minPanel

1 Introduction

This vignette of package minPanel (Github) presents …

1.1 Prerequisite

Package groupedHyperframe requires R version 4.5.0 (released 2025-04-11) or higher (macOS, Windows, Linux).

Sys.info()[c('sysname', 'release', 'machine')]
#  sysname  release  machine 
# "Darwin" "25.1.0"  "arm64"
R.version
#                _                           
# platform       aarch64-apple-darwin20      
# arch           aarch64                     
# os             darwin20                    
# system         aarch64, darwin20           
# status                                     
# major          4                           
# minor          5.1                         
# year           2025                        
# month          06                          
# day            13                          
# svn rev        88306                       
# language       R                           
# version.string R version 4.5.1 (2025-06-13)
# nickname       Great Square Root

Experimental (and maybe unstable) features are released extremely frequently to Github.

remotes::install_github('tingtingzhan/minPanel')

1.2 Getting Started

Examples in this vignette require that the search path has

library(minPanel)
library(ggplot2)
library(flextable)

2 Data Set

Study data should be described in a peer-reviewed manuscript in the following language.

<Biology> samples from n_1 <positive.subjects> were obtained from our collaborators <institutionA-1>, <institutionA-2> under approved IRB protocols. <Biology> samples from n_0 <negative.subjects> from <institutionB-1>, <institutionB-2> were used as controls. Demographic information of human subjects is shown in <table> and <supplementary materials>.

Data from Zezulinski et al. (2025) is included in package minPanel.

Data object m1 is a logical matrix of 12877 variants in 50 early & late stage, i.e., positive, patients.

m1 |> 
  dim()
# [1] 12877    50

m1 |> 
  head(n = c(3L, 3L))
#                     UPH-106P UPH-004P UPH-083P
# chr17:7176997 C>T      FALSE    FALSE     TRUE
# chr11:118583356 G>A    FALSE    FALSE    FALSE
# chr12:49269877 C>T     FALSE    FALSE     TRUE

Data object m0 is a logical matrix of the same 12877 variants in 31 UPC, i.e., negative, patients.

m0 |> 
  dim()
# [1] 12877    31

m0 |> 
  head(n = c(3L, 3L))
#                     UPC-022P UPC-021P UPC-044P
# chr17:7176997 C>T      FALSE    FALSE    FALSE
# chr11:118583356 G>A    FALSE    FALSE    FALSE
# chr12:49269877 C>T     FALSE    FALSE    FALSE

3 Panel of Variant-Collections

Function panel() identifies the variants that identifies the same set of positive subjects in m1, and the same set of negative subjects in m0. These variants are considered identical in terms of m1 and m0. Therefore they are put in one variant-collection. We use “collection” for short in this vignette.

m = panel(m1 = m1, m0 = m0)

m
# Panel of 1253 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

In other words, the 12877 variants in both m1 and m0 are consolidated into 1253 collections. For example, the variants below identify the same set of positive and negative subjects, thus they are consolidated as one collection.

m@id[[8L]] |>
  cat(sep = '\n')
# chr1:29053290 C>A
# chr1:29053291 A>G
# chr1:29053294 G>C
# chr1:29053295 C>T

4 Selected Ordered Panel

Methodology should be described in a peer-reviewed manuscript in the following language.

A minimal panel of variants-collections is determined using the following steps: (1) we exclude the collections that identify none of the <positive.subjects> per collection; (2) we exclude the collections that identify at least 1 of the <negative.subjects> per collection; (3) we sort the remaining collections by the number of <positive.subjects> identified per collection, from most to least; (4) we exclude the collections that identify no more <positive.subjects> than all the preceding collections. Such panel of collections is ordered, i.e., a researcher may reduce the size of the panel by keeping the collections in the top of the panel and dropping those in the bottom of the panel. <Figure> is the Consolidated Standards of Reporting Trials (CONSORT) diagram of this selection procedure. This methodology is implemented in R (R Core Team (2025)) package minPanel (citation coming soon).

x0 = m |>
  subset(subset = true_positive > 0L) |>
  subset(subset = false_positive <= 0L, append.label = TRUE) |>
  sort_by(y = ~ true_positive, decreasing = TRUE) |>
  subset(subset = diff(cum_true_positive) > 0L) |>
  append_label(info = 'cum_false_positive')

The pipeline above consists of five steps.

