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