4 An R Package Engineering Workflow

Good Software Engineering Practice for R Packages

Wenyi Liu

July 23, 2026

Motivation

From an idea to a production-grade R package

Example scenario: in your daily work, you notice that you need certain one-off scripts again and again.

The idea of creating an R package was born because you understood that “copy and paste” R scripts is inefficient and on top of that, you want to share your helpful R functions with colleagues and the world…

Professional Workflow

Photo CC0 by ELEVATE on pexels.com

Typical work steps

  1. Idea
  2. Concept creation
  3. Validation planning
  4. Specification:
    1. User Requirements Spec (URS),
    2. Functional Spec (FS), and
    3. Software Design Spec (SDS)
  1. R package programming
  2. Documented verification
  3. Completion of formal validation
  4. R package release
  5. Use in production
  6. Maintenance

Workflow in Practice

Photo CC0 by Chevanon Photography on pexels.com

Frequently Used Workflow in Practice

  1. Idea
  2. R package programming
  3. Use in production
  4. Bug fixing
  5. Use in production
  1. Bug fixing + Documentation
  2. Use in production
  3. Bug fixing + Further development
  4. Use in production
  5. Bug fixing + …

Bad practice!

Why?

Why practice good engineering?

Cost distribution among software process activities

doi:10.14569/IJACSA.2020.0110375

Why practice good engineering?

Origin of errors in system development

Boehm, B. (1981). Software Engineering Economics. Prentice Hall.

Why practice good engineering?

  • Don’t waste time on maintenance
  • Be faster with release on CRAN
  • Don’t waste time with inefficient and buggy further development
  • Fulfill regulatory requirements1
  • Save refactoring time when the PoC becomes the release version
  • You don’t have to be shy any longer about inviting other developers to contribute to the package on GitHub

Why practice good engineering?

Invest time in

  • requirements analysis,
  • software design, and
  • architecture…

… but in many cases the workflow must be workable for a single developer or a small team.

Workable Workflow

Photo CC0 by Kateryna Babaieva on pexels.com

Suggestion for a Workable Workflow

  1. Idea
  2. Design docs
  3. R package programming
  4. Quality check (see Ensuring Quality by Wenyi)
  5. Use in production

Example - Step 1: Idea

Let’s assume that you used some lines of code to create simulated data in multiple projects:

dat <- data.frame(
    group = c(rep(1, 50), rep(2, 50)),
    values = c(
        rnorm(n = 50, mean = 8, sd = 12),
        rnorm(n = 50, mean = 14, sd = 11)
    )
)

Idea: put the code into a package

Example - Step 2: Design docs

  1. Describe the purpose and scope of the package
  2. Analyse and describe the requirements in clear and simple terms (“prose”)
Obligation level Key word1 Description
Duty shall “must have”
Desire should “nice to have”
Intention will “optional”

Example - Step 2: Design docs

Purpose and Scope

The R package simulatr shall enable the creation of reproducible fake data.

Package Requirements

simulatr shall provide a function to generate normal distributed random data for two independent groups. The function shall allow flexible definition of sample size per group, mean per group, standard deviation per group. The reproducibility of the simulated data shall be ensured via an optional seed It should be possible to print the function result. A graphical presentation of the simulated data will also be possible.

Example - Step 2: Design docs

Useful formats / tools for design docs:

UML Diagram

Example - Step 3: Packaging

R package programming

  1. Create basic package project (see R Packages by Shuang)
  2. C&P existing R scripts (one-off scripts, prototype functions) and refactor1 it if necessary
  3. Create R generic functions
  4. Document all functions

Example - Step 3: Packaging

One-off script as starting point:

sim.data <- function(n1, n2, m1, m2, s1, s2) {
    data.frame(
        group = c(rep(1, n1), rep(2, n2)),
        values = c(
            rnorm(n = n1, mean = m1, sd = s1),
            rnorm(n = n2, mean = m2, sd = s2)
        )
    )
}

Example - Step 3: Packaging

Refactored script:

getSimulatedTwoArmMeans <- function(n1, n2, mean1, mean2, sd1, sd2) {
    data.frame(
        group = c(rep(1, n1), rep(2, n2)),
        values = c(
            rnorm(n = n1, mean = mean1, sd = sd1),
            rnorm(n = n2, mean = mean2, sd = sd2)
        )
    )
}

Almost all functions, arguments, and objects should be self-explanatory due to their names.

