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mintyr

mintyr turns “many groups × many variables” data into analysis-ready pieces — and back into files — with data.table speed and no surprises.

A lot of real-world analysis follows the same loop: take a wide table, split it by trait and group, fit something on every piece (often with cross-validation), then write each piece or result to its own file or sheet. mintyr packages that loop into a small set of consistent functions:

 files ──► import ──► reshape & nest ──► cross-validate / summarise ──► export ──► files
          (xlsx/csv)   (by trait × group)      (per piece)          (txt/csv/xlsx)

It was born in animal breeding, where this loop is everywhere (multi-trait, multi-breed, multi-farm data feeding ASReml-R, HIBLUP or DMU), but nothing in it is specific to genetics.

Who is it for?

Field	Typical use
Animal & plant breeding	Per-trait / per-breed models, genetic correlations across traits or environments, input files for HIBLUP or DMU
Multi-site / multi-batch experiments	The same analysis for every site × variable, with results written back per site
Grouped machine learning	Reproducible (stratified) k-fold CV run separately within every subgroup
Reporting	Read dozens of Excel workbooks, clean them in one table, write them back to the original file/sheet layout
Any grouped analysis	Pairwise correlations between many variables, top/bottom-N% summaries per group

Installation

# From CRAN
install.packages("mintyr")

# Development version from GitHub
pak::pak("tony2015116/mintyr")

Quick start

Fit a model for every trait within every group, using 4-fold cross-validation, and export each subset to its own folder:

library(mintyr)
library(data.table)

# 1. Wide -> long, nested by trait (`name`) and group (`am`)
nested <- w2l_nest(mtcars, cols = c("mpg", "qsec"), by = "am")
nested
#>    name am               data
#> 1:  mpg  1 <data.table[13x9]>
#> 2:  mpg  0 <data.table[19x9]>
#> 3: qsec  1 <data.table[13x9]>
#> 4: qsec  0 <data.table[19x9]>

# 2. Reproducible 4-fold CV inside every trait x group
cv <- nest_cv(nested, v = 4, seed = 2026)

# 3. Predictive ability per fold, then averaged
cv[, r := mapply(function(tr, va) {
  fit <- lm(value ~ wt + hp, data = tr)
  cor(predict(fit, va), va$value)
}, train, validate)]
cv[, .(mean_r = round(mean(r), 3)), by = .(name, am)]
#>    name am mean_r
#> 1:  mpg  1  0.816
#> 2:  mpg  0  0.881
#> 3: qsec  1  0.884
#> 4: qsec  0  0.858

# 4. One file per trait x group: <path>/<name>/<am>/data.txt
export_nest(nested, path = file.path(tempdir(), "by_trait"))

What’s inside

Import and export — a lossless round trip

Function	What it does
`import_xlsx()`	Read many workbooks and sheets into one table, with `excel_name` / `sheet_name` source columns; optional parallel reading (`workers`)
`import_csv()`	Read many CSV/TXT files with `fread()`, with a source-file column
`export_xlsx()`	Write a table back to the original file/sheet layout, or a list of tables into one workbook or one file each
`export_nest()`	Write every nested table to `<path>/<group1>/<group2>/<column>.txt`
`export_list()`	Write every element of a named list to its own file (names may contain `/` for sub-folders)

Reshape and nest

Function	What it does
`w2l_nest()` / `w2l_split()`	Wide → long, then nest (list-column) or split (named list) by variable and group
`c2p_nest()`	All pairs (or triples, …) of variables, renamed to `value1`, `value2`, … for uniform pairwise analysis
`r2p_nest()`	Same variable across levels of another column (e.g. one column per environment), aligned by an ID

Cross-validation and summaries

Function	What it does
`split_cv()` / `nest_cv()`	Repeated, optionally stratified k-fold CV for a list of tables or a nested table; returns fold indices and (optionally) the subsets
`top_perc()`	Statistics of the top / bottom X% per variable and group
`format_digits()`	Format numeric columns for reports (decimals, percentages)
`get_path_info()`	Extract file names or path segments, cross-platform

Examples by use case

Pairwise correlations between many variables, per group

pairs <- c2p_nest(mtcars, cols = c("mpg", "hp", "wt"), by = "am")
pairs[, .(r = sapply(data, function(d) cor(d$value1, d$value2))), by = .(pairs, am)]

The same trait in different environments (e.g. genotype × environment)

# growth: one row per animal x farm
r2p_nest(growth, names_from = "farm", cols = c("adg", "backfat"), id = "animal")
# -> per trait: animal | farm1 | farm2, ready for a cross-environment correlation

Clean many Excel files and put them back where they came from

dt <- import_xlsx(list.files("raw", pattern = "\\.xlsx$", full.names = TRUE))
dt <- dt[!is.na(weight)]                  # any data.table processing
export_xlsx(dt, path = "cleaned")         # same file names, same sheet names

Input files for command-line breeding software

export_nest(nested, path = "hiblup_runs", na = "NA")       # HIBLUP
export_nest(nested, path = "dmu_runs", na = "-9999",       # DMU: numeric missing code,
            col.names = FALSE)                             #      no header

Design principles

No side effects. Your input objects are never modified by reference.
No silent data loss. File names are sanitised; if two outputs would land on the same path, mintyr stops instead of overwriting. r2p_nest() refuses ambiguous IDs instead of returning counts.
Consistent arguments. data is the input, cols are the columns to work on, by are grouping columns, out_type chooses data.table ("dt") or data.frame ("df"), path is where files go.
Light dependencies. data.table, readxl, writexl and base R; cross-validation is implemented in the package itself.
Reproducible. seed makes CV folds reproducible without disturbing your global random-number stream.

Upgrading from 0.1.x

Version 0.2.0 unifies argument names (cols2l, cols2bind, trait, nest_dt, group_cols, export_path, rbind, … → cols, data, by, path, combine, …); the old names are no longer accepted. Cross-validation no longer depends on rsample: the splits column is replaced by train_idx / validate_idx. See NEWS for the complete old → new table.

Cheat sheet

Acknowledgments

AI assistants helped turn the initial ideas behind mintyr into code, refine its structure and write its documentation.

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.