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Working with factors and strings is common in data analysis from "summary" of R for Data Science by Hadley Wickham,Garrett Grolemund
Factors and strings are two important data types that are commonly used in data analysis. Factors are used to represent categorical data in R. They are a type of vector that can only contain a specific set of values, known as levels. Factors are useful for representing data that has a fixed number of categories, such as gender or educational level. When working with factors in R, it is important to understand how they are stored and manipulated. Factors are actually stored as integers, with each level corresponding to a specific integer value. This can sometimes lead to unexpected results if factors are not handled correctly. One common task when working with factors is to change the levels or order of the levels. This can be done using the factor() function in R. By changing the levels of a factor, you can control how the data is displayed and analyzed. Strings, on the other hand, are used to represent text data in R. Strings are a type of vector that can contain any sequence of characters, such as words, sentences, or numbers. Strings are commonly used when working with textual data, such as names or addresses. When working with strings in R, it is important to be mindful of how they are treated by different functions and operators. For example, some functions may not work as expected if they are used on string data. One common task when working with strings is to manipulate or extract specific parts of the text. This can be done using functions such as substr() or strsplit(). By manipulating strings, you can clean and process text data for analysis. In summary, factors and strings are essential data types in R that are commonly used in data analysis. By understanding how to work with factors and strings, you can effectively handle categorical and text data in your analyses.
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