Blog Post #3

This week, we learned about Tidy Data Sets. When it comes to Tidy Data Sets, there are three principles: each variable forms a column, each observation forms a row, and each type of observational unit forms a table.

In order to organize my research, there are a few ways that Wickham suggests. The first one is to melt them, or stack them. To do this, we would need to turn columns into rows. This also means that a list of columns are already variables or colvars. Another organizational method we can use is to record spaced observations over time. Wickham uses the example of organizing variables such as artist, track, date entered, rank and week. Another way of tidying data is melting and then splitting columns into two different variables. The third way to tidy datasets is to melt with colvars.

When I go to do research, I will be able to implement tidy data and organizational research methods. One of which, is to melt my data. I can do this if I am doing research on death certificates, for example, and I want to melt the dates that historical figures died. If I want to record spaced observations over time, I can do this if I were to record observations on how environmental factors affected how long people lived in a certain time period, when they died, and the different environmental factors. Another way I can incorporate these research methods is if I split columns into two different variables like dates historical figures died and how they died.

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