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    <lastmod>2023-09-01</lastmod>
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  </url>
  <url>
    <loc>https://www.tanyashapiro.com/peloton-review</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2022-12-02</lastmod>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/839ddefd-1313-4306-b3dd-f1a08a0eddf1/logo-big.png</image:loc>
      <image:title>Peloton Analysis</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/2ed28739-8fcb-4c27-9efc-cc9db2b79144/peloton-instructors.jpeg</image:loc>
      <image:title>Peloton Analysis - Toughest Instructors</image:title>
      <image:caption>My goal for this visual was to recreate the style and feel of Peloton’s user leaderboard to explore toughest instructors by fitness discipline. The leaderboard uses an average of all difficulty ratings per class (an average score based on user feedback). The rankings use archived classes from January 2019 to September 2022. I added in supplementary information about the instructors, i.e. hometown, from Google search.</image:caption>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/99e52fd0-8bae-4084-9fa4-5fc8e96d1537/peloton-bump-chart.png</image:loc>
      <image:title>Peloton Analysis - Do I Have a Favorite Instructor?</image:title>
      <image:caption>The animated racing bar chart was a cool way to visualize most time spent per instructor over time. I wanted to also produce a static visual that could convey the same message - who was I spending the most amount of time with and did my preferences change over time? I used ggplot and ggbump to analyze my instructor rankings over time. While Olivia Amato remains a staple in my Peloton routine, it’s hard not to notice Kendall rising to the top as one of my leading favorites.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/566ecdc1-2887-4b88-9e5a-f8a17ebcf38e/peloton-avg-output.jpeg</image:loc>
      <image:title>Peloton Analysis - Pedal To The Metal!</image:title>
      <image:caption>Inspired by Cédric Scherer's lollipop-jitter plot, I wanted to visualize my Peloton cycling metrics by instructor - am I pushing myself harder with a particular trainer? I used point shapes to indicate different class types. Important to note that class type can influence output: for instance, "Low Impact" classes call for less resistance (yielding less output) and "Intervals &amp; Arms" classes have deliberate cycling breaks to focus on arm weight sections. Plot created with R ggplot and ggimage.</image:caption>
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      <image:title>Peloton Analysis - Active Days Calendar</image:title>
      <image:caption>The Peloton App keeps users highly engaged with visuals like the active days calendar - which displays the last 30 days and highlights each day at least one class was taken. At the end of 2021, I was inspired to do a review of my own workouts and put my own spin on the active days calendar. Using R ggplot, I created a visual showing the entire calendar year, and used different colors to denote the type or types of workouts taken on each active day.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/a9f0ac8f-81a7-4d85-ab18-41aa93eb42d0/peloton-gt.jpg</image:loc>
      <image:title>Peloton Analysis - Peloton Instructor Summary</image:title>
      <image:caption>For my end of 2021 workout analysis, I was also curious to see a breakdown by instructors: who did I spend the most time with? who was the toughest? what type of classes did we do together? To answer these questions, I knew a table would be best to show all the relevant pieces of information. Using R and gT tables and gtExtra, I made the following visual. Not surprised to see Olivia Amato at the top - she kills me on the bike and I love her core classes!</image:caption>
    </image:image>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/7cb0aaa4-2435-4406-aa8f-703d5aa1a8fb/peloton-bar-race.gif</image:loc>
      <image:title>Peloton Analysis - On Your Mark, Get Set - Go!</image:title>
      <image:caption>My partner noticed a moving bar chart video, or what’s also known as a “racing bar chart”, on TikTok. She asked me if it was possible to do something using R. I was up for the challenge! Fortunately, with R packages like gganimate, this task was easy. I used my Peloton workout history data to show cumulative hours per instructor by month. Visualization exported in GIF format.</image:caption>
    </image:image>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/e06e144f-ee2c-46ed-b9ab-16c449e79594/peloton-gif.gif</image:loc>
      <image:title>Peloton Analysis - Developing My Shiny App</image:title>
      <image:caption>As my obsession with analyzing Peloton data evolved, I wanted to take things to the next level. I had more questions that couldn’t be satisfied by one graphic or table alone. I constantly wanted to change the date ranges and filter to see my data by different class types. To satiate my curiosity, I set forth on building my own application using R Shiny. The Shiny application has different inputs that interact with reactable summarizing workouts and metrics by instructors.</image:caption>
    </image:image>
  </url>
  <url>
    <loc>https://www.tanyashapiro.com/30-day-map</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2022-12-02</lastmod>
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      <image:title>30 Day Map Challenge</image:title>
      <image:caption />
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      <image:title>30 Day Map Challenge</image:title>
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      <image:title>30 Day Map Challenge</image:title>
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      <image:title>30 Day Map Challenge</image:title>
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      <image:title>30 Day Map Challenge</image:title>
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      <image:title>30 Day Map Challenge</image:title>
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      <image:title>30 Day Map Challenge</image:title>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/49dcdb59-3f9d-4178-8202-8810947fbf82/basel_map_dark_light.png</image:loc>
      <image:title>30 Day Map Challenge</image:title>
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    <image:image>