1. The first call to subset() (Appendix Section 6.2.2) selects the collections that identify at least one positive subjects per collection (Appendix Section 6.2.1).
2. The second call to subset() selects the collections that identify none of the negative subjects per collection (Appendix Section 6.2.1). The per-collection criterion 'False(+) ≤0/31' is appended to the @label of the output.
3. The call to sort_by() (Appendix Section 6.2.3) sorts the collections by their number of positive subjects identified per collection (Appendix Section 6.2.1), in decreasing order.
4. The last call to subset() identifies the collections, so that each one collection identifies at least one more positive subjects than all the preceding collections (Appendix Section 6.2.1).
5. The call to append_label() (Appendix Section 6.2.4) summarizes the number of negative subjects identified as a panel of collections (Appendix Section 6.2.1). The panel-wise criterion 'panelFalse(+) ≤0/31' is appended to the @label of the output.

x0
# False(+) ≤0/31
# panelFalse(+) ≤0/31
# Panel of 23 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

Function consort.panel() (Appendix Section 6.2.5) presents a Consolidated Standards of Reporting Trials (CONSORT) diagram of the creation of a selected panel. Figure 1 shows the creation of x0.

x0 |>
  consort.panel()

Figure 1: CONSORT Diagram of x0

Similarly, we relax the constraint on false_positive-per-collection and create

x1 = m |>
  subset(subset = true_positive > 0L) |>
  subset(subset = false_positive <= 1L, append.label = TRUE) |>
  sort_by(y = ~ true_positive, decreasing = TRUE) |>
  subset(subset = diff(cum_true_positive) > 0L) |>
  append_label(info = 'cum_false_positive')

x1
# False(+) ≤1/31
# panelFalse(+) ≤7/31
# Panel of 18 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

x2 = m |>
  subset(subset = true_positive > 0L) |>
  subset(subset = false_positive <= 2L, append.label = TRUE) |>
  sort_by(y = ~ true_positive, decreasing = TRUE) |>
  subset(subset = diff(cum_true_positive) > 0L) |>
  append_label(info = 'cum_false_positive')
x2
# False(+) ≤2/31
# panelFalse(+) ≤13/31
# Panel of 17 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

x2
# False(+) ≤2/31
# panelFalse(+) ≤13/31
# Panel of 17 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

x3 = m |>
  subset(subset = true_positive > 0L) |>
  subset(subset = false_positive <= 3L, append.label = TRUE) |>
  sort_by(y = ~ true_positive, decreasing = TRUE) |>
  subset(subset = diff(cum_true_positive) > 0L) |>
  append_label(info = 'cum_false_positive')
x3
# False(+) ≤3/31
# panelFalse(+) ≤12/31
# Panel of 16 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

x3
# False(+) ≤3/31
# panelFalse(+) ≤12/31
# Panel of 16 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

5 Operating Characteristics

Function panellist() collects multiple panels.

z = panellist(x0, x1, x2, x3)

The S3 method dispatch base::print.listof() displays a panellist.

z
# Component 1 :
# False(+) ≤0/31
# panelFalse(+) ≤0/31
# Panel of 23 Variant-Collections from
# 50 positive subjects
# 31 negative subjects
# 
# Component 2 :
# False(+) ≤1/31
# panelFalse(+) ≤7/31
# Panel of 18 Variant-Collections from
# 50 positive subjects
# 31 negative subjects
# 
# Component 3 :
# False(+) ≤2/31
# panelFalse(+) ≤13/31
# Panel of 17 Variant-Collections from
# 50 positive subjects
# 31 negative subjects
# 
# Component 4 :
# False(+) ≤3/31
# panelFalse(+) ≤12/31
# Panel of 16 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

5.1 Visualization

The operating characteristics should be described in a peer-reviewed manuscript in the following language.

<Figure> presents the operating characteristics of multiple ordered-panels of variants-collections. The x-axis is the number of collections from the top of each ordered-panel. The y-axis is the percentage of <positive.subjects> identified. An operating characteristics curve moves towards the upper-left corner to indicate a better performance, i.e., higer percentage of <positive.subjects> identified with lower number of collections in the ordered-panel. Investigators should evaluate multiple operating characteristics curves by their control on number of <false.subjects> identified.

The S3 method dispatches autoplot.panellist() and autolayer.panellist() visualize the operating characteristics of panels x0, x1, x2 and x3 in Figure 2.

