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I'm a fan of histograms above nearly all else. When I need to show change over time, I actually prefer heatmaps: http://deliveryimages.acm.org/10.1145/1810000/

I'm a fan of histograms above nearly all else. When I need to show change over time, I actually prefer heatmaps: http://deliveryimages.acm.org/10.1145/1810000/1809426/gregg3.png http://deliveryimages.acm.org/10.1145/1810000/1809426/gregg3... http://queue.acm.org/detail.cfm?id=1809426 http://queue.acm.org/detail.cfm?id=1809426 It's like a scatterplot, but a bit better at showing collapsed data (i.e., there's a lot of data at that point on the graph -- is in more or less than that other jumble of Xs?).

brown9-2 14y ago
Very frustrating that Oracle broke all the blogs.sun.com links in that ACM article.
- brendangregg 14y ago
  Frustrating, yes; I have linked the real images here http://dtrace.org/blogs/brendan/2010/06/05/visualizing-system-latency/ http://dtrace.org/blogs/brendan/2010/06/05/visualizing-syste...
tmoertel 14y ago
Histograms are great for visualizing relative density, but they have problems when items don't sort neatly into bins. They also make it hard for viewers to accurately estimate the portion of the distribution that falls within arbitrary ranges, say the percentage of smokers aged 24 thru 35. Plotting the empirical cumulative distribution function solves these problems. For a numeric random variable X, its CDF F(x) gives P(X <= x). So if X gives the age of smokers, the answer to our earlier question is just F(35) less F(23). Plotting F over all values of X, then, lets us not only "see" the shape of the distribution but also answer range questions: just lookup two points on the graph and subtract. Some examples: http://docs.ggplot2.org/0.9.2.1/stat_ecdf.html http://docs.ggplot2.org/0.9.2.1/stat_ecdf.html
- sesqu 14y ago
  The problem with the ECDF is that it's harder to get a feel for, being cumulative. I spent a few days once playing with N-bin histograms, and still feel those should be the default (with non-compact ends, maybe gamma distributed) over smoothed hacks.

brown9-2 14y ago

Very frustrating that Oracle broke all the blogs.sun.com links in that ACM article.

brendangregg 14y ago
Frustrating, yes; I have linked the real images here http://dtrace.org/blogs/brendan/2010/06/05/visualizing-system-latency/ http://dtrace.org/blogs/brendan/2010/06/05/visualizing-syste...

tmoertel 14y ago

Histograms are great for visualizing relative density, but they have problems when items don't sort neatly into bins. They also make it hard for viewers to accurately estimate the portion of the distribution that falls within arbitrary ranges, say the percentage of smokers aged 24 thru 35. Plotting the empirical cumulative distribution function solves these problems. For a numeric random variable X, its CDF F(x) gives P(X <= x). So if X gives the age of smokers, the answer to our earlier question is just F(35) less F(23). Plotting F over all values of X, then, lets us not only "see" the shape of the distribution but also answer range questions: just lookup two points on the graph and subtract. Some examples: http://docs.ggplot2.org/0.9.2.1/stat_ecdf.html http://docs.ggplot2.org/0.9.2.1/stat_ecdf.html

sesqu 14y ago
The problem with the ECDF is that it's harder to get a feel for, being cumulative. I spent a few days once playing with N-bin histograms, and still feel those should be the default (with non-compact ends, maybe gamma distributed) over smoothed hacks.