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Category: TidyTuesday

Posts in the tidy tuesday blog

  • Water Insecurity in the U.S.

    Water Insecurity in the U.S.

    Week 4 of 2025 of the TidyTuesday project provided a dataset on water insecurity in the different counties of the United States.

    This data was featured in the article “Mapping water insecurity in R with tidycensus“. The visual I came up with for this week’s blog is almost identical to one of the visuals featured in the article. I only discovered this afterwards, which illustrates that it’s a natural reflex to fall back on standard maps when visualizing geographical data.

    The visual shows the different counties in the United States. The darker the colour of the county, the more households without access to sanitation (relative to the total population of the county). Note that there are some counties with high numbers of households without access to sanitation in northern Arizona.
    The 5 counties with the highest percentage of households without access to sanitation are Apache (Arizona), McKinley (New Mexico, Fairbanks North Star (Alaska), Navajo (Arizona) and Coconino (Arizona).

    Feel free to hover over the different counties to see more detail. You can also toggle between the 2022 and 2023 data at the top of the visualisation.

    I used Power BI for this visualisation. Initially, like the previous two blog posts, I wanted to create this in Vega-Lite, but I soon found out that visualizing a county-based map of the U.S. was easier in the former. It also allowed me to refresh the use of tooltips in Power BI.

    I would like to point out that quite a few counties are not represented in this data. I left these missing data in white so as not to ‘overload’ the visual. The disadvantage of this is that it is not immediately obvious which data is missing and which is not.

    For mor information about this data or the TidyTuesday project in general, visit the official Github repo.

  • Himalayan Mountaineering

    Himalayan Mountaineering

    Week 3 of 2025 of the TidyTuesday project provided a dataset on mountaineering in the Himalayas.

    Within this data, I looked at the various missions undertaken by mountaineers between May 2020 and June 2024.
    In the timeline below, it is immediately apparent that there is some form of seasonality in the timing of the missions. While most missions were successful, bad weather and poor conditions were the main reasons for early termination.
    Feel free to explore this data a little further yourself. You can filter for a specific time period in the timeline at the bottom, or use the bar graph at the top right to filter for a specific reason why a mission was terminated. If you hover over the dots, you find more detail about the mission.

    Embedding Vega-Lite

    For mor information about this data or the TidyTuesday project in general, visit the official Github repo.