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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 --> filesEach 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)
#>
#> Attaching package: 'data.table'
#> The following object is masked from 'package:base':
#>
#> %notin%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)
#> excel_name sheet_name col1 col2 col3
#> <char> <char> <num> <char> <lgcl>
#> 1: xlsx_test1 Sheet1 4 d FALSE
#> 2: xlsx_test1 Sheet1 5 f TRUE
#> 3: xlsx_test1 Sheet1 6 e TRUE
#> 4: xlsx_test1 Sheet2 1 a TRUE
#> 5: xlsx_test1 Sheet2 2 b FALSE
#> 6: xlsx_test1 Sheet2 3 c TRUE2. 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")
#> cyl mpg hp wt
#> <char> <char> <char> <char>
#> 1: 4 26.66 ± 4.51 82.64 ± 20.93 2.29 ± 0.57
#> 2: 6 19.74 ± 1.45 122.29 ± 24.26 3.12 ± 0.36
#> 3: 8 15.10 ± 2.56 209.21 ± 50.98 4.00 ± 0.76
#> 4: Total 20.09 ± 6.03 146.69 ± 68.56 3.22 ± 0.983. 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
#> name am data
#> <char> <num> <list>
#> 1: mpg 1 <data.table[13x9]>
#> 2: mpg 0 <data.table[19x9]>
#> 3: qsec 1 <data.table[13x9]>
#> 4: qsec 0 <data.table[19x9]>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)]
#> name am mean_r
#> <char> <num> <num>
#> 1: mpg 1 0.816
#> 2: mpg 0 0.881
#> 3: qsec 1 0.884
#> 4: qsec 0 0.8585. 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)
#> [ export_nest ] Auto-selected nested columns: data
#> [ export_nest ] Auto-selected grouping columns: name, am
#> [ export_nest ] Export complete. 4 file(s) written to: C:\Users\tony2\AppData\Local\Temp\Rtmpsxilk2/by_trait
basename(dirname(files))
#> [1] "1" "0" "1" "0"
unlink(out, recursive = TRUE)data,
cols, by, out_type,
path mean the same thing in every function.data.table,
readxl, writexl and base R.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.
Health stats visible at Monitor.