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Hi, Im Sharon Machlis, Director of Editorial Data amp;amp; Analytics at IDG Communications. Im here with Episode 12 of Do More With R: Reshaping data with the tidyr package. Its Murphys Law of Data: The data you have isnt always in the format that you need. And not all problems have to do with mistakes or gaps in the data. Sometimes youve got wide data that needs to be long; or long data that needs to be wide. Lets work on an example. Ill read in a spreadsheet of home prices in 5 U.S. metro areas: Boston, Detroit, Philadelphia, San Francisco, and San Jose (which Im calling Silicon Valley). More specifically, data about home prices every 2 years, when all cities started with an index of 100 in 1995. This data runs from 2000 to 2018. Heres a look at the spreadsheet: And heres what it looks like when I import it with the rio package. This is a pretty human-friendly format. Its sometimes referred to as a wide format. Each metro area has its own column, and you can scan down