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      <image:title>30 Day Map Challenge</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/5f7efb5a-b064-425d-9fee-79048ad085c8/nyc_urban.jpeg</image:loc>
      <image:title>30 Day Map Challenge</image:title>
    </image:image>
    <image:image>
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      <image:title>30 Day Map Challenge</image:title>
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      <image:title>30 Day Map Challenge</image:title>
    </image:image>
    <image:image>
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      <image:title>30 Day Map Challenge</image:title>
    </image:image>
    <image:image>
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      <image:title>30 Day Map Challenge</image:title>
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    <loc>https://www.tanyashapiro.com/tidytuesday</loc>
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      <image:title>TidyTuesday</image:title>
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      <image:title>TidyTuesday</image:title>
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      <image:title>TidyTuesday</image:title>
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      <image:title>TidyTuesday</image:title>
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      <image:title>TidyTuesday</image:title>
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      <image:title>TidyTuesday</image:title>
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      <image:title>TidyTuesday</image:title>
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  <url>
    <loc>https://www.tanyashapiro.com/rupauls-drag-queens</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2022-11-17</lastmod>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/4dad39c2-bcd3-4b73-9f75-bd3656f71b9f/rpdr-schema.jpg</image:loc>
      <image:title>Drag Queens - Scraping &amp; Pre-Processing</image:title>
      <image:caption>The majority of data for this project was collected using an R web scraping library, rvest (equivalent of Python’s Beautiful Soup). Data was scraped from Wikipedia &amp; RuPaul’s FanWiki. Data collected was then normalized and reshaped using additional R libraries (namely tidyverse). Data encompasses RuPaul’s Drag Race, All Stars, and all International franchises (e.g. Canda’s Drag Race). For seasons still in progress or not yet aired, there may be incongruencies in table information - e.g. season contestants are present, but episode outcomes are not.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/37a0f851-4f30-47f2-9cd9-77504315e6c6/drag_netwerk.png</image:loc>
      <image:title>Drag Queens - International Drag NetWerk</image:title>
      <image:caption>I wanted to explore the relationships among contestants across different international franchises and seasons. The analysis includes contestants from region specific franchises - UK, Canada, Holland, España, Italia, and France - and includes cross-overs from RuPaul’s global “Drag vs. The World” spin-offs, e.g. Canada’s Drag Race vs. The World. Since España, Italia, and France are relatively new compared to other international shows, it’s not surprising to see no overlap of contests with the Global franchise (yet). The network graph was produced using R ggplot and GGally.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/ee738c17-c2ed-4ab8-abf1-de1a3b2a8e97/drag-map.gif</image:loc>
      <image:title>Drag Queens - Where Are You Queen?</image:title>
      <image:caption>Where are the queens originally from? I used tidygeocoder to produce the geo coordinates for each hometown, and used leafletR (Leaflet wrapper) to generate the map. It's pretty cool to see how Ru's family footprint has expanded around the world over the past decade. You can find a queen in almost every continent! The interactive version available here.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/1656613937861-6T35LW8EAL2SHZ338X0T/contestant-performance.png</image:loc>
      <image:title>Drag Queens - Drag Queens - But Make It Sports!</image:title>
      <image:caption>I’ve seen a number of beautiful data table visualizations about athletes and related sports statistics (especially NFL). I wanted to do something similar for RuPaul’s Drag Race contestants (all franchises and seasons). I used reactablefmtr to create the table using season contestant information and htmlwidgets to export the table in HTML format. Table shows contestants, their respective rank, and placement outcome % out of all challenges. The interactive version is available here.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/914f5874-64f0-403f-80d4-5ae6cf2e6804/song-herstory.png</image:loc>
      <image:title>Drag Queens</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/75027ed8-c472-4f9e-90d4-2cb1fb0334b3/lip_sync_all_stars2.png</image:loc>
      <image:title>Drag Queens - Background</image:title>
      <image:caption>Each episode of Drag Race is known for ending with an epic lip sync dual. At the end of an episode, select contestants will face off in a lip sync battle. These contestants either represent the bottom or the top of the group depending on the show format. The traditional show format calls for the bottom two contestants to perform: the winner gets to continue on to the next round, while the other losing contestant “sashays away” (eliminated). In some lip syncs, there is a tie outcome - either both contestants win, or both contestants are eliminated. Audio Features After collecting the list of lip sync songs per season and episode, I used Spotify’s API to run a search on each song using song title and artist and return Spotify’s track ID along with a list of the song’s audio features. These audio features are assigned by Spotify and are typically scaled from 0 to 1. These features include - “danceability”, “speechiness”, “energy”, "and “valence.” To explore these Spotify features, I created a radar plot of all lip sync songs on RuPaul’s All Stars Season 2. Winning contestant is depicted in the center of each plot. Plot rendered using R ggplot and ggplot extension packages (e.g. ggimage, ggtext, and geom_textpath).</image:caption>
    </image:image>
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    <loc>https://www.tanyashapiro.com/test</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2022-08-05</lastmod>