Example - Step 3: Packaging

Define that the result is a list1 which is defined as class2:

getSimulatedTwoArmMeans <- function(n1, n2, mean1, mean2, sd1, sd2) {
    result <- list(n1 = n1, n2 = n2, 
         mean1 = mean1, mean2 = mean2, sd1 = sd1, sd2 = sd2)
    result$data <- data.frame(
        group = c(rep(1, n1), rep(2, n2)),
        values = c(
            rnorm(n = n1, mean = mean1, sd = sd1),
            rnorm(n = n2, mean = mean2, sd = sd2)
        )
    )
    # set the class attribute
    result <- structure(result, class = "SimulationResult")
    return(result)
}

Example - Step 3: Packaging

The output is impractical, e.g., we need to scroll down:

x <- getSimulatedTwoArmMeans(n1 = 50, n2 = 50, mean1 = 5, mean2 = 7, sd1 = 3, sd2 = 4)
x
$n1
[1] 50

$n2
[1] 50

$mean1
[1] 5

$mean2
[1] 7

$sd1
[1] 3

$sd2
[1] 4

$data
    group      values
1       1  4.66940994
2       1  0.31933787
3       1  2.96272303
4       1  2.70531420
5       1  5.12062835
6       1  2.35548281
7       1  2.57505997
8       1 -0.88399994
9       1  3.63627101
10      1  3.94361037
11      1  4.81529069
12      1 -0.39436274
13      1  0.99314238
14      1  3.86245486
15      1  3.05956322
16      1  5.26162202
17      1  1.40752260
18      1  4.92269100
19      1  7.20619497
20      1  3.76065566
21      1  5.67176326
22      1  5.48324162
23      1  4.57165615
24      1  4.02082846
25      1  1.76495973
26      1  2.15497735
27      1  4.57652508
28      1  5.62678657
29      1  5.39330334
30      1  4.95059693
31      1 11.57762809
32      1  7.51376113
33      1  5.05448260
34      1  6.67430470
35      1  0.83801881
36      1  9.82340707
37      1  4.61023079
38      1  1.96505103
39      1  3.36016238
40      1  0.45187905
41      1 -2.48387431
42      1  2.45670705
43      1  3.00338362
44      1  6.30101612
45      1 -1.13825791
46      1  5.05039573
47      1 -0.17725051
48      1  4.46943294
49      1 10.41500771
50      1  5.14376410
51      2  3.32257554
52      2 -0.83476399
53      2  8.47058612
54      2  8.04365637
55      2  8.88584475
56      2 12.82533998
57      2 13.03905444
58      2  4.66104027
59      2  8.94761758
60      2  6.13961007
61      2  8.51968121
62      2 -0.22076005
63      2  5.61787474
64      2  8.34187355
65      2  7.26682253
66      2  8.76645937
67      2  9.15701444
68      2  4.78205822
69      2  5.38654596
70      2 12.15277922
71      2  8.94837757
72      2  7.54055899
73      2 -2.61793227
74      2  6.82242056
75      2 10.89935295
76      2  8.14174634
77      2  1.84714882
78      2  5.91265325
79      2  8.23455972
80      2  5.95902098
81      2  9.76543228
82      2  6.51640180
83      2  4.19512253
84      2 12.53705228
85      2  7.76524641
86      2 12.78336878
87      2  0.02924049
88      2  5.98756064
89      2  7.50028285
90      2  0.51681177
91      2  7.14619000
92      2 12.85481606
93      2 13.76424623
94      2  7.40377994
95      2  7.41457822
96      2 -0.71216408
97      2 11.74731391
98      2  0.09185472
99      2 14.22757810
100     2  2.69311096

attr(,"class")
[1] "SimulationResult"

Solution: implement generic function print

Example - Step 3: Packaging

Generic function print:

print.SimulationResult <- function(x, ...) {
    args <- list(n1 = x$n1, n2 = x$n2, 
        mean1 = x$mean1, mean2 = x$mean2, sd1 = x$sd1, sd2 = x$sd2)
    
    print(list(
        args = format(args), 
        data = dplyr::tibble(x$data)
    ), ...)
}
x
#' @title
#' Print Simulation Result
#'
#' @description
#' Generic function to print a `SimulationResult` object.
#'
#' @param x a \code{SimulationResult} object to print.
#' @param ... further arguments passed to or from other methods.
#' 
#' @examples
#' x <- getSimulatedTwoArmMeans(n1 = 50, n2 = 50, mean1 = 5, 
#'      mean2 = 7, sd1 = 3, sd2 = 4, seed = 123)
#' print(x)
#'
#' @export
$args
   n1    n2 mean1 mean2   sd1   sd2 
 "50"  "50"   "5"   "7"   "3"   "4" 

