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For this blog post I decided to build a data visualization for the data given to us for our final project. This data is information about artists and artworks that have been acquired by the Museum of Modern Art (MoMA) since its start in 1929. I created this visualization with Tableau and used the dimension “gender” and measure “number of records.” I then chose to present the data in the form of “packed bubbles” as I think it reveals a lot about the selected data.<\/p>\n
The first revelation is that a lot of data cleaning will need to be done. For example, this visualization shows that some pieces are labeled “()(Male)” while others are labeled “(Male)()” which are actually the same category. There are many other situations like this which means that many categories will need to be merged with software such as OpenRefine. Also if you click on the circles you find that many pieces of data have things like “()” and “()()()” which both indicate that their is no information available. The visualization above also has a large circle with no label which also indicates that no information was found about gender for those pieces of data. As a result, if my group decides that gender is something we want to look into, then we know we can exclude this data.<\/p>\n
Another thing this visualization shows is the fact that a lot of these artworks were worked on by multiple people. This was hard to see when just looking at the data table given that the row was so narrow that it only revealed the first gender. As a result, if my group decides to work with gender, we’ll need to decide whether we want to look at pieces of artwork that were worked on by just\u00a0a single person or if we want to include pieces that were worked on by multiple people.<\/p>\n","protected":false},"excerpt":{"rendered":"