4 Ensuring Quality

BBS Course: Good Software Engineering Practice for R Packages

Friedrich

February 10, 2023

Apply Clean Code Rules

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Why is Clean Code important?

  • Maintainability: The code is readable and understandable and has a reduced complexity, i.e., it’s easier to fix bugs
  • Extensibility: The architecture is simpler, cleaner, and more expressive, i.e., it’s easier to extend the capabilities and the risk of introducing bugs is reduced
  • Performance: The code often runs faster, uses less memory, or is easier to optimize

Example: Clean Code Rules - Step by Step

This script breaks all common clean code rules:

y=function(x){
  s1=0
  for(v1 in x){s1=s1+v1}
  m1=s1/length(x)
  i=ceiling(length(x)/2)
  if(length(x) %% 2 == 0){i=c(i,i+1)}
  s2=0
  for(v2 in i){s2=s2+x[v2]}
  m2=s2/length(i)
  c(m1,m2)
}
y(c(1:7, 100))
[1] 16.0  4.5

We now refactor it (CCR#9) by applying clean code rules…

Example: CCR#1

y=function(x){
  s1=0
  for(v1 in x){s1=s1+v1}
  m1=s1/length(x)
  i=ceiling(length(x)/2)
  if(length(x) %% 2 == 0){i=c(i,i+1)}
  s2=0
  for(v2 in i){s2=s2+x[v2]}
  m2=s2/length(i)
  c(m1,m2)
}
y(c(1:7, 100))
[1] 16.0  4.5

CCR#1 Naming: Are the names of the variables, functions, and classes descriptive and meaningful?

Example: CCR#1

getMeanAndMedian=function(x){
    sum1=0
    for(value in x){sum1=sum1+value}
    meanValue=sum1/length(x)
    centerIndices=ceiling(length(x)/2)
    if(length(x) %% 2 == 0){
        centerIndices=c(centerIndices,centerIndices+1)
    }
    sum2=0
    for(centerIndex in centerIndices){sum2=sum2+x[centerIndex]}
    medianValue=sum2/length(centerIndices)
    c(meanValue,medianValue)
}

CCR#1 Naming

CCR#2 Formatting: Are indentation, spacing, and bracketing consistent, i.e., is the code easy to read

Example: CCR#2

getMeanAndMedian <- function(x) {
    sum1 <- 0
    for (value in x) {
        sum1 <- sum1 + value
    }
    meanValue <- sum1 / length(x)
    centerIndices <- ceiling(length(x) / 2)
    if (length(x) %% 2 == 0) {
        centerIndices <- c(
          centerIndices, centerIndices + 1)
    }
    sum2 <- 0
    for (centerIndex in centerIndices) {
        sum2 <- sum2 + x[centerIndex]
    }
    medianValue <- sum2 / length(centerIndices)
    c(meanValue, medianValue)
}

CCR#2 Formatting

CCR#3 Simplicity: Did you keep the code as simple and straightforward as possible, i.e., did you avoid unnecessary complexity

Example: CCR#3

Note:

  • From the Simplicity rule also follows that large source files should be split into multiple files
  • General guideline: keeping the number of lines to less than 1,000 lines per file can help maintain code readability and manageability

Example: CCR#3

getMeanAndMedian <- function(x) {
    meanValue <- sum(x) / length(x)
    centerIndices <- ceiling(length(x) / 2)
    if (length(x) %% 2 == 0) {
        centerIndices <- c(centerIndices, centerIndices + 1)
    }
    medianValue <- sum(x[centerIndices]) / length(centerIndices)
    c(meanValue, medianValue)
}

CCR#3 Simplicity

CCR#4 Single Responsibility Principle (SRP): Has each function a single, well-defined purpose

Example: CCR#4

getMean <- function(x) {
    sum(x) / length(x)
}

isLengthAnEvenNumber <- function(x) {
    length(x) %% 2 == 0
}

getMedian <- function(x) {
    centerIndices <- ceiling(length(x) / 2)
    if (isLengthAnEvenNumber(x)) {
        centerIndices <- c(centerIndices, centerIndices + 1)
    }
    sum(x[centerIndices]) / length(centerIndices)
}

CCR#4 Single Responsibility Principle (SRP)

CCR#5 Don’t Repeat Yourself (DRY): Did you avoid duplication of code, either by reusing existing code or creating functions

Example: CCR#5

CCR#5: DRY

Suppose you have a code block that performs the same calculation multiple times:

result1 <- 2 * 3 + 4
result2 <- 2 * 5 + 4
result3 <- 2 * 7 + 4

Create a function to encapsulate this calculation and reuse it multiple times:

calculate <- function(x) {
  2 * x + 4
}

result1 <- calculate(3)
result2 <- calculate(5)
result3 <- calculate(7)

