were shifting from Flash to HTML. This experience feels a bit like coming home. Changed, but still me with plenty to offer the community.

My second data viz project about HIV and how it has changed in the United States.

In One Year, I’ve Learned a Lot

I started my first data viz class in January 2019. I didn’t know what to expect so I didn’t. I had read a bit about Alberto Cairo before I arrived at UM which made my decision to attend UM all the better. It spoke to the School’s commitment to hiring working professionals and attracting the best from the industry.

But Infographics and Data Visualization, a specialization within the Interactive Media program was not my focus. I went to UM to learn more about and fill in the gaps with UX research and UX Design from Dr. Barbara Millet. Infographics and Data Visualization, I thought, would be great to learn more about since it would complement ux design and research. The fact that I would learn from a well-respected journalist, designer and professor? Cool, icing on the cupcake.

Imagine my surprise when I started thinking about shifting focus to data visualization. It wasn’t just another cool class, I suddenly started to think about doing it long term. Really? Now?

All spring, I went going back and forth. At one point I was fully committed. No. Yes. No. Yes. The debate in my head raged on through the remainder of the semester and through the summer. Do I switch? Then it dawned on me: Why do I need to switch? I could learn as much as possible about UX design and data visualization. There is clearly an intersection.


So while I continued to learn more ux design and ux research, Alberto was patient and always supportive regarding my plans for my future (graduation and job hunting). He also continued to encourage me (and all of his students) to fill in my knowledge gaps: statistics, R, D3… but also to read more about psychology, sociology, etc. Read, read, read.

Now, that may sound like more work (yes, it is) but I’ve come to learn that when professors encourage knowledge-building, you have to be grateful. To be in an environment such as the University of Miami is a privilege. To have someone care enough about your intellectual well-being and education so that you can become successful in the fullest sense as a designer and as a citizen and be enriched as a human being is pay dirt.

My third data viz project: The sentiment of Anne Sexton’s poems.

So in one year, I learned:

  • How to use Illustrator more than I did
  • How to use Flourish, RawGraphs, InZight, Data Illustrator
  • How to find stories that could use an infographic or a visualization
  • How to read and pull apart charts more than I did
  • The language of Graphicacy
  • What charts are good starting points for certain types of data
  • How to question charts I see all around me
  • How to explore data
  • When to and how to question data
  • How to use excel more than I had ever before
  • Why annotation layers are important
  • Why the “run of the mill” bar chart is not so run of the mill
  • How to build a chart in D3
  • How to use Tableau, DataWrapper, Charticulator
  • How to use CSSGrid
  • How to use CSS for animation
  • How to use R
  • Learning and continuing to learn statistics (now with R!)
  • How to do the basics of sentiment analysis
  • Who the movers and shakers are in Data Visualization
  • Where data visualization is being created and environments that understand its value in communicating information
  • To be open to the different ways of visualizing data
  • To remember to read about the empirical research done with data visualization
  • How to better manage your time
  • How to be a more informed, responsible citizen and designer

The list above is a shortlist and an incomplete list. Also, Alberto didn’t teach me everything on this list. I have a small but close community of fellow classmates — Alyssa, Qinyu, Maria, Mackenzie, Melissa— who also contributed to my learning. Still, Alberto provided me with a safe and equitable environment in which to learn. He gave gentle reminders when I made mistakes or misjudged. He was patient when I didn’t understand. He was also frank and asked tough questions. Most of all, he encouraged a “can do” attitude. All of these qualities factored into why I want to continue to learn and most definitely practice more data visualization. I’m looking forward to one more semester to take full advantage of his presence at UM for my capstone and to help me prep for my next journey.

Women in Data Viz: No Small Effect

Alberto brought in so many female data visualization speakers that as a woman, I couldn’t help but be inspired and encouraged. We women share similar struggles, stories, and wins. It was nice to hear them speak openly and honestly. To also learn that some did not have computer science, statistical or math backgrounds and still become successful data journalists and designers made me hopeful. Well, if they can do that, I can too. I may be late to start but if I can accomplish what I have in a year, stay tuned for what I will in the next.

2020 is looking good.

Anne Sexton: Poetry Between Pain

I’ll say it loud and proud. I’m excited to share this final project from my data visualization studio class about Anne Sexton’s poetry. I wasn’t sure I would be able to say that a few weeks ago but I have overcome numerous hurdles and have finally made it out of the Swamp of Despair. Read on about my journey and the process.

