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Data transformation is crucial for extracting useful information from "summary" of R for Data Science by Hadley Wickham,Garrett Grolemund
Data transformation plays a critical role in the process of extracting valuable insights from raw data. In its original form, data may be messy, incomplete, or in a format that is not conducive to analysis. By transforming the data, we can clean it up, organize it, and prepare it for further analysis. This is essential because the quality of the insights we can derive from data is directly tied to the quality of the data itself. One common task in data transformation is cleaning the data. This involves dealing with missing values, outliers, and errors in the data. By identifying and addressing these issues, we can ensure that our analysis is based on accurate and reliable information. For example, if there are missing values in a dataset, we may need to decide whether to impute them, remove them, ...
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