Chapter 3 Tidying Data

In the functionality provided by itraxR, the need for data cleaning is much reduced. However, you may still encounter poor quality data that needs removing from subsequent analysis. This can be broadly defined as:

• Data at the start and end of the the core, where a volume of core material is “missing”.
• Measurements where the optical configuration is out of position (marked as validity == 0), often due to holes or stones in the core.
• Areas of the core with low total counts.
• Individual measurements that are statistical outliers.

The easiest way to do this is using a tidyverse style sequence of pipes that set the observations of faulty data as NA.