[hellomisterofau-blog]

Byrd articulated her above comments in a panel that nodded to visualization as a pathway to STEM for under-represented groups. Visualization is not discipline-dependent. She suggested that we could engage students that don't necessarily self-identify as computer scientists, and encourage them to think critically about—and visualize—data and information in their existing area of interest, from science, to the humanities, sports, and beyond. Joseph Cottam, a researcher at Indiana University, echoed this sentiment in the same panel when speaking of his experiences teaching high school workshops: When a visualization project allows individuals to see themselves—or their interests—in data, it can act as a powerful engagement tool.

http://blogs.scientificamerican.com/sa-visual/the-science-of-visualization/

[hellomisterofau-blog]

One thing that struck me is that you find that message redundancy and data redundancy is effective. It seems that the principles of lean design and efficiency in data presentation – here I’m thinking of Tufte, who says that if you have to explain things too much, your underlying design is flawed – cut against ideas of simplicity and clean presentation. How do we balance these competing ideas of redundancy and simplicity?

http://www.storybench.org/understanding-what-makes-a-visualization-memorable/

[hellomisterofau-blog]

“Fundamentally learning about the world through data is really really cool,” he explains. “The analyses that get me excited are not Google crunching a terabyte of web ad data in order to optimize revenue… [but rather] the biologists who are absolutely passionate about this one swampfly and now they can use R and they can understand it.“

Hadley Wickham, the Man Who Revolutionized R

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some sea worms to celebrate International Polychaete Day (which was yesterday...forgive my tardiness!) 

[hellomisterofau-blog]

Day One 

#the100daysproject #100daysofsketchy

Why I’m picking up the textas and giving the keyboard a rest (sort of...), because you know, I’m typing this right now

[hellomisterofau-blog]

I think working with data is almost a little fraught, because when I started with it, it looked like "sky’s the limit," and I saw it from a very personal angle. Now, if you generate data, the first question to ask about it is, "How is this data going to be used against you?" While I’m still fighting the fight to show the narrative potential of data, there is a real shadow over it. I’m not sure where we land if the value added by having access to your data or having the data saved is greater than the potential damage that that data can create in the wrong hands. That’s something I wrestle with at the moment.

Data Viz Pioneer Nicholas Felton: "There Is A Real Shadow Over Data" | Co.Design | business + design

[hellomisterofau-blog]

At uni we’ve been doing a lot of work on data cleaning and preparation, and the iterative process you go through building a predictive model. In a discussion with another student in an entirely different course I am taking, she shared with me this link...which is funny, and engaging, and shows the importance of understanding the full context of the data set you are looking at.

[hellomisterofau-blog]

Chuffed to have been shortlisted for the latest Information is Beautiful Awards data challenge

http://www.hellomister.com.au/new-blog/2015/3/17/shortlisted-for-information-is-beautiful-challenge

[hellomisterofau-blog]

Put simply: more is not necessarily better; indeed it can be worse. In its extensive use of torture to acquire information, the CIA may have seen this first hand. Extensive torture led not to stronger data, but to larger amounts of weak data, along with more faulty hypotheses and time-consuming dead ends.

The Three Vs of Enhanced Interrogation — Medium

[hellomisterofau-blog]

Who Was Working —

[hellomisterofau-blog]

Probably the most immediate definition of wee things are things that are physically small: little things on a page. We see these all the time in news graphics, and we're probably familiar with some of their forms: small multiples, sparklines, icons, etc. I'll go into more details about all of these. These visual forms work because they serve as extensions of our mind – they are cognitive tools that complement our own mental abilities. They do this by recording information for us to make use of later, lending a hand to our (pretty terrible) working memories, helping us search and discover and recognize. We'll take a look at one task in particular they are great at: letting us make comparisons.

A Big Article About Wee Things - ProPublica

[hellomisterofau-blog]

I created another network graph using R and gephi again to make sure i could replicate what I did yesterday...

