15Differential Treatment of Your Data


Chapter Learning Objectives

  1. Isolating data: Creating new data files
  2. Isolating data: Segregating output and analyses

Occasionally, you'll want to separate “interesting” data from data that you, for whatever reason, find a bit less interesting. This chapter, therefore, focuses on manipulation requests that you can use to segregate your data.

Isolating Interesting Cases

Consider the following imaginary situation: A train leaves San Francisco for Pittsburgh at 8:00, carrying 150 women, 100 men, and traveling at 100 miles per hour. Another train leaves Pittsburgh for San Francisco at 9:30 (also going really fast). What's the likelihood of the Pittsburgh conductor getting a date from one of the 150 San Francisco women after the two trains collide?

OK, that's a stupid question – we don't even know if the conductor survived the crash – but it raises a point: to answer the question about the likelihood of a date, we'd probably want to isolate information about the 150 San Francisco women from information about other passengers. We don't really care about information from other people in this situation.

Taking this fictional scenario as a case study for the chapter, we will demonstrate how to isolate female San Franciscans from the larger dataset. There are ...

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