Example: CCR#5

getMean <- function(x) {
    sum(x) / length(x)
}

isLengthAnEvenNumber <- function(x) {
    length(x) %% 2 == 0
}

getMedian <- function(x) {
    centerIndices <- ceiling(length(x) / 2)
    if (isLengthAnEvenNumber(x)) {
        centerIndices <- c(centerIndices, centerIndices + 1)
    }
    getMean(x[centerIndices])
}

CCR#5 Don’t Repeat Yourself (DRY)

CCR#6 Comments: Did you use comments to explain the purpose of code blocks and to clarify complex logic

Example: CCR#6

# returns the mean of x
getMean <- function(x) {
    sum(x) / length(x)
}

# returns TRUE if the length of x is 
# an even number; FALSE otherwise
isLengthAnEvenNumber <- function(x) {
    length(x) %% 2 == 0
}

# returns the median of x
getMedian <- function(x) {
    centerIndices <- ceiling(length(x) / 2)
    if (isLengthAnEvenNumber(x)) {
        centerIndices <- c(centerIndices, 
             centerIndices + 1)
    }
    getMean(x[centerIndices])
}

CCR#6 Comments

CCR#7 Error Handling: Did you include error handling code to gracefully handle exceptions and unexpected situations 

getMean("a":"z")

Error in “a”:“z” : NA/NaN argument Additionally: Warning messages: In getMean(“a”:“z”) : NAs generated by conversion

Example: CCR#7

# returns the mean of x
getMean <- function(x) {
    tryCatch({
        sum(x) / length(x)      
    }, error = function(e) {
        stop("Failed to calculate the mean of 'x': ", e$message)
    })
}

# returns TRUE if the length of x is an even number; FALSE otherwise
isLengthAnEvenNumber <- function(x) {
    tryCatch({
        length(x) %% 2 == 0
    }, error = function(e) {
        stop("Failed to check if 'x' is an even number: ", e$message)
    })
}

# returns the median of x
getMedian <- function(x) {
    tryCatch({
        centerIndices <- ceiling(length(x) / 2)
        if (isLengthAnEvenNumber(x)) {
            centerIndices <- c(centerIndices, centerIndices + 1)
        }
        getMean(x[centerIndices])            
    }, error = function(e) {
        stop("Failed to calculate the median of 'x': ", e$message)
    })
}

CCR#7 Error Handling

Summary of Clean Code Rules

  1. Naming: Use descriptive and meaningful names for variables, functions, and classes
  2. Formatting: Adhere to consistent indentation, spacing, and bracketing to make the code easy to read
  3. Simplicity: Keep the code as simple and straightforward as possible, avoiding unnecessary complexity
  4. Single Responsibility Principle (SRP): Each function should have a single, well-defined purpose
  5. Don’t Repeat Yourself (DRY): Avoid duplication of code, either by reusing existing code or creating functions

Summary of Clean Code Rules

  1. Comments: Use comments to explain the purpose of code blocks and to clarify complex logic
  2. Error Handling: Include error handling code to gracefully handle exceptions and unexpected situations
  3. Test-Driven Development (TDD): Write tests for your code to ensure it behaves as expected and to catch bugs early
  4. Refactoring: Regularly refactor your code to keep it clean, readable, and maintainable
  5. Code Review: Have other team members review your code to catch potential issues and improve its quality

How to apply Clean Code Rules?

Recommended quality workflow for R packages:

  • Follow the naming and styling guidelines (#1, #2)
  • Continuously write tests and optimize the code coverage with help of tools (#7, #8)
  • Document the package and functions (#6)
  • Regularly refactor your code (#1 - #7, #9)
  • Publish your code on GitHub and invite colleagues to contribute (#10)

Package Testing

CCR#8: TDD

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Verification vs Validation

Verification:
Are we building the product right?

  • The product meets a set of specified requirements
  • Activities: reviews, inspections, and testing
  • Goal: ensure that the product has been designed and implemented correctly

Validation:
Are we building the right product?

  • Evaluate the product during or at the end of the development process
  • Show that the product meet the specified requirements
  • Goal: confirm that the product is fit for its intended purpose
    \(\rightarrow\) Refers back to the user’s needs

What are Unit Tests?

  • Automated tests
  • Record the expected output of a function using code
  • Check individual units of code (functions or methods) for correctness
  • Typically written by developers as part of the software development process
  • Typically run automatically as part of a CI/CD pipeline
  • Can be run before committing code changes

Why are unit tests important?