Figure 2 concludes that the panel x1, controlled at per-collection false positive of <=1/31 (colored green), is the optimal among the four panels, as

• Panel x0, controlled at a more stringent criterion of per-collection false positive of <=0/31, detects substantially less true-positives. In other words, compared to panel x1, panel x0 loses substantial power to detect true-positive due to a more stringent control of false-positive.
• Panels x2 and x3, controlled at less stringent criteria of per-collection false positive of <=2/31 and <=3/31, respectively, have almost identical operating characteristics as panel x1. In other words, compared to panel x1, panels x2 and x3 have no improvement on detection of true-positive, at the cost of higher false-positive.

z |> 
  autoplot()

Figure 2: Operating characteristics of panellist z

5.2 Selected Collections

The S3 method dispatch as_flextable.panellist() displays the collections selected in each panel. The collections that contain more than one variants are highlighted using the light-yellow color.

z |> 
  as_flextable()

Variant-Collection	Individual		Variants in Collection	Panel
	True(+)	False(+)		False(+) ≤0/31 panelFalse(+) ≤0/31 size=23	False(+) ≤1/31 panelFalse(+) ≤7/31 size=18	False(+) ≤2/31 panelFalse(+) ≤13/31 size=17	False(+) ≤3/31 panelFalse(+) ≤12/31 size=16
Collection 1	12/50	0/31	chr17:7176997 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 2	10/50	0/31	chr11:118583356 G>A	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 3	6/50	0/31	chr12:49269877 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 4	5/50	0/31	chr11:102350873 T>A chr11:102350877 A>C	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 5	5/50	0/31	chr3:9789723 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 6	5/50	0/31	chrX:47199344 G>A	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 7	4/50	0/31	chr1:29053204 TA>T	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31
Collection 8	4/50	0/31	chr1:29053290 C>A chr1:29053291 A>G chr1:29053294 G>C chr1:29053295 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31
Collection 11	4/50	0/31	chr17:7313221 G>A	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31
Collection 12	4/50	0/31	chr19:10115113 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 13	4/50	0/31	chr19:10252243 G>T chr19:10252246 G>T	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 14	4/50	0/31	chr19:10252358 G>C	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31
Collection 17	4/50	0/31	chr19:55092500 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 19	4/50	0/31	chr5:141583561 T>A	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 20	4/50	0/31	chr8:144098748 A>T chr8:144098750 G>C	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 22	3/50	0/31	chr1:11054996 C>A	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 23	3/50	0/31	chr1:160999732 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 31	3/50	0/31	chr11:64800903 A>C	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31
Collection 32	3/50	0/31	chr12:8946388 C>G	False(+) ≤0/31 panelFalse(+) ≤0/31	False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31
Collection 33	3/50	0/31	chr14:105249697 C>T	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 37	3/50	0/31	chr16:71921451 A>C	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 50	3/50	0/31	chr19:12747525 C>A	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 57	3/50	0/31	chr19:58551755 G>A chr19:58551758 C>A	False(+) ≤0/31 panelFalse(+) ≤0/31
Collection 340	12/50	1/31	chr18:51196776 G>C		False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 341	8/50	1/31	chr5:141582348 C>G		False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 342	7/50	1/31	chr17:43015806 C>T		False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 343	7/50	1/31	chr19:1080497 C>A		False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 346	5/50	1/31	chr19:38840293 C>T		False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31
Collection 348	4/50	1/31	chr1:12001541 C>T		False(+) ≤1/31 panelFalse(+) ≤7/31
Collection 349	4/50	1/31	chr10:17693290 A>AA		False(+) ≤1/31 panelFalse(+) ≤7/31	False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 350	4/50	1/31	chr11:118583892 C>A		False(+) ≤1/31 panelFalse(+) ≤7/31
Collection 815	11/50	2/31	chr17:50094891 GAGGAT>G			False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 816	8/50	2/31	chrX:154442587 T>A			False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 817	7/50	2/31	chr17:1651476 A>G			False(+) ≤2/31 panelFalse(+) ≤13/31
Collection 819	7/50	2/31	chr21:44856223 C>T			False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 827	5/50	2/31	chr7:64978289 G>A			False(+) ≤2/31 panelFalse(+) ≤13/31	False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 1042	11/50	3/31	chr13:45516236 A>G				False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 1043	8/50	3/31	chr16:71922614 C>A				False(+) ≤3/31 panelFalse(+) ≤12/31
Collection 1045	7/50	3/31	chr16:2037561 G>A				False(+) ≤3/31 panelFalse(+) ≤12/31

6 Appendix

6.1 About

R terminology may differ from that commonly used in mathematics and statistics. For explanations and references to the terms and abbreviations used in this vignette, please refer to Section 8.

6.1.1 Environment

This vignette is created under R version 4.5.1 (2025-06-13) with the aid of packages knitr (Xie 2025, v1.50), quarto (Allaire and Dervieux 2025, v1.5.1 with Quarto v1.8.24) and rmarkdown (Allaire et al. 2024, v2.29). Documentation is supported by package roxygen2 (Wickham et al. 2025, v7.3.3).

