Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. Once you’ve imported your data, it is a good idea to tidy it. If you can’t get your data into R, you can’t do data science on it! This typically means that you take data stored in a file, database, or web application programming interface (API), and load it into a data frame in R.
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