When you work with data in R, you will often encounter information stored as text, also known as strings. Strings are essential for representing names, addresses, codes, and other textual data. In data analysis, handling text is crucial because real-world datasets frequently contain important information in string form. For example, you might need to combine first and last names for a mailing list, extract a product code from an inventory file, or clean up inconsistent formatting in user-entered data. Mastering string manipulation allows you to prepare, clean, and analyze text data efficiently, making your analyses more accurate and insightful.

In the code above, you use the paste() function to join two strings: first_name and last_name. The sep argument specifies what character to place between the two strings—here, a space (" "). paste() is useful when you want to merge columns of text, create readable labels, or format output for reports. By changing the sep argument, you can control how the strings are combined, such as using a comma, dash, or no separator at all.

This code demonstrates how to extract a substring from a longer string using the substr() function. The start and stop arguments define the position of the substring you want to extract. In this example, substr(phone_number, start = 1, stop = 3) pulls out the first three characters, which represent the area code. Substrings are useful for tasks like pulling out codes, abbreviations, or other components from larger text fields.

In real-world datasets, string manipulation can be challenging. You may find inconsistent capitalization, extra spaces, varied delimiters, or misspelled words. Addressing these issues is a critical step in cleaning and preparing your data for analysis, ensuring that your results are reliable and meaningful.