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Data manipulation is an integral part of data cleaning and analysis. For large sets of data, it is always preferable to perform the operation within subgroups of a dataset to speed up the process. In R, this type of data manipulation can be done with base functionality, but for large-scale data it requires a considerable amount of coding and eventually takes more processing time. In the case of large-scale data, we can split the dataset, perform the manipulation or analysis, and then combine it into a single output again.

In statistical modeling, the behavior of a numeric variable and categorical variable is different, so it is important to store the data correctly to ensure valid statistical analysis. In R, a factor variable stores distinct numeric values internally and uses another character set to display the contents of that variable. In other software, such as Stata, the internal numeric values are known as values and the character set is known as value labels. Previously, we saw that the mode of a factor variable is numeric; this is due to the internal values of the factor variable.

This chapter starts with the concept of split-apply-combine and is followed by the different functions and utilities of the plyr package. The split-apply-combine strategy Often, we require similar types of operations in different subgroups of a dataset, such as group-wise summarization, standardization, and statistical modeling. org/v40/i01/paper). To understand the split-apply-combine strategy intuitively, we could compare this with the map-reduce strategy for processing large amounts of data, recently popularized by Google.

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