For my initial data, I knew there was definitely a Taylor Swift fanpage that would have statistics on outfit variations. I stumbled across the one shown here, from @teddysversion on X, that featured pie charts of outfits, shoes, etc. This is open and free data, which allows anyone to access it. However, this data still needed to be interpreted numerically for creation of my data visualization. I put outfit variations from the beginning of the tour to November 2nd into my own Google Spreadsheet, calculating percentages and numbers that would benefit me. I did try to include a piece of each era, but I did not want to have too much information either. Also, during the tour, she did add different sets or whole new variations, which would not make sense to incorporate due to how late they were added into the tour. For some, the second version (my Google Spreadsheet) may be complicated or confusing, as a lot of the outfits are given different names by fans or different acronyms. Although this was a little more complicated than expected, it was a lot better than pulling this data myself without initial data to fall back on. I separated the outfits into category by era, and listed said outfits. I chose one outfit per era, as it would give me easier and less complex results and not clutter my visualization. Luckily, some of the eras only have 1-2 variations, which made data interpretation easier. Once I gathered my data on Google Spreadsheets, it made inputting into RawGraphs much easier. When it came to what I used for my final from the data, I chose the top 3 variations with the highest amount of wear.