This time; frequent collaborators of the Cohen Brothers. All paths lead to Frances McDormand. 

[hellomisterofau-blog]

Before it was a final graphic though, it was a demo piece I hastily hacked into Google Earth using its KML format. I remember feeling pretty proud of myself at how cool even a crude rendering like this looked, and the detailed work I had done to pull out all the data within reports to see these dots surge and wane as I dragged the slider. Then I remembered that each of those data points was a life snuffed out, and I suddenly felt ashamed of my pride in my programming chops. As data journalists, we often prefer the “20,000 foot view,” placing points on a map or trends on a chart. And so we often grapple with the problems such a perspective creates for us and our readers—and from a distance, it’s easy to forget the dots are people. If I lose sight of that while I am making the map, how can I expect my readers to see it in the final product?

Connecting with the Dots - Learning - Source: An OpenNews project A great piece on the ethics and empathy behind data graphics and data journalism

[hellomisterofau-blog]

Well, this was a DOOZY to finish. I'm really just stepping out into the world of R, and let's just say the learning curve is cliff-face steep. Bit once you find those magic functions that help you do a task quickly and efficiently it feels worth the effort. If you are reading this, and know a lot about R, then this will seem like a simple task, and you probably are thinking I went around in a giant circle... here goes...

But before I launch into the drudgery of how I created this, here is a fun fact! Bill Murray and Owen Wilson have been in every film except one. For Bill Murray it was Bottle Rocket and for Owen Wilson it was Moonrise Kingdom.  

I copied some data from a table in wikipedia that showed actors who have regularly collaborated with Wes Anderson. I saw this yesterday and thought I could turn it into a cool little network graph. So I saved the file as a CSV, imported it into R, and then spent most of the day searching for a function that would create a matrix so I could change it into an edge file.  Urgh - anyhow, I found the function - crossprod( ) in case you ever need to know, and then it was a pretty easy task. I played around with an arc plot in R, but it was a bit meh, so took the file into gephi, fiddled around a bit, exported it into Illustrator, and there you go!  

[hellomisterofau-blog]

Over the weekend I watched The Grand Budapest Hotel with my husband. As we watched he was looking at some of the information available on Wes Anderson's films, and shared with me some of the data that he's noticed that I've been playing around with lately - box office, budgets, etc.

I was quite surprised, as Anderson's films haven't been as financially successful as I imagined. Given the buzz around a film like Bottle Rocket, I had assumed (wrongly) that this was a movie made on an oily-rag, yet made a lot of cash at the box office. BUT It's budget was $9M and it only took $560K worldwide. It did get a very respectable 85 RT score though. When you look at the profitability of his films, 3 of them haven't broken even. 

I was a little taken aback by the "hard data". I love his films - even The Life Aquatic with Steve Zissou. I love the layers, his details, the scripts, the cast... 

Anyhow! Here you go - slowly making my way through a list of directors I want to do. Next I'd like to look at actors that he has collaborated with on more than one film. Watch this space! 

(and I have A LOT of catching up to do regarding 30 days and thirty things! But my son is back at school, so I have a lot more time to work on this!) 

(Note: I posted this originally yesterday, but an eagle eyed buddy noted the data looked weird for some of the films, and it looked weird because it was weird ... AKA wrong. So I have fixed this up and replaced the image)

[hellomisterofau-blog]

My friend alerted me to this today, and like her, I cannot stop watching it. I

llustrations by Helen Green. Go and see her work. It's beautiful.

[hellomisterofau-blog]

It’s not crime, it’s reported crime. It’s not the number of meteor strikes, it’s the number of recorded meteor strikes. It’s not the outer diameter of a mechanical part, it’s the measured outer diameter. It’s not how the public feels about a controversial topic, it’s how survey respondents are willing to say they feel. It’s not how many people suffer from a particular disease, it’s how many times a doctor diagnosed people with a particular disease.

Avoiding Data Pitfalls, Part 1: Gaps Between Data and Reality | DataRemixed