Assessment of the performance and up-to-date diagnostics of scientific equipment is one of the key components in contemporary laboratories. Most reliable checks are performed by real test experiments while varying the experimental conditions (typically, in the case of infrared spectroscopic measurements, the size of the beam aperture, the duration of the experiment, the spectral range, the scanner velocity, etc.). On the other hand, the stability of the instrument response in time is another key element of the great value. Source stability (or easy predictable temporal changes, similar to those observed in the case of synchrotron radiation-based sources working in non top-up mode), detector stability (especially in the case of liquid nitrogen- or liquid helium-cooled detectors) should be monitored. In these cases, recorded datasets (spectra) include additional variables such as time stamp when a particular spectrum was recorded (in the case of time trial experiments). A favorable approach in evaluating these data is building hyperspectral object that consist of all spectra and all additional parameters at which these spectra were recorded. Taking into account that these datasets could be considerably large in size, there is a need for the tools for semiautomatic data evaluation and information extraction. A comprehensive R archive network--the open-source R Environment--with its flexibility and growing potential, fits these requirements nicely. In this paper, examples of practical implementation of methods available in R for real-life Fourier transform infrared (FTIR) spectroscopic data problems are presented. However, this approach could easily be adopted to many various laboratory scenarios with other spectroscopic techniques.
