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Read the statistical result tables from a JASP, jamovi or notebook file

Source: R/stat-tables.R
read_stat_tables.Rd

Opens a .jasp, .omv, .spv or .ipynb file and returns every result table as a tidy data frame together with the analysis it belongs to. No statistics knowledge is applied here — this is the lossless extraction layer; semantic typing happens downstream in stat_output_json() / stat_results_long().

Usage

read_stat_tables(path)

Arguments

path

path to a .jasp, .omv, .spv or .ipynb file

Value

a list with one element per result table, each a list of analysis (the nearest heading above the table), title (the table's own caption / first heading, when distinguishable), data (a data.frame of the table as rendered, header row as column names), and table_index (1-based ordinal position of this table among all tables in the rendered document — there is no source line to attach, since a JASP/jamovi analysis is GUI-produced). Empty list when the archive has no index.html or no tables.

Details

Four sources are tried, in order of fidelity:

  1. .jasp — the archive's own analyses.json, which holds the structured results;

  2. .omv — jamovi's protobuf-serialised AnalysisResponse blobs, decoded natively (see inst/schema/jamovi/PROVENANCE.md);

  3. .ipynb — the outputs saved inside each code cell of a Jupyter notebook (dispatched on extension; it is JSON, not a zip archive);

  4. any archive — the rendered index.html, as a fallback when the above are absent or unreadable.

The structured sources are preferred because they carry each column's machine name even when its displayed header is blank (the HTML then yields an unnamed column), store values at full precision (a p of 0.000632 rather than the rendered "< .001"), and mark missing cells explicitly instead of as a rendered dash.

Examples

jasp <- system.file("extdata", "sample.jasp", package = "metacheck")
tables <- read_stat_tables(jasp)
length(tables)
#> [1] 2
tables[[1]]$data
#>                      v N               mean                sd                se
#> 1  Illusion_score_sync 9        1.925925926 0.862454149896004 0.287484716632001
#> 2   control_score_sync 9 -0.148148148111111  1.19153392169221 0.397177973897402
#> 3 Illusion_score_async 9  0.703703703888889 0.963852865109736 0.321284288369912