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    <loc>https://www.tanyashapiro.com/drag-queens-copy</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2022-12-19</lastmod>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/4dad39c2-bcd3-4b73-9f75-bd3656f71b9f/rpdr-schema.jpg</image:loc>
      <image:title>Drag Queens (Copy) - Scraping &amp; Pre-Processing</image:title>
      <image:caption>The majority of data for this project was collected using an R web scraping library, rvest (equivalent of Python’s Beautiful Soup). Data was scraped from Wikipedia &amp; RuPaul’s FanWiki. Data collected was then normalized and reshaped using additional R libraries (namely tidyverse). Data encompasses RuPaul’s Drag Race, All Stars, and all International franchises (e.g. Canda’s Drag Race). For seasons still in progress or not yet aired, there may be incongruencies in table information - e.g. season contestants are present, but episode outcomes are not.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/1656613937861-6T35LW8EAL2SHZ338X0T/contestant-performance.png</image:loc>
      <image:title>Drag Queens (Copy) - Drag Queens - But Make It Sports!</image:title>
      <image:caption>I’ve seen a number of beautiful data table visualizations about athletes and related sports statistics (especially NFL). I wanted to do something similar for RuPaul’s Drag Race contestants (all franchises and seasons). I used reactablefmtr to create the table using season contestant information and htmlwidgets to export the table in HTML format. Table shows contestants, their respective rank, and placement outcome % out of all challenges. The interactive version is available here.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/ee738c17-c2ed-4ab8-abf1-de1a3b2a8e97/drag-map.gif</image:loc>
      <image:title>Drag Queens (Copy) - Where Are You Queen?</image:title>
      <image:caption>Where are the queens originally from? I used tidygeocoder to produce the geo coordinates for each hometown, and used leafletR (Leaflet wrapper) to generate the map. It's pretty cool to see how Ru's family footprint has expanded around the world over the past decade. You can find a queen in almost every continent! The interactive version available here.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/37a0f851-4f30-47f2-9cd9-77504315e6c6/drag_netwerk.png</image:loc>
      <image:title>Drag Queens (Copy) - International Drag NetWerk</image:title>
      <image:caption>I wanted to explore the relationships among contestants across different international franchises and seasons. The analysis includes contestants from region specific franchises - UK, Canada, Holland, España, Italia, and France - and includes cross-overs from RuPaul’s global “Drag vs. The World” spin-offs, e.g. Canada’s Drag Race vs. The World. Since España, Italia, and France are relatively new compared to other international shows, it’s not surprising to see no overlap of contests with the Global franchise (yet). The network graph was produced using R ggplot and GGally.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/914f5874-64f0-403f-80d4-5ae6cf2e6804/song-herstory.png</image:loc>
      <image:title>Drag Queens (Copy)</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/6022b7409a0cdd54b1bc0c5f/75027ed8-c472-4f9e-90d4-2cb1fb0334b3/lip_sync_all_stars2.png</image:loc>
      <image:title>Drag Queens (Copy) - Background</image:title>
      <image:caption>Each episode of Drag Race is known for ending with an epic lip sync dual. At the end of an episode, select contestants will face off in a lip sync battle. These contestants either represent the bottom or the top of the group depending on the show format. The traditional show format calls for the bottom two contestants to perform: the winner gets to continue on to the next round, while the other losing contestant “sashays away” (eliminated). In some lip syncs, there is a tie outcome - either both contestants win, or both contestants are eliminated. Audio Features After collecting the list of lip sync songs per season and episode, I used Spotify’s API to run a search on each song using song title and artist and return Spotify’s track ID along with a list of the song’s audio features. These audio features are assigned by Spotify and are typically scaled from 0 to 1. These features include - “danceability”, “speechiness”, “energy”, "and “valence.” To explore these Spotify features, I created a radar plot of all lip sync songs on RuPaul’s All Stars Season 2. Winning contestant is depicted in the center of each plot. Plot rendered using R ggplot and ggplot extension packages (e.g. ggimage, ggtext, and geom_textpath).</image:caption>
    </image:image>
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    <loc>https://www.tanyashapiro.com/about</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2023-02-24</lastmod>
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    <loc>https://www.tanyashapiro.com/new-page</loc>
    <changefreq>daily</changefreq>
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    <lastmod>2023-02-24</lastmod>
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    <changefreq>daily</changefreq>
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    <lastmod>2023-02-24</lastmod>
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      <image:title>Services 1</image:title>
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    <loc>https://www.tanyashapiro.com/talks-1</loc>
    <changefreq>daily</changefreq>
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    <lastmod>2023-02-24</lastmod>
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    <lastmod>2023-02-25</lastmod>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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    <image:image>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
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    <image:image>
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      <image:title>GALERY</image:title>
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      <image:title>GALERY</image:title>
      <image:caption>Slice of Staten Island</image:caption>
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    <image:image>
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