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Loading, Tidying, and Cleaning Data in the tidyverse
Cleaning data is a crucial step in the data science process. It involves identifying and correcting errors, inconsistencies, and missing values in the data, as well as formatting and structuring the data in a way that makes it easy to work with. This allows the data to be used effectively for analysis, modeling, and visualization. The R tidyverse is a collection of packages designed for data science and includes tools for data manipulation, visualization, and modeling. The dplyr and tidyr packages are two of the most widely used packages within the tidyverse for data cleaning. dplyr provides a set of functions for efficiently manipulating large datasets, such as filtering, grouping, and ...
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