  Indexes are useful for summarizing multivariate information into single
metrics for monitoring, communicating, and decision-making. While most work has
focused on defining new indexes for specific purposes, more attention needs to
be directed towards making it possible to understand index behavior in
different data conditions, and to determine how their structure affects their
values and variation in values. Here we discuss a modular data pipeline
recommendation to assemble indexes. It is universally applicable to index
computation and allows investigation of index behavior as part of the
development procedure. One can compute indexes with different parameter
choices, adjust steps in the index definition by adding, removing, and swapping
them to experiment with various index designs, calculate uncertainty measures,
and assess indexes robustness. The paper presents three examples to illustrate
the pipeline framework usage: comparison of two different indexes designed to
monitor the spatio-temporal distribution of drought in Queensland, Australia;
the effect of dimension reduction choices on the Global Gender Gap Index (GGGI)
on countries ranking; and how to calculate bootstrap confidence intervals for
the Standardized Precipitation Index (SPI). The methods are supported by a new
R package, called tidyindex.
