3 An R Package Engineering Workflow

Tutorial: Good Software Engineering Practice for R Packages

Friedrich Pahlke

July 8, 2024

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 11.5693750
2       1  7.3404635
3       1  1.9951822
4       1  1.5486142
5       1  3.6997158
6       1  1.8197855
7       1  4.7974151
8       1  5.9363158
9       1  1.9576641
10      1  9.5840016
11      1  8.2897101
12      1  3.8041150
13      1  7.4080279
14      1  7.8556250
15      1  1.7393297
16      1  7.6601508
17      1  7.0192073
18      1  6.3546494
19      1  2.7823651
20      1  4.2246955
21      1 -3.6293900
22      1  4.7855269
23      1 -0.3131968
24      1  1.5188898
25      1  1.7574595
26      1  5.0273721
27      1  6.0304331
28      1  2.6196664
29      1  6.0885153
30      1  3.7927841
31      1  3.8770709
32      1  7.9345873
33      1  1.3859051
34      1  4.5973378
35      1  7.2393474
36      1  7.4264402
37      1  4.2208044
38      1 -1.5457377
39      1  7.9211873
40      1  8.7994999
41      1  9.6472600
42      1  1.1720068
43      1  5.3464461
44      1  4.6280664
45      1 11.0531705
46      1  1.6871314
47      1  4.1355045
48      1  2.2057299
49      1  4.9713388
50      1 10.4105115
51      2  2.5909838
52      2 13.5848519
53      2 -0.2860471
54      2  4.4231713
55      2  2.3596517
56      2  9.6541313
57      2  8.5516826
58      2 11.0188773
59      2  9.4833127
60      2 11.3461939
61      2  1.3714247
62      2  1.3233199
63      2 11.8880661
64      2  6.4357442
65      2  7.6893199
66      2  4.1248432
67      2  5.8043052
68      2  7.9004182
69      2  8.8014256
70      2  4.9447497
71      2  7.1251847
72      2 11.6735921
73      2  6.4680892
74      2  3.2783245
75      2  4.8847914
76      2  8.0258668
77      2  8.0327691
78      2  4.8022986
79      2 13.3150540
80      2  6.9746742
81      2 12.1579331
82      2  2.5758951
83      2  0.7907483
84      2  6.9307380
85      2 13.1737993
86      2  7.9174856
87      2 14.3368072
88      2  8.8623672
89      2  5.6714398
90      2  1.0337590
91      2  5.9304270
92      2  5.2571077
93      2  3.6875055
94      2  6.9289352
95      2  6.9911529
96      2  9.9797865
97      2  6.0139200
98      2  7.0502888
99      2 11.7914250
100     2  5.1818498

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  11.6 
 2     1   7.34
 3     1   2.00
 4     1   1.55
 5     1   3.70
 6     1   1.82
 7     1   4.80
 8     1   5.94
 9     1   1.96
10     1   9.58
# 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/user2024-tutorial-gswep, which is forked from and a subset of the original version at github.com/RCONIS/workshop-r-swe-zrh.

• Important: To use this work you must provide the name of the creators (initial authors), a link to the material, a link to the license, and indicate if changes were made