Efficient data communication with colleagues is crucial in research. Although many statisticians and data scientists prefer standard text files or software specific data, like RData files, Microsoft Excel remains as a to-go software to create and store data for many researchers. MS Excel has many well-known detrimental effects, but it also has some perks that can be exploited, for example, hiding columns, imposing restrictions on columns, and creating hyperlinks. On the other hand, you can use a shiny app for a similar goal with more freedom via a shiny app if your colleagues have R installed.
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The fish species of Grass Carp (Ctenopharyngodon idella), Silver Carp (Hypophthalmichthys molitrix), and Bighead Carp (H. nobilis) are categorized as invasive carp in North America. There is interest from a natural resource management perspective to monitor the populations and spread of the fish species. A common monitoring practice is to collect and genetically identify fish eggs, but this process is both costly in money and time. Camacho et al. (2019) demonstrated the use of machine learning as a possibility for a more efficient method of identifying invasive carp.
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Clustering is one of the principal tools used by data analysts for uncovering the structure present within a data set. Hierarchical clustering is particularly popular since it can reveal multiple scales of groupings at once without forcing the data analyst to commit to a certain number of clusterings. However, hierarchical clustering’s usefulness as a visualization tool is severely degraded by increasing data set sizes. We present an interactive tool that overcomes this difficulty, making hierarchical clustering useful for exploring data sets at scales of interest.
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