CCR#8: TDD

  • Help to ensure that individual units of code are working correctly
  • Ensure that changes to the codebase do not break existing functionality
  • Express the desired behavior in a way that a human can understand
  • Help to identify and fix bugs early on in the development process
  • Save time and resources by catching issues before they become more difficult and costly to fix

Unit tests help to increase the reliability and maintainability of the code

What other important test types exist?

  • Integration Testing: Test if different functions or scripts work together as expected
  • Performance Testing: Analyze the performance of the implemented functions and check whether they meet the requirements (UR/UX)
  • Snapshot Testing: Record the results in a separate human-readable file and compare it to the output during the test; useful for large/complex outputs and binary formats like plots

Testing in Practice

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How to realize testing with R?

R package testthat

  • Popular testing framework for R that is easy to learn and use
  • Unit testing, integration testing, and snapshot testing supported
  • Also performance testing, e.g., with help of microbenchmark package

Example: unit test passed

library(testthat)
object <- getMean(c(1, 2, 3))
expected <- 2
expect_equal(object, expected)

Example: unit test failed

expect_equal(getMean(1:4), 2)

Error: mean(1:4) (actual) not equal to 2 (expected).

testthat Comparisons Functions (1/2)

Function name Does code…
expect_condition fulfill a condition?
expect_equal return the expected value?
expect_error throw an error?
expect_false return ‘FALSE’?
expect_gt return a number greater than the expected value?
expect_gte return a number greater or equal than the expected value?
expect_identical return the expected value?
expect_invisible return a invisible object?
expect_length return a vector with the specified length?
expect_lt return a number less than the expected value?
expect_lte return a number less or equal than the expected value?
expect_mapequal return a vector containing the expected values?
expect_message show a message?
expect_named return a vector with (given) names?

testthat Comparisons Functions (2/2)

Function name Does code…
expect_no_condition run without condition?
expect_no_error run without error?
expect_no_message run without message?
expect_no_warning run without warning?
expect_output print output to the console?
expect_s3_class return an object inheriting from the expected S3 class?
expect_s4_class return an object inheriting from the expected S4 class?
expect_setequal return a vector containing the expected values?
expect_silent execute silently?
expect_true return ‘TRUE’?
expect_type return an object inheriting from the expected base type?
expect_vector return a vector with the expected size and/or prototype?
expect_visible return a visible object?
expect_warning throw warning?

How to check the package quality?

  • pkgbuild: Tools needed to build R packages
  • rcmdcheck: Run R CMD check from R and capture results
  • devtools: Tools to make developing R packages easier, e.g., check() automatically builds and checks a source package, using all known best practices

How to improve the test coverage?

covr: Track and report code coverage for your package

library(covr)
x <- package_coverage()
report(x)

Example on test coverage

Let’s assume we have added a generic function to cat a simulation result:

#' 
#' @export
#'
cat.SimulationResult <- function(... , file = "", sep = " ", 
        fill = FALSE, labels = NULL, append = FALSE) {
    args <- list(...)
}

But we forgot to finalize the implementation and didn’t create unit tests.

Re-execution of

report(package_coverage())

then results in…

Example on test coverage

Example on test coverage

We can go into the details by clicking on a file name:

Code Style

CCR#2: Formatting

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Why is Code Style important?

CCR#2: Formatting

  • Make the code more readable, maintainable, and consistent
  • Make it easier for others to understand and contribute to the codebase
  • Adhering to a consistent code style can reduce the number of errors and make debugging simpler
  • “Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.” (The tidyverse style guide)

How to optimize the code styling?

Two popular R packages support the tidyverse style guide:

The devtools function spell_check runs a spell check on text fields in the package description file, manual pages, and optionally vignettes.

Code Styler in RStudio

How to link the styler1 package to a keyboard shortcut:

Exercise

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Tasks

Take your local simulatr package project (see previous excercise) and refactor it, i.e., apply the linked clean code rules:

  1. Optimize naming manually (CCR#1)
  2. Use the styler package to optimize the formatting (CCR#2)
  3. Check and correct where appropriate:
    • Simplicity (CCR#3)
    • SRP (CCR#4)
    • DRY (CCR#5)
  1. Implement appropriate error handling (CCR#7) in the simulatr package
  2. Add comments to explain the purpose of code blocks (CCR#6)
    • If it concerns exported functions use Roxygen2 notation
  3. Check correct spelling with the devtools function spell_check()

Apply CCR#8 to the simulatr package project:

  1. Add unit tests
  2. Optimize your test coverage with help of the covr functions package_coverage and report

Check if your package is ready for use in production with the devtools function check()

References

  • Cotton, R. (2017). Testing R Code (Illustrated Edition).
    Taylor & Francis Inc. [Book]
  • Martin, R. (2008). Clean Code: A Handbook of Agile Software Craftsmanship (1st Edition). Prentice Hall. [Book]

License information

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