I’d love to get my hands on the YouTube information for analysis, but their API doesn’t look like it’s data extraction friendly.

Thanks for taking the time to comment. Don’t be a stranger, I’m a fan of your work. I like what you’ve done with Lyza. I stayed away from it after Stephen Few ripped it a while ago. It’s good to see folks go against the grain and turn out quality stuff.

See: http://www.youtube.com/watch?v=Lx39t_EikbI

ReadWriteWeb had a good write up on how it was done: http://www.readwriteweb.com/archives/how_old_spice_won_the_internet.php

I am sure YouTube is having a great time with the analysis of the viewing trends of those videos.

After reading the comments on your post, I think Joe Mako’s suggestion of a dot plot is quite good, as is Funky Gawy’s suggestion to “fill up a 30×29 grid with these symbols for the human category, but fill a much smaller 5×6 grid for all the rest. This is so often done inappropriately but this might be the paradigmatic use.”

I’d like to see someone take a shot at Funky’s idea with this example.

Ideally I would want to keep the scale the same as you slice the data, but you can’t use a consistent linear scale in this case as the 350,000 total is so much larger than the 800 or so people who show positive for these genetic markers. So the natural choice (?) is to switch to an area, allowing you to decrease the dimensions – hence bubble charts and their ilk. Now they of course have their issues as we know.

I tried a few things like having the 350,000 be the background for the whole image, but nothing was that satisfactory. I think your option is probably the best, if you’re going to chart it. Perhaps just text would work just as well.

If you’re going to use an area format like the original, I’d change the color of the light gray 350k to a dark color for contrast. I’d also use a square instead of a bar when slicing from 10,000 to 3000 – keep the shapes consistent.

Now can you do Britney Spears?

-Ralph Winters

The typical pattern I see in your Tweets is an initial Tweet followed by 3 repeats, spaced at 8 hour intervals. For example, one of my favorite recent Tweets of yours is about the couple who discovers photo showing paths crossing as kids at Disney World. I got chills seeing that photo. Anyway, you originally tweeted that one on June 9th at 8:40 pm Pacific. Then the Tweet was repeated on June 10th at 4:40 am, 12:40 pm and finally on June 11th at 8:40pm. I’m counting this as 3 repeats and one original, not 4 repeats.

If you’re saying that you Tweet once, and then repeat that initial Tweet 4 times, then I’ve missed a Tweet somewhere. If you’re saying that each of your Tweets is generally Tweeted 4 times, then we’re saying the same thing.

I limited the scope of this analysis to your Twitter acccount ‘GuyKawasaki’ and looked at holykaw.com links only. I did read your ‘How I tweet, just the faqs’ post while doing my research, but I have to admit, I’m still a bit unclear on whether or not you’re counting your original Tweet in the repeat count.

I know this is a hot topic, one that can make a big difference in the effectiveness of a Twitter campaign. Thank you for taking the time to comment on it.

–John

Wow, I didn’t know I was doing some of this stuff. One thing: I repeat the Holykaw.com tweets 4 times, not three. It’s been like this for months.

And I do not schedule the initial tweets. They just happen whenever me or my ghosts happen to post, and I’m often in far off timezones. The rational behind the 4 x 8 hours apart is that no matter when we post, this strategy means we’ll hit prime Pacific even time.

Thanks,

Guy

Alex, you’re absolutely right about not adjusting for inflation. I can’t wait to check our your insights on that.

Naomi, I didn’t know about that setting in Excel. Thanks for pointing it out. I won’t ask why you know about it. I’m sure you never made a 3D graph in your life ;>