An Integrated Development Environment (IDE), e.g., RStudio (Posit team 2025) or Positron, is not required, but highly recommended.

6.1.2 Dependency

Package minPanel Imports packages

• cli (Csárdi 2025, v3.6.5), for attractive command line interfaces
• consort (Dayim 2024, v1.2.2), for Consolidated Standards of Reporting Trials (CONSORT) diagram
• flextable (Gohel and Skintzos 2025, v0.9.10), for flexible table
• ggplot2 (Wickham 2016), patchwork (Pedersen 2025), for visualization
• 🗝 matrixStats (Bengtsson 2025, v1.5.0), key dependency, for matrix algebra
• 🗝 methods shipped with vanilla R version 4.5.1 (2025-06-13), key dependencies, for S4 generic function and method dispatches
• scales (Wickham, Pedersen, and Seidel 2025, v1.4.0), for colour pallet

6.2 panel

Function panel() (Section 3) returns an object of S4 class 'panel', with slots

• @m1, a logical matrix, collections on rows and positive subjects on columns
• @m0, a logical matrix, collections on rows and negative subjects on columns
• @id, a list of character vectors. Each character vector contains the variants in a collection.
• @label, an optional character scalar

The S4 method dispatch of show to signature = 'panel' prints the vital information, e.g., the number of collections, and the number of positive and negative subjects.

6.2.1 (Cumulative) True & False Positives

Function true_positive() finds the number of true positives per collection, i.e., the rowSums of the slot @m1.

Function false_positive() finds the number of false positives per collection, i.e., the rowSums of the slot @m0.

Functions true_positive() and false_positive() return an integer vector.

# example

Function cum_true_positive() finds the number of true positives, by a sub-panel of the first i collections.

Function cum_false_positive() finds the number of false positives, by a sub-panel of the first i collections.

Function cum_true_positive() and cum_false_positive() return a not-strictly increasing integer vector.

# example

6.2.2 Subset

The S3 method dispatch subset.panel() 🚧

6.2.2.1 by Panel-Wise False Positives

Panel x1 in Section 4, controlled at false_positive <= 1L per collection, has a substantial panel-wise false positive rate

x1
# False(+) ≤1/31
# panelFalse(+) ≤7/31
# Panel of 18 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

User may impose an additional criterion of panel-wise false positive of 5 or less out of 31 negative subjects.

x1a = m |>
  subset(subset = true_positive > 0L) |>
  subset(subset = false_positive <= 1L, append.label = TRUE) |>
  sort_by(y = ~ true_positive, decreasing = TRUE) |>
  subset(subset = diff(cum_true_positive) > 0L) |>
  subset(subset = cum_false_positive <= 5L, append.label = TRUE)

x1a
# False(+) ≤1/31
# panelFalse(+) ≤5/31
# Panel of 14 Variant-Collections from
# 50 positive subjects
# 31 negative subjects

Figure 3 shows ..

panellist(x0, x1, x1a) |>
  autoplot()

Figure 3: Operating characteristics of x0, x1 and x1a

6.2.3 Sort-By

The S3 method dispatch sort_by.panel() 🚧

6.2.4 Append Label

Function append_label() 🚧

6.2.5 CONSORT Diagram

Function consort.panel() creates a CONSORT diagram of the creation of a panel, using package consort (Dayim 2024).

Function consort.panel() is a wrapper of the function consort::consort_plot(), which returns an object of class 'consort'.

Examples using function consort.panel() are provided in Section 4.

6.3 panellist

6.3.1 CONSORT Diagram

Function consort.panellist() organizes the CONSORT diagrams of each panel member in a panellist in a collage, using packages consort (Dayim 2024) and patchwork (Pedersen 2025).

Figure 4 presents side-by-side the selection of collections in x0, x1, and x2.

z[1:3] |> 
  consort.panellist(nrow = 1L)

Figure 4: CONSORT Diagram of panellist

7 What We Don’t Do

7.1 subset.panel()

7.1.1 by Multiple Criteria

too difficult to parse..

m |> 
  subset.panel(subset = (true_positive > 0L) & (false_positive <= 3L))

8 Terms & Abbreviations

Table 1 presents a comprehensive glossary of R terms and abbreviations used in this vignette, which is folded to avoid overwhelming the reader with its excessive length.

R terminology and nomenclature could be drastically different from that of mathematics and statistics. Readers are strongly advised to read closely from the reference links in Table 1, most of which point to webpages on search.r-project.org, cran.r-project.org, adv-r.hadley.nz and en.wikipedia.org.

