3 An R Package Engineering Workflow

BBS Course: Good Software Engineering Practice for R Packages

Daniel

March 24, 2023

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)

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

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 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 by Joe

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 by Joe)
5. Publication (see Publication by Liming)
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	shall	“must have”
Desire	should	“nice to have”
Intention	will	“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.

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:

• 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 by Liming)
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 by Joe

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  5.98879547
2       1  8.45406384
3       1  6.33393873
4       1  4.73534951
5       1  6.22901913
6       1  4.46917585
7       1  9.57319989
8       1  2.99212334
9       1 10.28045738
10      1  5.45014836
11      1  1.73438805
12      1  4.97937338
13      1  1.87011579
14      1  5.91523715
15      1  3.24274769
16      1  6.97725695
17      1  3.17082970
18      1  6.99807551
19      1  9.80686354
20      1  3.28699553
21      1  5.97037685
22      1  4.22627350
23      1  2.56687195
24      1  1.57033105
25      1  7.55921355
26      1  2.74321576
27      1  2.03677499
28      1  9.07443238
29      1  5.70732620
30      1  8.96886654
31      1  7.59082745
32      1  1.84782111
33      1  1.37512371
34      1 -0.90151532
35      1  5.51888192
36      1  5.75549760
37      1 -0.35299132
38      1  5.81325625
39      1  2.09980309
40      1  3.24322503
41      1  7.89070780
42      1  6.32204874
43      1  6.69188477
44      1  4.23160024
45      1  7.39511417
46      1  0.13608340
47      1  2.30461443
48      1  5.21956189
49      1 -1.40200237
50      1 -0.74881237
51      2  3.89425007
52      2 10.49286520
53      2  6.32054559
54      2  4.16492185
55      2  1.16340748
56      2  5.08499817
57      2 11.58772555
58      2  4.24707494
59      2 17.91217539
60      2  6.28250406
61      2  9.42065413
62      2 -0.36096911
63      2  7.52635022
64      2  7.43643349
65      2  6.95277173
66      2 10.78475009
67      2  7.15120533
68      2 11.43059638
69      2  0.01178977
70      2  6.06776505
71      2  6.11764297
72      2  8.72388776
73      2  7.31624670
74      2  3.66203314
75      2 15.15989740
76      2  5.50338198
77      2  8.75154282
78      2 12.19994927
79      2  8.24046933
80      2  0.30104210
81      2  3.78153051
82      2  4.53308119
83      2  5.02717233
84      2  1.22982600
85      2  6.02142265
86      2  8.78826498
87      2  5.99681883
88      2  9.04900599
89      2 16.18401144
90      2  9.06763288
91      2  8.22686111
92      2  6.41426579
93      2 10.12519902
94      2  2.62685492
95      2  4.35392643
96      2 11.78124397
97      2  9.95442351
98      2 14.75851204
99      2  8.11630910
100     2  1.04221831

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   5.99
 2     1   8.45
 3     1   6.33
 4     1   4.74
 5     1   6.23
 6     1   4.47
 7     1   9.57
 8     1   2.99
 9     1  10.3 
10     1   5.45
# 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
• This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
• The source files are hosted at github.com/openpharma/workshop-r-swe, which is forked from the original version at github.com/kkmann/workshop-r-swe.
• 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