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tplyr2

{tplyr2} is a grammar of clinical summary tables. Clinical reports – demographics tables, adverse event summaries, lab shift tables – all share a common structural pattern. Each section is some kind of summary: a set of counts, a block of descriptive statistics, or a cross-tabulation. Rather than writing bespoke data manipulation code for every table, {tplyr2} lets you describe what the table should contain using a declarative specification, and handles the computing, formatting, and assembly for you.

{tplyr2} is a ground-up rewrite of {Tplyr}, built on data.table for performance. The spec-based API separates configuration from data, making specs portable, reusable, and serializable to JSON/YAML.

Installation

You can install the development version of {tplyr2} from GitHub:

# install.packages("devtools")
devtools::install_github("atorus-research/tplyr2")

Example

Every table starts with a tplyr_spec() that declares the column structure and layers. Data is supplied at build time with tplyr_build().

library(tplyr2)

spec <- tplyr_spec(
  cols = "TRT01P",
  layers = tplyr_layers(
    group_count("SEX", by = "Sex n (%)"),
    group_desc(
      "AGE",
      by = "Age (Years)",
      settings = layer_settings(
        format_strings = list(
          "n"         = f_str("xxx", "n"),
          "Mean (SD)" = f_str("xx.x (xx.xx)", "mean", "sd"),
          "Median"    = f_str("xx.x", "median"),
          "Min, Max"  = f_str("xx, xx", "min", "max")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adsl)
knitr::kable(result[, !grepl("^ord", names(result))])

Key Concepts

• Spec: A tplyr_spec() object is pure configuration – no data, no side effects. It describes the column variable, filters, population data, and layers.
• Layers: Each layer is a summary block created with group_count(), group_desc(), group_shift(), or group_analyze().
• Build: tplyr_build(spec, data) executes the spec against a dataset and returns a formatted data frame.
• Format strings: f_str() declarations control numeric precision and alignment (e.g., f_str("xx.x (xx.xx)", "mean", "sd")).

Layer Types

Count Layers

Tabulate frequencies of categorical variables, with support for nested counts, distinct subject counting, and total rows.

spec <- tplyr_spec(
  cols = "TRTA",
  layers = tplyr_layers(
    group_count(
      c("AEBODSYS", "AEDECOD"),
      settings = layer_settings(
        distinct_by = "USUBJID",
        format_strings = list(
          n_counts = f_str("xxx (xx.x%)", "distinct_n", "distinct_pct")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adae)
knitr::kable(head(result[, !grepl("^ord", names(result))], 10))

Descriptive Statistics Layers

Summarize continuous variables with built-in statistics (n, mean, sd, median, min, max, q1, q3, var, iqr, missing) or custom summary functions.

Shift Layers

Cross-tabulate a baseline value against a post-baseline value within each treatment arm using group_shift().

Analyze Layers

Run user-defined analysis functions with group_analyze() for full flexibility.

Features

• Population data: Control denominators with pop_data() and display (N=n) header counts with tplyr_header_n()
• Total & custom groups: Add “Total” columns or combine treatment arms with total_group() and custom_group()
• Auto-precision: Dynamically set decimal places based on collected precision with the precision cap system
• Risk difference: Compute risk differences and confidence intervals on count layers
• Post-processing: Row masks, row label collapsing, conditional formatting, and text wrapping helpers
• Metadata: Cell-level traceability to trace any result back to its source records
• Numeric data: Access raw unformatted results via tplyr_numeric_data()
• Serialization: Save and load specs as JSON or YAML with tplyr_write_spec() / tplyr_read_spec()
• ARD: Convert to and from Analysis Results Data format with tplyr_to_ard() / tplyr_from_ard()

Learning More

Getting started

• vignette("tplyr2") – Getting started
• vignette("table") – Spec-level table properties

Layers

• vignette("count") – Count layers in depth
• vignette("desc") – Descriptive statistics layers
• vignette("shift") – Shift layers
• vignette("analyze") – Custom analyze layers
• vignette("adverse-events") – A full adverse event table, end to end

Formatting

• vignette("format_strings") – The format string system
• vignette("precision_alignment") – Data-driven precision and parenthesis hugging
• vignette("display_conventions") – <1%, zero-count suppression, statistics as columns

Table customization

• vignette("denom") – Denominators, population data, header N
• vignette("sort") – Ordering rows and layers
• vignette("options") – Session options
• vignette("post_processing") – Post-processing helpers

Statistics and traceability

• vignette("riskdiff") – Risk difference columns
• vignette("binding-statistics") – Association tests and binding external model results
• vignette("metadata") – Cell-level metadata and traceability
• vignette("serialization") – Saving and loading specs
• vignette("ard") – Analysis Results Data conversion
• vignette("migration") – Moving from Tplyr v1

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.
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