Compute a complete correlation matrix with original labels
Select physical column numbers, declare missing-data policy, and inspect every coefficient, effective N, status, raw record and fully labelled heatmap.
Choose physical columns and retain raw labels
The choice 1,2,3,1 creates four axis positions, with the last referencing the first physical column again. Matching Age_55_64 headers can also belong to different physical columns; sourceColumnIndex and selectionOrdinal distinguish them.
- Axis labels receive no filename prefix.
- Duplicate or empty headers remain unchanged; no-header input receives column_n labels.
Declare the valid-row set
Selected cells are trimmed. Empty cells and declared markers are missing; other invalid text refuses the whole task. Text in unselected columns remains unchanged.
| Choice | Valid set and ranks |
|---|---|
| Pairwise | Rows valid for both variables; filter first, then rank that subset. |
| Listwise | Rows valid for all selected physical columns; each pair uses the same set. |
Use the verified finite example
The page fixture has 6 data rows, duplicate headers and a repeated choice. For selections 1 and 2, Pearson pairwise N is 4, as in listwise mode. Selections 1 and 3 instead have pairwise N of 5.
Age_55_64,GDP,Age_55_64,Constant,Few,Note
1,10,7,5,NA,alpha
2,20,7,5,NA,beta
2,NA,8,5,NA,"quoted, note"
4,40,8,5,NA,γ
NA,50,9,5,NA,missing
6,60,10,5,12,tail
Hand off the complete result
matrix.csv and effective-n.csv contain every axis and cell. pair-status.json includes complete statuses, selection identities, raw records, validity masks, policy and actual budget measurements. heatmap.svg contains full raw labels and cell titles.
- Keep original.csv, settings.json and LICENSE.jstat.txt.
- The finite view does not replace complete downloads.
Recover after a refusal
All limits form joint budgets; maximum rows and selections are not a simultaneous capacity promise. The complete task, from reading the file to displaying its result, has a 30-second deadline. Failure, cancellation or timeout clears the prior result and returns no partial matrix. Reduce the task, then rerun with the original file and settings.
References
- Original question
The seven saved complete public posts were read. The author resolved the label issue in R. No complete original numeric CSV is available; browser preference and market size are unknown.
- jStat 1.9.6 core and MIT license
Average tie ranks and correlation semantics are retained with scoped changes for bounds, actual comparison counting and stable arithmetic. The complete MIT license is included.
Tools in this category
Expand a tool to see its steps, options and supported formats, then open its workspace.
Column correlation matrixCompute Pearson or Spearman correlations from a complete CSV, preserving original labels, repeated selections, each pair’s effective N and status.
Select economic, social or other numeric variables by their physical column numbers. Preserve raw labels and repeated choices, compute every variable pair, and use N and status to explain unavailable cells. Pairwise and listwise deletion can produce different valid-row sets.
Steps
- Choose the complete UTF-8 file and check its delimiter and header setting.
- Enter physical column numbers, choose Pearson or Spearman, and declare deletion policy and exact missing markers.
- Inspect coefficients, N and statuses in the finite preview, then download the full matrix, effective N, status JSON, SVG, original, settings and license.
Available options
- Numeric column numbers
- 1,2
Comma-separated physical column numbers starting at 1, such as 1,2,3,1. Repeated choices retain distinct selection ordinals.
- Correlation method
- Pearson · Spearman
Spearman computes average tie ranks separately within each pair-valid subset.
- Missing-data policy
- Pairwise deletion · Listwise deletion
Pairwise uses rows valid for that pair; listwise uses rows valid for every selected physical column.
- Input delimiter
- Comma · Semicolon · Tab · Pipe
- Header row
- First row is a header · No header
Duplicate or empty headers remain unchanged; no-header input receives labels such as column_1.
- Missing-value markers
- Enter as needed
One per line, case-sensitive after trimming. Empty cells are always missing.
Capabilities and limits
- One nonempty UTF-8 CSV/TSV/TXT file: at most 20 MiB, 50,000 data rows and 2,000,000 cells including a header when present. Rows must have equal width; UTF-8 BOM, quotes and quoted line breaks are supported.
- Select 1–64 ordinals, including repeats. Selected nonmissing cells must be finite binary64 numbers in decimal or scientific notation. Other text, infinity and source underflow refuse the whole task. Original numeric strings and unselected columns remain complete.
- Up to 20 missing markers, 100 characters each and 2,048 characters overall. Fewer than two valid rows or constant columns produce a null coefficient and explicit status, as do detected arithmetic underflow, lost distinctions or nonfinite intermediates. Only an excess beyond ±1 within 32 Number.EPSILON is corrected and recorded.
- Joint budgets: at most 100,000,000 pair-row work units and 20,000,000 actual sort comparisons; files plus full text 32 MiB, compact typed JSON 16 MiB, their aggregate 48 MiB, binary plus metadata transport 64 MiB and conservative estimated memory 512 MiB. Axis ceilings do not promise simultaneous maximum capacity.
- A 30-second absolute deadline covers native file validation, reading, Worker loading, calculation, full-result validation, cleanup and first UI publication after await. Failure or cancellation publishes no partial result. The view shows at most 10×10 cells and 120 characters per label; downloads retain every cell and full label.