The simplerspec package aims to facilitate spectral and additional data handling and model development for spectroscopy applications such as infrared soil spectroscopy. Different helper functions are designed to create a data and modeling workflow. Data inputs and outputs are stored in common S3 `R` objects (`lists` and `data frames`), using in addition [`data.table`](https://rdatatable.gitlab.io/data.table/) and [`tibble`](https://tibble.tidyverse.org/index.html) extensions. The functions are built to work in a pipeline and cover commonly used procedures for spectral model development and application.
The newest version of the package is available on this GitHub repository. If you find bugs you are highly welcome to report issues (write me an [email](mailto:info@spectral-cockpit.space) or create an [issue](https://github.com/philipp-baumann/simplerspec/issues)). You can install {simplerspec} from GitHub or directly from the r-universe.
1. [`read_opus_univ()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/read-opus-universal.R): Read spectra and metadata from Bruker OPUS binary files into R list
2. [`gather_spc()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/gather-spc.R): Gather spectra and metadata from list into a tibble object (list-columns)
4. [`resample_spc()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/resample-spc.R): Resample spectra to new wavenumber intervals
2. [`average_spc()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/average-spc.R): Average spectra for replicate scans
5. [`preprocess_spc()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/preprocess-spc.R): Perform pre-processing of spectra
6. [`select_spc_vars()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/select-spc.R): Select every `n`-th spectral variable and corresponding X-unit values.
7. [`join_spc_chem()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/join-chem-spectra.R): Join chemical and spectral data sets by `sample_id`
8. [`plot_spc_ext()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/plot-spc-extended.R): Extended spectral plotting; e.g. group spectra using different
9. [`fit_pls()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/pls-modeling.R): Perform model tuning and evaluation based on Partial Least Squares (PLS) regression
10. [`select_ref_spc()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/select-ref-spectra.R): Select a set of reference samples to measured by
11. [`predict_from_spc()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/predict-spc.R): Predict multiple chemical properties from a list of calibrated models and new soil spectra
12. [`assess_multimodels()`](https://github.com/philipp-baumann/simplerspec/blob/master/R/utils-stats.R): Assess model performance given multiple pairs of predicted and measured variables.
5. Provide an integrated data-model framework that features tidy data structures designed for both user-friendly printing and efficient data processing.
* [`prospectr`](https://cran.r-project.org/web/packages/prospectr/index.html): Various utilities for pre-processing and sample selection based on spectroscopic data. An introduction to the package with examples can be found [here](https://l-ramirez-lopez.r-universe.dev/articles/prospectr/prospectr.html).
*`plyr` and [`dplyr`](https://dplyr.tidyverse.org): Fast data manipulation tools with an unified interface.
*`ggplot2 `: Alternative plotting system for R, based on the grammar of graphics. See [here](https://ggplot2.tidyverse.org).
*`caret `: Classification and regression training. A set of functions that attempt to streamline the process for creating predictive models. See [here](https://topepo.github.io/caret/) for details.
Consistent and reproducible data and metadata management is an important prerequisite for spectral model development. Therefore, simplerspec functions are based on storing spectral data and related data in R data structures which keep related data in rows. Every row representing an observation contains data related to a single spectral measurement. Simplerspec functions uses tibble data frames as principal data structures because they allow to store lists within the well-known data frame structures. Lists are flexible data structures and can e.g. contain other lists, vectors, data.frames, or matrices.
List-columns features provided within the tibble framework are an excellent base to work with functional programming tools in R, which allows to efficiently write code.
Bruker FTIR spectrometers produce binary files in the OPUS format that can contain different types of spectra and many parameters such as instrument type and settings that were used at the time of data acquisition and internal processing (e.g. Fourier transform operations). Basically, the entire set of setup measurement parameters, selected spectra, supplementary metadata such as the time of measurement are written into OPUS binary files. In contrast to simple text files that contain only plain text with a defined character encoding, binary files can contain any type of data represented as sequences of bytes (a single byte is sequence of 8 bits and 1 bit either represents 0 or 1).
Simplerspec comes with reader function `read_opus_univ()` that is intended to be a universal Bruker OPUS file reader that extracts spectra and key metadata from files. Usually, one is mostly interested to extract the final absorbance spectra (shown as *AB* in the OPUS viewer software).
* Antoine Stevens and Leonardo Ramirez-Lopez for their contributions to the [prospectr package](https://l-ramirez-lopez.r-universe.dev/prospectr) and the
