You can download the data in CSV format or in Excel. There are four columns: actor name, show, pay per episode, and show type (comedy or drama). I entered this data by hand to get it in a manageable format, so keep an eye out for typos (just like in real life!).

The challenge here will be that there's quite a few names on the list, so it's a bit too much to show every value at once. You'll have to decide what you want to show and what story you want to tell. Do actors in drama or comedies make more? What kind of distribution do you see? How do top actor salaries compare to that of the average actor?

One more time, here's the link to the CSV and Excel files. Leave links to your graphs in the comments below.

Deadline: February 22, 2011

The graph isn't too bad, but it's kind of busy and could use some design. Can we do better? I think so. Here's the data as a CSV file. Get your graph on, and link to your efforts in the comments below.

You might also find the original Pew Research article helpful for background as well as more detailed data for demographic breakdowns.

One tricky point. Respondents were allowed to provide two answers if they wanted, but we don't have the raw data, so we don't know the exact answer breakdowns. That's why the percentages for each year don't add up to 100. Does this affect the design or does a small note on the bottom (like in the original) suffice?

Deadline: January 20, 2011

[Pew Research | Thanks, Elise]

The Jimmy appeal banner was the big performer by far, earning more donations as wells a much higher click through rate. The results page shows about twice as much donations for the Jimmy banner, but via the spreadsheet with the more specific breakdowns, that's actually because the Jimmy ad ran twice as much as the other three.

What's your take on the data? Get the spreadsheet here, and post links to your graphics in the comments.

Deadline: Friday, November 19.

[via @kevinweil]

Deadline: October 27, 2010

For this round, we'll take a look at results from the National Survey of Sexual Health and Behavior conducted by Indiana University. They asked over 5,000 participants if they've engaged in certain behaviors in the past year. They also asked age and gender.

Here are the results of that poll (click to enlarge):

See comments below for the table as an Excel spreadsheet.

The table itself is pretty interesting. I'm sure the first thing you did was to to your age group and gender and scan the column. How does your group relate to the others? Can you represent the data visually? Leave links to your graphics in the comments below. Or if you don't have time to make something, just tell us what you'd do. Finally, you're also welcome to email me your attempt if you don't have anywhere to upload and link to.

One rule: let's keep it tasteful. I probably don't need to say this, but given the topic, I probably should. Have fun!

Deadline: October 27, 2010

Here we have a case of worthwhile data and an interesting story about obesity trends. People are getting heavier younger. The graph was made to show this; however, it's hard to understand and kind of makes things more confusing. Can you redesign the above, using the same data, to tell the story more clearly?

The Data

This is what we know about the data. It comes from a study that took place between 1971 and 2006. We have obesity rates, separated by when study participants were born. So for example, in the above graph, the orange line shows the obesity trend for people who were born between 1956 and 1965. When that group was in between 30 and 39 years old, about 27% of them were obese.

It's a little confusing at first, but let it simmer for a little. It's actually not too bad.

The Question

Okay, now the question: are people getting fatter faster? The original graph suggests that yes, people are, but the story isn't as clear as it could be. Plus, it took 19 powerpoint slides to tell it. Your job is to put it all in one graphic. Are you up for it? I think so. Leave your suggestions and links to remakes in the comments below.

One more time - here's the data [csv], and you can find more info about the study here.

[via FD forums]

Last week I posted some parallel coordinate plots that related SAT scores and class size. Now it's your turn to take a crack.

You can find the data that I used (and more) from the National Center of Education Statistics. There's a link to download the data as an Excel file. You can find pupil/teacher ratios here.

The two best entries each win a copy of David McCandless' The Visual Miscellaneum: A Colorful Guide to the World's Most Consequential Trivia as well as eternal glory on FlowingData. Yes. Eternal.

Different Views and Other Factors

There were a lot of great comments on alternative ways to view the dataset and other factors that play into average SAT scores and education quality. Obviously class size isn't the only thing.

For example, the percentage of graduates who take the SAT in each state plays a big part in the story that wasn't in my original graphic. Eighty-seven percent of Maine graduates took the test while only 3% in Iowa. There's going to be a higher average if the only people who take the test in your state are the ones who want to go to college.

