Data Slicer simply slices numeric data (provided that they are integer values) into any given number of quantiles (to be chosen in the form). For example, if one has a database compiling information about individuals including their age, it may be useful to transform this field in bins of various significant ages. In turn, it will allow to contrast a given behavior with people under 20 y.old, or 45, etc. Keeping the original ages would end up in statistical uncertainties if the number of individuals is not sufficient.

Two slicing strategies are available:

- regular slicing generates quantiles which values are evenly distributed from the minimum to the maximum value,
- homogeneous slices produce quantiles gathering roughly the same number of items.

Alternatively, one can simply type Â personnalized intervals. For instance to produce 5 bins disitnguishing between very negative, negative, neutral, positive and very positive sentiment polarity, you would enter: [-10:-4];[-3:-1];[0];[1:3];[4:10]