The Emotional Journey of Creating Anything Great modified by Alberto Cairo for his Data Visualization Studio class, Fall 2019.

Why Anne Sexton?

A sociology professor introduced me to Transformations and Anne Sexton while an undergrad. I can still remember how these poems busted my Disney-fied interpretations of fairy tales. I remember being mortified and fascinated at the same time. I recall thinking the experience is a good because, at the time, I was ready to face and look at the harsher realities of life. I was already waking up; experiencing life in a city outside of a privileged suburban life. It was the perfect time for Anne Sexton to come into my life.

Fast forward decades later as a graduate student, I found myself proposing a data visualization project to get to know Anne Sexton and her poetry a bit more. Somehow the period of my life while an undergrad and my life currently as a graduate student feel similar. It’s hard to explain and one thing is clear, changes are taking place.

Python, bring it on.

Thinking back, I had no idea what I was taking on.

It was the beginning of the semester and I had made it a personal mission to learn and practice more coding. After some research on text analysis, I was certain Python was the right path. I read it can be applied in so many ways from visualization to physical computing to machine learning. If I’m going to learn, might as well go with a language that is versatile; more bang for your buck kind-of-thing. Right?

Well, I knew I would need some guidance so based on past experience, I decided to reach out to the data viz community for some leads on how to get started.

Data Viz Twitter

I’ve spent years with Photo Twitter and Design Twitter. Both still very cool communities I dip my toe into but Data Viz Twitter is cupcake for the foreseeable future. For example, when I tweeted a request for Python help, Mindy McAdams, a Digital journalism professor at the University of Florida was so kind as to tweet me one of her tutorials on setting up a Python environment.

I struggled a bit at first as I was not familiar with command-line and the idea of typing into Terminal was intimidating but Mindy’s tutorial got me up and running with Jupyter Notebook and a fresh install of Python.

Miniconda over anaconda. Check. Jupyter Notebook as my IDE. Check.

Data Wrangling: Gathering Her Poems

Many of Anne Sexton’s poems are available online but not all and I definitely wanted to be efficient so I went about learning how to scrape the content. In the middle of reading how to do just that I discovered ParseHub. Easy to use but limited to the free version, I collected a sizeable number of poems. The downside was the duplication and I wasn’t an expert. So in order to make sure I had all poems I bought Anne Sexton: The Complete Poems to cross-check my collection. It was a great idea because some online sites only featured parts of her poems.

Anne Sexton: The Complete Poems became my Bible and it eventually helped to structure my project. Given that I was not going to re-publish any poem and the data would remain in my hands for a school project, I felt better when I needed to do a bit of PDF editing magic to extract her poems.

In total, I collected 308 poems from online sources and ebooks.

Just a few of the text files that made up the Anne Sexton corpus.
A friend had a script that helped to pulled the data from my text files into an excel sheet.

A note about hidden characters

There were a lot of these funky characters —Â,Áí, Á ,Ķ Äî‚Äô— that caused quite a few hiccups. I thought if I made sure the text was UTF-8, all was good, but no such luck. There were invisible characters in addition to those above. Find/Replace came in handy for most but didn’t catch all and I found myself having to manually clean them up.

Python? What about R?

I’m not exactly sure when I realized I needed to say goodbye to Python but I felt that too much time had passed. After several weeks I felt I had not made any progress. I was worried.

What if I’m not able to pull this off?

Did I just set myself up for major failure?

Is that so bad?

Uh yeah…sort of.

I had been moving through Codecademy’s python course but could not find much online that would help a beginner get started with the basics of text analysis using Python. I found many machine learning tutorials and many articles that provided instruction on how to use packages for text analysis but I found a giant gap in understanding why.

I shared these concerns with fellow designer and classmate, Alyssa Fowers, and she suggested I give R and this book a try.

But I was stubborn. I had set a goal and I wanted to follow through. It wasn’t for another few weeks that I decide to let go of my grand ideas about Python…

R, I love you.

Yes, it happened that quickly. What can I say?

There’s no particular reason I didn’t start with R from the beginning. I just wanted to learn Python. But in October the university had a long weekend break. I had time to focus on one thing so I decided to give R a run. If I could make progress over the weekend, any progress, I’m game.