$data
# A tibble: 100 × 2
   group values
   <dbl>  <dbl>
 1     1  4.67 
 2     1  0.319
 3     1  2.96 
 4     1  2.71 
 5     1  5.12 
 6     1  2.36 
 7     1  2.58 
 8     1 -0.884
 9     1  3.64 
10     1  3.94 
# ℹ 90 more rows

Website with pkgdown

Setup of pkgdown

  • pkgdown makes it quick and easy to build a website for your package
  • After installing pkgdown, just use usethis::use_pkgdown() to get started
  • Main configuration happens in _pkgdown.yml file
  • Many customizations can be applied, but main work during development is to keep the reference section updated with names of .Rd files

Example _pkgdown.yml file

---
url: https://openpharma.github.io/mmrm

template:
  bootstrap: 5
  params:
    ganalytics: UA-125641273-1

navbar:
  right:
    - icon: fa-github
      href: https://github.com/openpharma/mmrm

reference:
  - title: Package
    contents:
      - mmrm-package
  - title: Functions
    contents:
      - mmrm
      - fit_mmrm
      - mmrm_control
      - fit_single_optimizer
      - refit_multiple_optimizers
      - df_1d
      - df_md
      - component

Publication as GitHub Page

  • It is helpful for users to read the website online
  • GitHub is very helpful here because it allows
    • A separate branch gh-pages that stores the rendered website
    • GitHub actions automatically render the website when the main branch is updated
  • To get started, use usethis::use_pkgdown_github_pages()
    • Or, manually deploy site with pkgdown::deploy_to_branch()

Exercise

Photo CC0 by Pixabay on pexels.com

Preparation

  1. Download the unfinished R package simulatr
  2. Extract the package zip file
  3. Open the project with RStudio
  4. Complete the tasks below

Tasks

Add assertions to improve the usability and user experience

Tip on assertions

Use the package checkmate to validate input arguments.

Example:

playWithAssertions <- function(n1) {
  checkmate::assertInt(n1, lower = 1)
}
playWithAssertions(-1)

Error in playWithAssertions(-1) : Assertion on ‘n1’ failed: Element 1 is not >= 1.

Add three additional results:

  1. n total,
  2. creation time, and
  3. allocation ratio

Tip on creation time

Sys.time(), format(Sys.time(), '%B %d, %Y'), Sys.Date()

Add an additional result: t.test result

Add an optional alternative argument and pass it through t.test:

alternative = c("two.sided", "less", "greater")

Implement the generic functions print and plot.

Tip on print

Use the plot example function from above and extend it.

Tip on plot

Use R base plot or ggplot2 to create a grouped boxplot of the fake data.

Optional extra tasks:

  • Implement the generic functions summary and cat

  • Implement the function kable known from the package knitr as generic. Tip: use

    kable <- function(x) UseMethod("kable")

    to define kable as generic

Optional extra task1:

Document your functions with Roxygen2

  1. If you are already familiar with Roxygen2

References

  • Gillespie, C., & Lovelace, R. (2017). Efficient R Programming: A Practical Guide to Smarter Programming. O’Reilly UK Ltd. [Book | Online]
  • Grolemund, G. (2014). Hands-On Programming with R: Write Your Own Functions and Simulations (1. Aufl.).
    O’Reilly and Associates. [Book | Online]
  • Rupp, C., & SOPHISTen, die. (2009). Requirements-Engineering und -Management: Professionelle, iterative Anforderungsanalyse für die Praxis (5. Ed.). Carl Hanser Verlag GmbH & Co. KG. [Book]
  • Wickham, H. (2015). R Packages: Organize, Test, Document, and Share Your Code (1. Aufl.). O’Reilly and Associates. [Book | Online]
  • Wickham, H. (2019). Advanced R, Second Edition.
    Taylor & Francis Ltd. [Book | Online]

License information