RPR-Coding style

R Coding Style

 

Keywords:  R coding style; software development

 

 

Caution!

This unit is under development. There is some contents here but it is incomplete and/or may change significantly: links may lead to nowhere, the contents is likely going to be rearranged, and objectives, deliverables etc. may be incomplete or missing. Do not work with this material until it is updated to "live" status.

 

Abstract

Now that you have encountered some concepts of R programming, how do you write good R code?

 

This unit ...

Prerequisites

You need to complete the following units before beginning this one:

• RPR-Plotting

 

Objectives

...

 

Outcomes

...

 

Deliverables

• Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
• Journal: Document your progress in your course journal.
• Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

 

Evaluation

Evaluation: NA

This unit is not evaluated for course marks.

 

Contents

What do we even mean by "good" R code? ...

Warning: Coding style is a volatile topic. Friendships have been renounced, eternal vows of marriage have been dissolved, stock-options have been lost, all over a disagreement about the One True Brace Style, or whether fetchSequenceFromPDB()is a good function name or not. I am laying out coding rules below that reflect a few years of experience. They work for me, they may not work for you.

However:

• If you are taking one of my workshops, I recommend you to follow these rules: I write this way, and we will find it easier to communicate if you do too.
• If you are collaborating on a software project, these rules embody the standard across the project, and I will not check-in code that deviates. Here, consistency is key; but if you think you have a better approach, you only need to convince me and we will change the rule and apply it throughout the codebase.
• If you are taking one of my courses, you may lose marks if you do not adhere to these standards. Of course, following rules must not be done blindly – we are training future collaborators, not parrots – but you need to write in the spirit of the one rule we all agree on:

Well written code helps the reader to understand the intent.

 

 

General

It should always be your goal to code as clearly and explicitly as possible. R has many complex idioms, and it being a functional language that can generally insert functions anywhere into expressions, it is possible to write very terse, expressive code. Don't do it. Pace yourself, and make sure your reader can follow your flow of thought. More often than not the poor soul who will be confused by a particularly witty use of the language will be you, yourself, half a year later. There is an astute observation by Brian Kernighan that applies completely:

"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it."

• Never sacrifice being explicit for saving on keystrokes. Code is read much more often than it is written!

• Use lots of comments. Don't describe what the code does, but explain why.
• Indent comment hashes to align with the expressions in a block.
• Use only <- for assignment, not =
• ...but do use = when passing values into the arguments of functions.
• Don't use <<- (global assignment) except in very unusual cases. Actually never.
• Define global variables at the beginning of the code, use all caps variable names (MAXWIDTH) for such parameters. Never have "magic numbers" appear in your code.
• If such variables are meant to be truly global use options() to set them.

• Don't use attach().
• Always use for (i in seq(along=x)) {...} rather than for (i in 1:length(x)) {...} because if x == NULL the loop is executed once, with an undefined variable.

 

Layout

• Limit yourself to 80 characters per line.
• Don't use semicolons to write more than one expression on a line.

 

Design and granularity

• Don't repeat code. Use functions instead.
• Don't repeat code. If you feel the urge to type code more than once, that's how you know you should break up the code into functions.
• Don't repeat code. I'm repeating this for emphasis.

One of the general principles of writing clear, maintainable code is collocation. This means that information items that can affect each other should be viewable on the same screen. Spolski makes a great argument for this, together with a few excellent examples; he also makes a case for a special kind of prefix notation for variable and function names that has a lot of merit.

• If the code for a function does not fit on approxiamtaley one prnted page, you should probably break it up further.

• if your loops or conditionals are nested more than three levels deep, you should rethink the logic.

 

Headers

• Give your sources headers stating purpose, author, date and version information, and note bugs and issues.
• Give your functions headers that describe purpose, arguments (including required datatypes), and return values. Callers should be able to work with the function without having to read the code.

 

Sections

• Use separators (# --- SECTION -----------------) to structure your code.

 

Parentheses and Braces

• In mathematical expressions, always use parentheses to define priority explicitly. Never rely on implicit operator priority. (( 1 + 2 ) / 3 ) * 4
• Always use braces {}, even if you write single-line if statements and loops.

 

Spaces

if and for are language keywords, not functions. Separate the following parenthesis from the keyword with a space.

Good:

if (silent) { ...

Bad:

if(silent) { ...

• Always separate operators and arguments with spaces.
• Never separate function names and their following parentheses with spaces.
• Always use a space after a comma, and never before a comma.

Good:

print(1 / 3, digits = 10)

Bad:

print (1 / 3 ,digits = 10)

 

Names

There are only two hard things in Computer Science: cache invalidation and naming things.

- Phil Karlton

• Use informative and specific filenames for code sources; give them the extension .R
• Periods have a syntactic meaning in object-oriented classes. I consider their use in normal variables names wrong, even though this is not a syntax error.
• Alphabetically sort names for related together, code autocomplete will be more useful.
• Use the concise camelCaseStyle for variable names, don't use the confusing.dot.style or the rambling pothole_style.
• Don't abbreviate argument names. You can, but you shouldn't.

• Never reassign reserved words.
• Don't use c as a variable name since c() is a function.
• Don't call your data frames df since df() is a function.

• Name length should be commensurate with the scope of a variable.

Specific naming conventions I like

isValid, hasNeighbour ... Boolean variables

findRange(), getLimits() ... simple function names (verbs!)

initializeTable() ... not initTab()

node ... for one element; nodes ... for more elements

nPoints ... for number-of

isError ... not isNotError: avoid double negation

 

Conditionals

 

Indent Style

No need for much discussion. Follow the One True Bracing Style and we will both be happy. If you don't immediately see why: read about indent style here.

Indentation of long function declarations

• Use spaces to align repeating parts of code, so errors become easier to spot.

 

Loops

 

Functions

• Always explicitly return values from functions, never rely on the implicit behaviour that returns the last expression.

• In general, return only from the end of the function, not from multiple places.
• Explicitly assign values to crucial function arguments, even if you think you know that that value is the default.

 

Efficiency

If possible, do not grow data structures dynamically, but create the whole structure with "empty" values, then assign values to its elements. This is much faster.

 # This is bad:
 v <- 0
 for (i in 1:100000) {
     v <- c(v, sqrt(i))
 }
    user  system elapsed
 20.192   2.182  22.540

 # This is marginally better:
 v <- numeric()
 for (i in 1:100000) {
     v[i] <- sqrt(i)
 }
   user  system elapsed
 14.185   2.036  16.230

 # This is much, much better (200 times faster):

 v <- numeric(100000)
 for (i in seq_along(v)) {
     v[i] <- sqrt(i)
 }
   user  system elapsed
  0.101   0.008   0.108

 

# [END]

• Always end your code with an # [END] comment. This way you can be sure it was copied or saved completely and nothig has been inadvertently omitted.

References and Notes

• Google's R Style Guide
• Jenny Bryan has very useful comments on how style supports coding
• XKCD on Code Quality
• R source code itself is largely based on the GNU coding standards
• Tim Ottinger on Naming
• Joel Spolski on collocation

 

 

Further reading, links and resources

 

Notes

 

Self-evaluation

 

 

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If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.

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About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-08-05

Version:

0.1

Version history:

• 0.1 First stub

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