Using the online version of Text Mining with R and a Data Camp text analysis course and tutorial using the songs of Prince (Little Red Corvette anyone?) I taught myself enough R in one weekend to feel good and make progress on my project. I started to make my first charts in R dutifully following tutorials then I started to practice with Sexton’s poems. Of course, there were many errors, charts that made no sense, and plenty of experimentation. I made useless chord diagrams and too many word clouds. It was fun.

The beauty of Text Mining with R is that while learning R and I was also learning about text analysis.

Some of the questions I had about her poems:

  • How many words exist in the collection of published works?
  • What are the most frequent words throughout the collection? What are the most frequent per poem?
  • What is the diversity of words used per poem?
  • Are there frequent word relationships?
  • What is the overall sentiment of her poems? Does it change over time?
  • What is the most positive poem and the most negative in each collection?
  • What is the most negative and positive collection?

Text analysis and sentiment analysis is quite the specialization and I hope to learn more since I’ll be working on a project next semester with text and images. It should not have surprised me but sentiment analysis is incredibly subjective so based on what I learned in Text Mining in R, I used the Bing lexicon for my project.

Turns out, this project and process dove-tailed nicely with an Artificial Intelligence class I took with Dr. Chuan. We had many conversations in class about ethics and AI. Each time I have to remind myself to remain calm when I learn what people are doing just because they can.

One book I need to purchase is Liu Bing’s book, Sentiment Analysis and Opinion Mining. I think this book will help me understand a lot more especially for future projects. Hmm, my capstone comes to mind.


I realized early on that I had to take notes; lots of them. At one point I had accidentally filtered out the word, “God” while also removing stop words. And there is probably some magical way of tracking variables and data frames but I got a bit carried away and started to lose track. I had to make myself a chart to understand and remember that this set fed into this other set and created yet another set. Highlighters became my BFF.

Now I recognize that my workflow may be incredibly flawed but honestly, that was the least of my concerns. I had an assignment and deadlines and I did what I could while making a wonderful mess.

The notes saved me quite a few times because I had to redo some of my charts and having these notes was sunshine. Thank you past self!

First Draft: Creativity, Move Aside

“It looks too much like R charts.”


That is the one line of feedback that burst my R bubble. But yes, I agreed, it did look too clinical. I started to wonder what I could do differently when a different thought or question came to mind: Why is it that creativity went out the door? I somehow couldn’t think beyond getting things to work in R.

It brought to mind a quote I read from Daniel Hooper, the founder of Principle, an app for animating user interfaces:

Even if you’re a person who knows both design and engineering, your brain operates very differently in each state. Putting people in the technical engineering mindset suppresses their innate visual creative skills.

I lost sight of how to present what I was discovering about her poems in a compelling and cohesive way. Seems obvious. Yet, I think many of us experience this phenomenon when learning anything for the first time and especially when it is technical. I have a love/hate relationship learning code (or anything new) because of the simple fact that it is unfamiliar and in that process you get stuck and stumble often. But once the light bulb goes off…BOOM.

The Moment

I’m not sure when it happened but suddenly everything started to gel. I think at some point I finally had a clear direction. Coding the website was a big factor. I initially had plans to use scrollytelling but I soon realized I was trying to fit content into buckets or in this case, content into a popular storytelling format. A format that isn’t required nor is it always appropriate.

We had a brief discussion in class about interactivity and learning when it might be needed or not. More often than not, making a chart interactive wasn’t needed. I think this goes back to balancing design and engineering. Just because you can doesn’t mean you should or have to.

I color the backgrounds of my containers to see them.

I started to play with parallax and split screens to see how charts, text, images would play out “on the page” so-to-speak. I also learned a bit of CSS animation to see if the word cloud I had in mind might work well animated. I also learned a bit about CSSgrid which blew my mind. No more floats? Cool.

Broken version of each section using parallax.

Essentially I was sketching with code in a way that made sense to me. Definitely not pretty but that wasn’t the goal.


If there’s one big thing I learned through this process when it comes to coding it’s this: Start. Go ahead and break things. You won’t learn unless you start and stop worrying about breaking things. Change one thing. Test it. If it breaks you don’t lose track of what broke it. Firefox is also your best friend (ok, or Chrome).

