3 An R Package Engineering Workflow

Short Course: Good Software Engineering Practice for R Packages

August 12, 2025

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

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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)
   4. Test Plan (TP)

5. R package programming
6. Documented verification
7. Completion of formal validation
8. R package release
9. Use in production
10. Maintenance

Extensive documentation, huge paperwork, lots of manual work, lots of signatures, …

Workflow in Practice

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Frequently Used Workflow in Practice

1. Idea
2. R package programming
3. Use in production
4. Bug fixing
5. Use in production

6. Bug fixing + Documentation
7. Use in production
8. Bug fixing + Further development
9. Use in production
10. 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 Proof-of-Concept (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

1. See also Ensuring Quality

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

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Suggestion for a Workable Workflow

1. Idea
2. Design docs
3. R package programming
4. Quality check (see Ensuring Quality)
5. Publication
6. 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	must2	“must have”
Desire	should	“nice to have”
Intention	may	“optional”

1. Rupp, C., & SOPHISTen, die. (2009). Requirements-Engineering und -Management: Professionelle, iterative Anforderungsanalyse für die Praxis (5. Ed.). Carl Hanser Verlag GmbH & Co. KG.

2. Shall and must - plainlanguage.gov

Example - Step 2: Design docs

Purpose and Scope

The R package simulatr is intended to enable the creation of reproducible fake data.

Package Requirements

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

Example - Step 2: Design docs

Useful formats / tools for design docs:

• R Markdown¹ (*.Rmd)
• Quarto¹ (*.qmd)
• Overleaf²
• draw.io³

UML Diagram

1. Based on Markdown, the easy-to-use markup language

2. The easy to use, online, collaborative LaTeX editor

3. A picture is worth a thousand words

Example - Step 3: Packaging

R package programming

1. Create basic package project (see R Packages)
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

1. See also Ensuring Quality

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)
}

1. It’s common practice to add the function arguments to the result \(\rightarrow\) should be a list or class

2. To implement generics, the result must be a class

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  8.64218153
2       1 10.91226374
3       1  4.98990735
4       1  3.79359415
5       1  8.93235219
6       1  3.45334903
7       1 15.41808102
8       1  1.21796744
9       1  3.35649889
10      1  2.25498157
11      1 -0.37714311
12      1  3.60553906
13      1  6.13896903
14      1 -2.91886075
15      1  7.29617732
16      1  3.36024176
17      1  2.92418256
18      1  7.07479597
19      1  1.07617830
20      1  9.86282624
21      1  7.98214428
22      1  6.16850528
23      1  7.27313995
24      1  9.13085791
25      1  2.22992214
26      1  1.35226663
27      1  2.87387133
28      1  1.91652946
29      1 10.82354483
30      1  1.75103998
31      1  4.67699187
32      1  0.66234909
33      1  7.84656008
34      1  4.12680527
35      1  3.80270242
36      1  8.15205838
37      1  6.32891444
38      1  4.83506199
39      1  2.46015430
40      1 -1.89478462
41      1 12.35935634
42      1  6.86726023
43      1  3.78855406
44      1  5.04217363
45      1  0.08343361
46      1  7.20014144
47      1  6.18135414
48      1 -1.72764643
49      1  8.18389042
50      1  0.39340892
51      2  4.12868187
52      2  7.17012708
53      2  7.84531088
54      2 13.24874264
55      2  3.56378939
56      2  6.55268334
57      2 16.16230343
58      2  6.56316727
59      2  5.37347462
60      2  3.40636184
61      2 12.71588054
62      2  9.79670036
63      2  5.99278457
64      2  8.27316355
65      2 10.33232979
66      2  6.39699620
67      2 13.95158726
68      2  5.93473956
69      2  4.62064267
70      2  4.17495108
71      2 11.14784347
72      2 -1.20128354
73      2  1.09235579
74      2  0.04896084
75      2  8.69846556
76      2  6.04000300
77      2  5.34799958
78      2 16.78808504
79      2  2.33264842
80      2 10.87346752
81      2 12.52031005
82      2  6.59318294
83      2 -0.15999628
84      2  4.04766756
85      2 11.20859771
86      2 10.02240058
87      2  6.72913834
88      2 12.22252099
89      2  8.78946651
90      2  6.24318357
91      2  4.42832620
92      2  7.85946776
93      2  6.54342340
94      2  9.31896537
95      2 12.09707605
96      2  4.55376951
97      2  2.01371568
98      2  7.45718502
99      2  7.86063272
100     2  6.81068531

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

Solution: implement generic function print

Example - Step 3: Packaging

Generic function print:

• Code
• Roxygen
• Output

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   8.64
 2     1  10.9 
 3     1   4.99
 4     1   3.79
 5     1   8.93
 6     1   3.45
 7     1  15.4 
 8     1   1.22
 9     1   3.36
10     1   2.25
# ℹ 90 more rows

Exercise

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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

• Task 1
• Task 2
• Task 3
• Task 4
• Task 5
• Task 6
• Task 7

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

• Creators (initial authors): Friedrich Pahlke
• In the current version, changes were done by (later authors): Andrew Bean
• This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
• The source files are hosted at github.com/RCONIS/mcp2025-shortcourse, which is forked from and a subset of the original version at github.com/RCONIS/user2024-tutorial-gswep.
• Important: To use this work you must provide the creators’ names, links to the material and license, and indicate if changes were made.