A picture tells 1000 words, but a graph can give more insight

3 guys walk into a bar. Its a Tuesday. They have a couple of pints. They leave. It's now Thursday. The same 3 guys walk into a bar. They have a couple of pints. They leave. (If you're waiting for the punchline...sorry...read on and I might just share a joke at the end...) Anyway, so what? We have 3 guys. They like pints. Not so unusual.
But, for arguments sake, lets map their intake on a graph. Lets say the data looks like this:

If we showed their intake levels on a couple of different graphs, it would look like this:

A basic 3D clustered column graph:

The graph very clearly shows the trends, and lets you make some assumptions, like: Jim is looking forward to the weekend, maybe Bob has a Friday meeting, and Joe, well, maybe Joe is having a bad week.

A stacked 3D column graph:

This graph adds everything together for the week. It very quickly shows you the total for each guy, without you having to do mental arithmetic. (Admittedly, not so tough with the given data, but if you were looking at sales data for 10 different types of widget for a week, you might appreciate this graph a bit more).

What if you wanted to see the % split of their intake during the week? Easy....




















The 100% stacked graph takes the total data for the week per man and shows you the % intake per day. Again, this would be useful if you were analysing sales across the week per widget, and wanted to assess whether any particular day was good for a particular widget, in order to up production or distribution, or increase promotions on the 'not so good' days.

Now....lets think about the pub. What are their strong sales day, and who is their favourite customer to be given extra loyalty points?
Take the 3D stacked graph, and using the same data, simply swap the axes:

Going on this graph, Joe is definitely the man to be getting the free beer mats or bottle openers. And maybe the pub should plan a pub quiz on Tuesdays to up the sales? 



So what is all the point of this pub talk, if not to remind you that its the weekend? I just wanted to illustrate how useful and effective graphs can be, if used correctly. I have used the most basic type of graphs here, but they show the trends really well. Graphs can get alot more complicated than this, and they all have their place in data analysis. The trick is to use the right graph, for the right data and for the right audience.

All good graphs should do the following 2 things:

1) Give you an instant picture of the current trends pertaining to the data used; and

2) Lead you to ask more questions about the data, forcing you to analyse your data in more depth.

By using graphs this way, you can very simply and quickly find out trends, deviations from trends, and anomalies. And they look prettier than rows upon rows of data.

Ok, so as promised...a joke. Sticking to the bar trend...2 atoms walk into a bar. The 1st atom says, "Wow, I partied so hard last night I think I lost an electron." The 2nd atom replies, "You positive?"

And with that, we end this week's lesson. If you would like a couple of handy tips on graphs, why not sign up to our newsletter at:

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Have a great weekend, and happy spreadsheeting!