{"id":6,"date":"2025-11-03T02:42:38","date_gmt":"2025-11-03T02:42:38","guid":{"rendered":"https:\/\/midterm.guana.sites.carleton.edu\/blog\/?page_id=6"},"modified":"2025-11-03T02:42:38","modified_gmt":"2025-11-03T02:42:38","slug":"visualizing-artistic-media-in-the-tate-collection","status":"publish","type":"page","link":"https:\/\/midterm.guana.sites.carleton.edu\/blog\/visualizing-artistic-media-in-the-tate-collection\/","title":{"rendered":"Visualizing Artistic Media in the Tate Collection"},"content":{"rendered":"\n
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Introduction<\/strong><\/h2>\n\n\n\n

This digital project analyzes artworks in the Tate collection and works jointly held with the National Galleries of Scotland. The goal of this project is to turn a large, messy spreadsheet into clear, accessible information. I have created two visualizations that show the distribution of media across the collection and the cumulative number of works by medium up to each decade.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Sources\u00a0<\/strong><\/h2>\n\n\n\n

This project analyzes the ARTIST ROOMS dataset, which contains metadata for roughly 70,000 artworks owned by Tate or co-owned with the National Galleries of Scotland. I focus on investigating the collection\u2019s medium composition and how the medium varies with acquisition year. Our analysis admits a selection bias because I exclude the missing entries from either of the columns.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Processes<\/strong><\/h2>\n\n\n\n

I downloaded the dataset from the provided Github link. Since the dataset is quite large, I relied on Excel and Python for their built-in tools that make data cleaning and organization easier. I created two visualizations, and each needed a different cleaning procedures. Therefore, this section is divided into two parts, with one part explaining the steps for each visualization. <\/p>\n\n\n\n

For the pie chart, I started with 69,200 rows and 3,413 distinct medium labels. I calculated the count of each medium, exported those counts to a CSV, and found that the top 12 media account for about 70% of the entire collection. I then created an Excel sheet with a collapsed version of the data that keeps only the 12 largest categories (grouping everything else as \u201cOther\u201d) to derive the pie chart.<\/p>\n\n\n\n

For the stacked area chart, I first dropped rows with missing values in the \u201cyear created\u201d field. This left 37,145 rows, and I limited the analysis to the top five mediums. I computed the number of works acquired in each year for those media and wrote the results to a spreadsheet. To reduce the noise, I aggregated counts by decade and then calculated cumulative sums to show how the totals build over time.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Presentation\u00a0<\/strong><\/h2>\n<\/div>\n<\/div>\n\n\n\n