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This video is about learning how to clean and reorganize messy data to make it more tidy using the tidyr package. And weamp;#39;ve already learned about what tidy data looks like and how tidy data is easier to analyze once we bring it into the computer. And remember that three of those principles of tidy data are that we have one column per type of information, one row per observation, and we only have one value in each cell. Unfortunately thereamp;#39;s lots of existing data out there that doesnamp;#39;t follow these and other tidy data rules and in those cases we need to know how to convert those data into a more tidy form in order to analyze them. And we can do a lot of this work in our using a package called tidyr spelled tidy r. So letamp;#39;s go ahead and that package so that we have it to work with so weamp;#39;ll do amp;#39;.packages(amp;#39;tidyramp;#39;). And while thatamp;#39;s installing weamp;#39;ll go ahead and create our library cal
