Slides from OU Rise Library Analytics Workshop: Rambling about Visualisation

For what it’s worth, slides from my presentation yesterday… As ever, they’re largely pointless without commentary…

… and even with the commentary, it was all a bit more garbled than usual (I forgot to breathe, had no real idea in my own mind what I wanted to say, etc etc…)

On reflection, here’s what I took from thinking back about what I should have tried to say:

– my assumption is that folk who are interested in asking data related questions should feel as if they can actually work with the data itself (direct data manipulation); I appreciate this is already way off the mark for some people who want someone else to work the data and then just read reports about it – but then that means you can’t ask or discover your own questions about the data, just read answers (maybe) to questions that someone else has asked, presented in a way they decided;

– you need to feel confident in working with data files – or at least, you need to be prepared to have a go at working with data files! (Bear in mind that many of the blog posts I write are write ups – of a sort – of how to do something I didn’t know how to do a couple of hours before… The web usually has answers to most of the questions that I come up against – and if I can’t find the answers, I can often request them via things like Twitter or Stack Overflow…) This can range from using command line tools, to using applications that let you take data in using one format and getting it out as another);

– different tools do different things; if you can get a dataset into a tool in the right way, it may be able to do magical things very very easily indeed…

– three tools that can do a lot without you having to know a lot (though you may have to follow a tutorial or two to pick up the method/recipe….or at least recognise a picture you like and a dataset whose shape you can replicate using your own data, and then the ability to see which bits you need to cut and paste into the command line…):

-=- Gephi: great for plotting networks and graphs. It can also be appropriated to draw line charts (if you can work out how to ‘join the dots’ in the data file by turning the line into a set of points connected by edges) or scatter plots (just load in nodes – no edges connecting them – and lay it out using Gephi’s geolayout tool which also lets you plot “rectilinear” plots based on x and y axis values; (I haven’t worked out a reliable way of working with CSV in Gephi – yet…); it’s amazing what you can describe as a graph when you put your mind to it…

-=- gnuplot: command line tool for plotting scatter plots and line graphs (eg from time series) using data stored in simple text file (e.g. TSV or CSV)

-=- R (and ggplot if you’re feeling adventurous and want :pretty”, nicely designed graphs out); another command line tool (I find R-Studio helps) that again loads in data from a CSV file; R can generate statistical graphs very easily from the command line (it does the stats calculations for you given the raw data).

– Visual analytics/graphical data analysis is a process – you tease out questions and answers through directly manipulating the data and engaging with it in a visual way;

– when you see a visualisation you like, look at it closely: what do you see? Spending five mins or so looking at a Gestalt psychology/visual perception tutorial will give you all sorts of tricks and tips for how to construct visualisations so that structure your eye can detect will jump out at you;

– I think I may have confused folk talking about “dimensions”: what I meant what, how many columns could you represent in a given visulisation at the same time, if each data point corresponds to a single row in a data set. So for example, if you have an x-y plot (2 dimensions), with different symbols (1 dimension) available for plotting the points, as well as different colours (1 dimension) and different possible size (1 dimension) for each symbol, along with a label (1 dimension) for each point, and maybe control over the size (1 dimension), colour (1 dimension) and even font (1 dimension) applied to the label, you might find you can actually plot quite a few columns/dimensions for each data point on your chart… Whether or not you can actually decipher it is another matter of course! My Gephi charts generally have 2 explicit dimensions (node size and colour), as well as making use of two spatial dimensions (x, y) to lay out points that are in some sense “close” to each other in network space. It’s worth remembering though, that if you’re using a tool to engage in a conversation with a dataset as you try to get it to tell its story to you, it may not matter that the visualisation looks a mess to anyone else (a bit like an involved conversation may not make sense if someone else suddenly tries to join it). (Presentation graphics, on the other hand, are usually designed to communicate something that the data is trying to say to another person in a very explicit way.)

– working with data is a tactile thing… you have to be prepared to get your hands dirty…