Some also suggested a scatterplot with some line-fitting to tell a more complete story while others wanted some actual numbers to see what was up. Both good ideas.

How to Enter

For those new to Visualize This, here's how it works.

1. Take your best crack at visualizing the data
2. Submit your entry to this thread in the FlowingData forums

If you haven't registered in the forums yet (it's free), you'll want to do that first.

A few of you posted alternatives in the comments of the original post. You're welcome to submit those as your entry. Just make sure you post it to the forum thread.

Finally, you're welcome (and encouraged) to bring in other data if you feel it tells a better story.

Get your entries in by November 24, 2009, 11:59pm EST. One entry per person please.

The Prize

Like I said, the two best entries will each win a copy of The Visual Miscellaneum. If you're international, you'll just have to cover shipping. Entries will be judged based on appropriate interpretation of the data, design, aesthetics, and how well you tell the story of class size and quality of education (not just SAT).

Good luck, everyone. I'm looking forward to what you all come up with.

This round of Visualize This is a fun one. We've got the Rambo kill chart, which shows well, a breakdown of kills in each of the four Rambo movies. It's surprisingly detailed with several cuts of the dataset like number of bad guys killed by Rambo with his shirt on and off, number of good guys killed by bad guys, number of people killed per minute, and several others.

The problem is that the data is just in a table. Surely we can do better than that. Can you visualize this?

Person with the best viz gets a copy of Darrell Huff's classic How to Lie with Statistics. Get your entry in by July 1. One entry per person.

Cool Threads

• Visual Ideological History of the US Supreme Court: Alex Lundry visualizes the last seven decades of ideologies of US Supreme Court judges. Interact through the years and split the data in several ways.
• Visualizing Biological Data: VisualMOA is an information browser for the Microbial Online Analysis database. Is it useful without subject knowledge?
• Processing vs. Flash: Both are heavily used for visualization on the Web, but both have their pros and cons. Processing is good for coding beginners. Flash loads quicker using vectors. Which one should you use?
• Mapping SPAM and Sensornet Attackers: Using some heat mapping and Circos, Ben, a visualization beginner, is looking for some input.

A couple weeks ago, FlowingData ran another Visualize This challenge. I posted a dataset on the number of days it takes the average person in each state to pay his or her taxes and asked you to visualize it. The number of days vary, because tax burden varies state-by-state. The day all taxes have been paid is dubbed Tax Freedom Day. Alaska has the earliest Tax Freedom Day while Connecticut has it last.

Here are the interesting results you all came up with. Thanks to those who participated. Nice work all around.

Joe Mako used Tableau Software to create an interactive dashboard:

Andrew threw together an interactive Adobe Flex based visualization:

Annie took a different route with an interactive calendar interface. This was definitely one of my favorites, partially because of the smooth interaction and partially probably because I wasn't expecting it:

Frank went with a map on Many Eyes, the online application that promotes social data analysis:

Rachel, from bime, used the Tax Freedom Day dataset to test their online dashboard tool:

couvares from Verifiable, another online data analysis toolset, put this one in:

Finally, Alex, tried out a couple of readily available online applications to visualize the dataset, one with Verifiable and the other with UUorld:

The Data

About 28.2% of the average American's income goes towards taxes, which means the first 103 days of the year is to pay for government. At the end of these 103 days - April 13 - is Tax Freedom Day. However, because of varying state-by-state tax burdens and average incomes, Tax Freedom Day varies by state. Alaska, for example, has the earliest Tax Freedom Day (March 23) because it has low state and local taxes while Connecticut is last on April 30, because of "extraordinarily high federal income taxes." For this Visualize This we're looking at the number of days each state spends paying taxes this year (2009).

Your Mission

As with previous Visualize This segments, show us your best shot at visualizing the Tax Freedom Day data in this forum thread. I've put the data in an Excel spreadsheet that you can find at the bottom of the forum post. You are welcome to incorporate any other data too if you feel that it adds to the story.

Map? Graphs? Both? Let's see what you've got. Oh, and most importantly, have fun. If you haven't registered a (free) forum account, you'll want to do that first.

DEADLINE: April 30, 2009

[Thanks, Alex]