In the previous chapter, we covered how to integrate data from various data sources. However, simply collecting data is not enough; you also have to ensure the quality of the collected data. If the quality of data used is insufficient, the results of the analysis may be misleading due to biased samples or missing values. Moreover, if the collected data is not well structured and shaped, you may find it hard to correlate and investigate the data. Therefore, data preprocessing and preparation is an essential task that you must perform prior to data analysis.
Those of you familiar with how SQL operates may already understand how to use databases to process data. For example, SQL allows users to add new records with the insert operation, modify data with the update operation, and remove records with the delete operation. However, we do not need to move collected data back to the database; R already provides more powerful and convenient preprocessing functions and packages. In this...
