I wanted to see how the funding profile of different funders varied, using the index of multiple deprivation as a reference point. To do this, I used lava lamp plots to compare the distribution of grants. These are essentially violin plots, created in R using ggplot. If the plot has a fatter bottom, then the funder gives more grants to recipients in more deprived areas. If the plot has a fatter head, then the funder gives more grants to recipients in less deprived areas. A broadly rectangular shape indicates even distribution of grants across all deprivation vigintiles. To calculate this, I downloaded a dump of all data from GrantNav (~250,000 grants), and filtered out all grants for whom the recipient postcode field was empty. This left me with around 63,000 grants. I then removed any invalid postcodes (non-England, partial, etc) which left me with 53,000 grants, and then mapped these firstly to lower layer super output area, and then to deprivation data. This dataset then allowed me to draw the plots. I genuinely feel this is a novel and useful way to look at data like this - you immediately get a sense of the overall picture of grant funding, through the lens of deprivation, and allows people to ask questions of, for example, Lloyd's Register Foundation. I think there's more work to be done here, and I'd love to have looked into it further (indeed I might carry on). There are a few issues that I need to resolve: * The shapes look at number of grants, not amount * This particular visualisation could probably do with a bit more design * My horrorshow code needs sorting out * I'm not sure whether recipient is the best measure to use. Beneficiary would probably tell a more compelling story but the data just isn't there in the same detail There's some other stuff that's out of my hands - the fact that almost 200,000 grants were missing postcode. Imagine if we could do this for all grantmakers, and include all grants. Thanks for looking, and sorry it's so rushed!