Interlude: Getting to Know Anne

Reading her biography I came to understand Anne Sexton much more intimately. I also started reading Anne Sexton: A Self-Portrait in Letters which is giving me more insight and understanding. Some of what she did was downright shocking and self-indulgent but perhaps that was her way of coping. Unfortunately, the impact of some of her actions had on her family was heartbreaking.

Philip McGowan’s book is a more contemporary lens from which to understand Anne Sexton and her work. So many reviewers slammed her posthumously published works as ramblings. McGowan helped me remember the mind is complex and that sometimes text can be limiting. How does or how can poetry push the boundaries of words. There’s definitely more to think and learn about that.

These two books in addition to the wealth of information online bridged the events in her life with her poetry. Where I could, it was important to give greater context to the charts because they are negative.

The Big D3

So, one of my last charts needed to be interactive. This meant I needed to learn it toute suite. Previous attempts, however, were not positive so I needed a strategy (back up plan) because I was feeling good about how my project was shaping up.

I learned from my dad to always have a back up plan. The solution wasn’t ideal but I had to find an alternative to present the sentiment of each poem in each collection just in case I couldn’t learn enough D3 to pull it off. After trying DataWrapper, Flourish, Charticulator, and RawGraphs, I settled on Tableau. The only pet peeve (ok, a big one) was the branded tab bar that is carried with embedded Tableau charts. I tried so many different ways I knew to hide the bar but no luck. The code was buried deep and I just couldn’t figure out a way to hack it.

Made with Tableau Public

Nope, Must Be D3

It’s interesting what motivates me. I wouldn’t be all that disappointed if I had the Tableau chart in my final project but the lack of customization and that tab bar was enough to make me sit in Starbucks until midnight after sitting for several hours before to at least get a y-axis with negative values and an x-axis.

Plus, I just had to create a chart using D3. Python was one goal. D3 was the other in a long list of goals for the semester. Next semester I’ll be taking a course focused on D3 so I also wanted to get a head start because I want to focus on storytelling. The tool shouldn’t feel like one.

After much sputtering, the D3.js Graph Gallery along with 3 tips (below) from my good friend and classmate, Qinyu found me on the path toward success. Naturally, there were many other resources online such as this, this and this. All in all, too many to list and frankly too many to track but another goal for this semester was soon to be crossed off my list. Yes.

website screengrab

The Three D3 Tips = Priceless

Tip 1: Read in data this way

For some reason, this structure makes the most sense to me.

  .then(function(data) {
      // data is now whole data set
      // draw chart in here!

Tip 2: Group your data

Just like in R when I grouped the data by collection, I needed to group the data for D3 to process. I didn’t make that connection. In hindsight, not knowing this was the source of my frustration weeks ago. Of course, it makes a whole lotta sense now but when I started to learn how to build a scatterplot in D3 I was looking at tutorials that used only two dimensions and linear data rather than ordinal data. Lodash.js, according to Qinyu, would help me a lot. Oh boy, did it ever. (Turns out, in the process of learning how to use lodash with D3, I also learned that there is a nest function in D3. I’m assured by the professor for our D3 course next semester, we will cover this – yay. )

Tip 3: scaleBand rather than scaleOrdinal or is it scale.ordinal or

So, I think one of the great aspects of learning these days is the on-demand results Google spits out. Like all searches, it depends on what and how you input keywords and phrases but also if people have taken the time to optimize their content and web pages. Stackoverflow has been a gold mine but it also full of dated crap. There were loads of dead ends which is fine but the process can really become demoralizing rather quickly. So, when Qinyu informed me that scaleBand would be better than scaleOrdinal, I went with it.

While I realize there are many different ways to write code for D3 charts, these three tips helped me down a more successful path. One other concept I’ve been thinking about is this: In D3, the structure is counterintuitive because you create these invisible placeholders before you draw shapes and attach your data.

The Beginning

R, more CSS3, CSSGrid, parallax, CSS animation, a taste of python, and D3 … wow. Sometimes a list is what you need to see what you’ve learned. Sentiment and text analysis is definitely something I want to practice more. I really had no idea what was involved in analyzing text. In fact, once I learn more I’d love to re-explore.

It dawned on me recently that I’m not even a year old learning about data visualization. In that case, I feel pretty damn good about my final project. This I believe is the beginning of what I hope to be the next phase of my life.