Get started with mintyr
In mintyr: Grouped and Nested Data Pipelines Built on 'data.table'

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

mintyr turns "many groups x many variables" data into analysis-ready pieces and back into files. A typical analysis follows one loop:

 files --> import --> reshape & nest --> cross-validate / summarise --> export --> files

Each step has its own article:

| Step | Functions | Article | |---|---|---| | Import and export | import_xlsx(), import_csv(), export_xlsx(), export_nest(), export_list() | vignette("import-and-export") | | Reshape and nest | w2l_nest(), w2l_split(), c2p_nest(), r2p_nest() | vignette("reshape-and-nest") | | Cross-validation | split_cv(), nest_cv() | vignette("cross-validation") | | Descriptive statistics | desc_stats(), top_perc(), format_digits() | vignette("descriptive-statistics") | | Utilities | get_path_info(), mintyr_example() | vignette("utilities") |

library(mintyr)
library(data.table)

The loop in five steps

1. Import. Several workbooks become one table; excel_name and sheet_name record where every row came from.

files <- mintyr_example(mintyr_examples("xlsx_test"))
raw <- import_xlsx(files)
head(raw)

2. Describe. A report table per group, with a total row.

desc_stats(mtcars, cols = c("mpg", "hp", "wt"), by = "cyl",
           fmt = "{mean} ± {sd}", total = TRUE, shape = "wide")

3. Reshape and nest. One row per trait and group, the data in a list-column.

nested <- w2l_nest(mtcars, cols = c("mpg", "qsec"), by = "am")
nested

4. Cross-validate inside every piece. Reproducible 4-fold CV; a model per fold, then the mean predictive ability per trait and group.

cv <- nest_cv(nested, v = 4, seed = 2026)
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)]

5. Export. One file per trait and group, e.g. as input for HIBLUP or DMU.

out <- file.path(tempdir(), "by_trait")
files <- export_nest(nested, path = out)
basename(dirname(files))
unlink(out, recursive = TRUE)

Design principles

No side effects: input objects are never modified by reference.
No silent data loss: file names are sanitised, and functions stop instead of overwriting files or returning ambiguous results.
Consistent arguments: data, cols, by, out_type, path mean the same thing in every function.
Light dependencies: data.table, readxl, writexl and base R.