Tabulated content is omnipresent in scientific literature. This work presents the R package *tableParser*, designed to extract and postprocess tables from NISO-JATS-encoded XML, HTML, DOCX, and, with limitations, PDF documents. *tableParser* focuses on extracting and analyzing statistical test results reported in scientific publications. It can be used for large-scale analysis of effect sizes, reporting practices, or summarization of results, as well as for checking completeness and consistency of standard test results in unpublished documents. Documents can be processed in three decoding levels. *table2matrix()* compiles all tables into a list of character matrices with captions and footnotes. *table2text()* collapses the matrix contents into human-readable text, mimicking a screen reader. Optionally, many common codings that are reported within the table's caption and footnote can be used to decode and expand the table's content. The collapsed and decoded table content can be further processed match an ideal input for the extraction of statistical standard results with the *standardStats()* function from the *JATSdecoder* package. The output of *table2stats()* is a data frame with all detected standard results as columns and, if calculation is possible, a recalculated p-value. If desired, an automated consistency check of the reported and the coded p-values with the recalculated p-value can be initiated. *tableParser* works best on barrier-free HTML tables encoded in NISO-JATS, where captions and footnotes are clearly identifiable. By guessing the tables captions and footnotes conservatively, the processing of tables within HTML and DOCX documents is comparably robust. Technically, tables in PDFs often fail to be correctly extracted, with captions and footnotes not detectable. Therefore, a decoding of codes is not possible, which lowers *tableParser*'s decoding accuracy on PDFs.

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