The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

tidymatrix 0.1.0

Major Features

Analysis Integration

• Added comprehensive support for analytical methods with automatic metadata integration
• PCA: compute_prcomp() performs PCA, adds scores to metadata and stores the full prcomp object (use store = FALSE to only add the scores)
• Clustering:
  • compute_hclust(): Hierarchical clustering with dendrogram storage
  • compute_kmeans(): K-means clustering
• Other embeddings: compute_mds(), compute_tsne() (Rtsne) and compute_umap() (umap)
• Analysis results are added as columns to metadata with clear naming:
  • PCA: {name}_PC1, {name}_PC2, etc.
  • Clustering: {name}_cluster

Analysis Management

• get_analysis(): Retrieve stored analysis objects (prcomp, hclust, kmeans)
• list_analyses(): List all stored analyses
• remove_analysis(): Remove specific or all stored analyses
• check_analyses(): Check validity of stored analyses

Analysis Invalidation

• Stored analysis objects are automatically removed when data is modified: filter(), slice(), arrange(), joins, summarize()/count()/tally(), t() and all matrix transformations
• Metadata columns (PC scores, cluster assignments) are preserved even when analysis objects are removed
• Clear warnings indicate which analyses were removed during data modification

Core Features (from initial development)

Data Structure

• tidymatrix(): Create tidymatrix objects combining matrix data with row and column metadata
• activate(): Switch context between rows, columns, or matrix
• Automatic validation of matrix-metadata alignment

dplyr Integration

• filter(): Filter rows or columns with corresponding matrix subsetting
• select(): Select metadata columns
• mutate(): Add or modify metadata columns
• arrange(): Reorder rows or columns
• slice(), slice_head(), slice_tail(), slice_sample(): Subset by position
• rename(): Rename metadata columns
• pull(): Extract metadata columns as vectors
• relocate(): Reorder metadata columns

Grouping and Aggregation

• group_by(): Group by metadata variables (creates grouped_tidymatrix)
• summarize(): Aggregate grouped data with matrix aggregation
  • Default mean() for numeric matrices
  • Required .matrix_fn parameter for non-numeric matrices
  • Type-aware error messages with helpful suggestions
• count(): Count observations by group with matrix aggregation
• tally(): Count within existing groups
• ungroup(): Remove grouping

Design Principles

1. Tidy principle: Same data type in, same data type out
2. Explicit naming: Clear, prefixed column names prevent conflicts
3. Smart defaults: Sensible defaults with explicit requirements where needed
4. Helpful errors: Type-aware error messages guide users
5. Analysis objects: Store full R objects (prcomp, hclust) for later use
6. Metadata persistence: Metadata columns persist even when analysis objects are invalidated

Row- and column-wise statistics

• compute_across() applies any function to every row or column, with access to the metadata of the other dimension
• Wrappers: compute_ttest(), compute_wilcox(), compute_anova(), compute_kruskal(), compute_correlation(), compute_lm(), compute_lm_simple()
• In two-group tests, groups that are not given explicitly are inferred in a deterministic order (factor levels, otherwise sorted values) and a message reports the comparison. Missing group labels are ignored.

Matrix operations

• transform_matrix(), scale(), center(), clip_values(), log_transform(), t() and add_stats()
• Extra arguments to transform_matrix() are evaluated with the metadata of the other dimension as data mask, so values can be transformed depending on their metadata (e.g. reverse-scoring questionnaire items)

Joins

• All six dplyr joins on rows or columns. The matrix keeps its storage type and row/column names; rows or columns that only exist in the joined table are filled with NA

Data and documentation

• New example datasets: a simulated Big Five personality survey (big5_responses, big5_respondents, big5_items) and a country table (big5_countries)
• Vignettes rewritten around the survey data: a getting-started tour plus vignettes on dplyr verbs, joins, matrix operations, PCA and clustering, other embeddings, row-wise statistics, and plotting and exporting

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.