Table 1: Terms & Abbreviations in R

Table 1: Terms & Abbreviations in R

Term / Abbreviation	Description
CRAN, R	The Comprehensive R Archive Network, https://cran.r-project.org
Depends, Imports, Suggests, Enhances	Writing R Extensions, Section 1.1.3 Package Dependencies
|>	Forward pipe operator introduced since R version 4.1.0
~	Tilde operator, to separate the left- and right-hand sides in a formula
.	Dot symbol, to denote the remaining variables contained in a data.frame. See formula and terms.formula for details.
:	Colon operator for interaction of factors, see section Details of formula documentation
/	Forward slash operator for nested grouping structure, see section Details of formula documentation
::, :::	Double and triple colon operators, explicitly-namespaced function or object
args	Argument list of a function
attr, attributes	Attributes
body	Body of a function
data.frame	Data frame
.Deprecated, .Defunct	R function retirement
deparse, deparse1	Expression deparsing, i.e., turn unevaluated expressions into character scalar or vector
diag	Matrix diagonals
environment, as.environment	(Coerce to an) environment
eval	Evaluate an R expression
export	Export tags in NAMESPACE
expression	Unevaluated expressions
factor	Categorical object, a.k.a., factor
file.size	File size in bytes
(one-sided) formula	Formula, or one-sided formula
foobar	Placeholder; https://en.wikipedia.org/wiki/Foobar
formals	Formal arguments of R function
function	R function definition
ggplot	Elegant data visualisations using the grammar-of-graphics, from package ggplot2 (Wickham 2016)
identical	Exact equality in R
inherits	Class inheritance
Inf, is.finite	Positive infinity \infty
kable	Tables in LaTeX, HTML, markdown and reStructuredText, from package knitr (Xie 2025)
language	R language object
length	Length of an object
lengths	Lengths of list or vector elements
library	Loading/attaching packages
list, listof	Lists of objects
list2env	Create an environment containing all list components as objects
log, log1p, log2, log10	\log(x) and \log(x+1) transformations
.Machine	Numerical characteristics of the machine R is running on, e.g., 32-bit integers and IEC 60559 floating-point (double precision) arithmetic
match	Value matching
matrix	Matrices
merge, merge.data.frame	Merge two data.frames
mc.cores	Number of central processing unit (CPU) cores to use for parallel computing
message	Diagnostic message printed in R console
missing	R function not having a default value for a formals argument
storage mode, vector mode	Storage mode; vector mode
NaN	Not-a-Number
na.fail, na.omit, na.exclude, na.pass	Handle missing values in R objects
names	Names of an R object
ncol, nrow	Number of rows/columns of an array
numeric	numeric, i.e., double-precision object
object.size	Memory allocation
pi	Mathematical constant \pi
pmean, pmedian	Point-wise mean and median, from package groupedHyperframe
pmax, pmin	Point-wise maxima and minima
.Primitive	Primitive, i.e., internally implemented, function
range	Range of values
return	R function return
S3, generic, UseMethod, methods, getS3method	S3 object oriented system, https://adv-r.hadley.nz/s3.html
S4, isS4, setClass, setGeneric, setMethod, getMethod	S4 object oriented system, https://adv-r.hadley.nz/s4.html, implemented in package methods shipped with R version 4.5.1 (2025-06-13)
save, saveRDS, xz	Save with xz compression
search	Search path
seq, seq.int	Sequence generation
signature	Tools for managing S4 generic functions
split	Divide into groups
stopifnot	To ensure the truth of R expressions
symbol, name	R object name/symbol
t, t.default	Transpose
tempfile	Create names for temporary files
typeof	R internal type or storage mode of any object
unclass	Remove S3 'class' attribute
vector	Vector
with, with.default, with.hyperframe	To evaluate an expression in a local environment, or in each row of a hyperframe
within, within.data.frame	To evaluate an expression in a local environment and makes modifications to the environment

9 References

Allaire, JJ, and Christophe Dervieux. 2025. quarto: R Interface to ’Quarto’ Markdown Publishing System. https://doi.org/10.32614/CRAN.package.quarto.

Allaire, JJ, Yihui Xie, Christophe Dervieux, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, et al. 2024. rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown.

Bengtsson, Henrik. 2025. matrixStats: Functions That Apply to Rows and Columns of Matrices (and to Vectors). https://doi.org/10.32614/CRAN.package.matrixStats.

Csárdi, Gábor. 2025. cli: Helpers for Developing Command Line Interfaces. https://doi.org/10.32614/CRAN.package.cli.

Dayim, Alim. 2024. consort: Create Consort Diagram. https://doi.org/10.32614/CRAN.package.consort.

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