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Not about making your code "pretty"! Our goal here is to make code that has fewer bugs and is easier to maintain and extend.
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R-Style-Guide -- Towards a Goal of RED Code

Not about making your code "pretty"! Our goal here is to write code that is readable, extensible and debugable (RED).

Why It's So Important

Your goal should be to avoid future grief, both for yourself and for others who might work on your code.

If you are writing code that you or others will be using in the future, it is absolutely essential that write "good" code, meaning:

  • It is easy to understand, both by others and by yourself, six months later. This facilitates debugging, and adding new features.

  • It is robust to errors, in that it does error checking and messaging. Especially important is avoiding "silent" errors, i.e. ones that do not cause execution to cease but cause possibly major damage in the output, undetected.

In other words, your goal should be

The RED Principle:

  • Readability.

  • Extensibility.

  • Debugability.

Note that the latter will be especially important in this document. You will typically spend much more time debugging your code than in writing it, and this will continue throughout the life of your code. So, anti-bugging is key.

What This Guide Does NOT Do

There are no suggestions here on object naming, number of spaces to indent, placement of braces and so on. Yes, meaningful names for variables etc. are very important, as is indenting, but we have no recommendations on the details. We stick to more specific things that can really make a difference in the quality of your code from an RED point of view.

Motivating Example

Recently I was running some vital production code written by others. The code runs input files and processes them in complex ways.

In many cases for this code, the input files have problems. These can occur in myriad ways, causing the software to choke. In the case of one particular input file, the code did choke, so I ran debugger(), finding that the problem occurred on the line (I've changed things slightly, changing some names and actually shortening it)

cn <- paste(g[which(!,"^[]*cn")[,"start"]))[1]:(abs_-1L)],collapse="")

My major obstacle to solving the problem with the input file was figuring out what this code does! Mind you, the code itself was correct; the input file was the problem, somehow. But it was quite difficult to work with here:

  • Way too much nesting of uf function calls: str_locate() within within "[()" within paste().

  • No code to check whether cn is NA (which it was in this instance).

  • No preceding or following comments explaining what cn and cLines were supposed to contain.

Writing Functions

Nested function calls

Nesting should rarely if ever go beyond two levels. Something like

d <- f(g(h(x)))

should be broken down, saving intermediate results, e.g.

th <- h(x)
tg <- g(th)
d <- f(tg)

There are two advantages to this multi-stage, multiline approach:

  • It's much easier to read, allowing one to "catch one's breath" at each stage and ponder what is happening.

  • It allows adding comments at some of the key stages.

  • It is already set up for debugging, so you can easily set a breakpoint, say at the second stage, then step through the code from there.

You could do something similar with Magrittr pipes if you like using them. Make sure to put each stage of the pipe on a separate line, to enhance the RED-ness of your code, especially for enabling comments at the key stages.

But that still would not address the debugability issue. One would nedd to physically modify the code to set a breakpoint, which is distracting and may break our train of thought. That is the whole point of having a debugging tool in the first place: the alternative, adding and deleting print() statements, takes time and is distracting.

I personally am not a fan of Magrittr pipes, as I just don't see the need for them. The same "left-to-right" (or top-to-bottom) thinking that pipes are supposed to facilitate are easily attained with the "each stage on a separate line" pattern displayed above. And impact on speed/memory is negligible.

Size of functions (in lines)

In order to improve clarity of code, computer science students are taught to code in a top-down or modular manner. Roughly, the idea is something like this:

f <- function(somearguments) 
   "glue" lines
   out1 <- g(some arguments)
   more "glue" lines
   out2 <- h(some arguments)
   more "glue" lines
   out3 <- i(some arguments)

In other words, the body of a function consists largely of calls to other functions. And those functions themselves will be of that form as well.

The advantage, which applies just as well to R users who are not primarily programmers as it does to CS people, is that in this manner, we limit the length of a function. Just as a supermarket may open a new checkstand whenever lines in the existing ones exceed, say, three people, if the number of lines in a function you write exceeds, say a screenful, you should shorten it by moving some of the lines into separate functions.

Why do this?

  • It's difficult to read/debug/enhance a long function.

  • This style in essence presents an outline of what the function does (especially with good function names).

  • When you are writing the code, you write this "outline" first, then fill in the details by writing the functions g(), h() and so on.

  • This makes your code easier to write.

  • For others, it makes your code easier to read.

  • For both you and others, it makes your code easier to debug.

Anonymous functions

Code like

g(x,y,function(t) t^2)

should be used very sparingly. Once again, the problem is debugging. Since the function has no name (technically, is not bound to a name), one cannot instruct one's debugging tool to pause in that function.

Use of global variables

Personally I have always thought the stern admonitions against global variables are overblown. Indeed, you may be surprised to see that some of your favorite CRAN packages, e.g. ggplot2, do make use of some globals.

Careful use of globals can make your code easier to write and debug. You should, however, place your globals in an R environment, so that they are clearly set apart from the others.

Commenting Your Code

In our motivating example above, much of my debugging time would have been unnecessary had the authors of the code included a comment stating what the line is supposed to do, e.g. something as simple as

# find line number for client information

Let's discuss this further.

Central role in code development

In any programming course for Computer Science students, this is absolutely central. If a student turns in a programming assignment with few or no comments, it will get a poor grade. If comments are needed for clarity and readability for CS students, who are presumably strong programmers, then R users who are not expert programmers need comments even more. A style guide at a top university computer science department puts it well:

Commenting involves placing Human Readable Descriptions inside of computer programs detailing what the Code is doing. Proper use of commenting can make code maintenance much easier, as well as helping make finding bugs faster. Further, commenting is very important when writing functions that other people will use. Remember, well documented code is as important as correctly working code.

(Also see specific tips on commenting, later in that document.)

Comments spanning an entire file or a chunk of one

One tends to think of a comment as pertaining to the one or two lines of code that follow it. But one should also write comments with wider scope.

  • At the top of each source file, insert comments giving the reader an overview of the contents. This will typically be an overview of the roles of each major function, how the functions interact with each other, what the main data structures are, and so on.

I strongly recommend that you write these comments at the top of a file BEFORE you start coding (and of course modifying it as you do write code). This will really help you focus during the coding process.

  • Comments that prepare the reader for the following chunk of code, say 6-12 lines, can also greatly enhance the RED-ness of your code, e.g.
# Our strategy will be to first create a matrix of index numbers of the
# k closest neighbors of each of the given points, then construct the
# corresponding graph.

Armed with this overview, the reader will be much better prepared to tackle the code that follows.

Can code be self-documenting?

To some degree, the number of comments can be reduced via use of descriptive variable and function names, e.g.

OverAge65Rows <-getOlder(.....)

But comments can be much more descriptive, e.g.

# at this point, the data frame w will consist of the original rows for
# people over age 65 and who are renters; the data continue to be sorted
# by increasing ZIP Code

And as noted earlier, comments with scope spanning the entire file or significant chunks of it can be quite helpful, something that mere chice of object names can't achieve.

Use of the roxygen Package

Use of this technique can save you time in writing the online help pages for your package. By writing some comments in roxygen format, you simultaneously are writing the help pages.

But in my view, program comments need to be much more detailed that what goes into help pages. I recommend not using roxygen.

Use of External Packages in General

One of the truly great things about the R language is CRAN and Bioconductor, with packages for everything under the sun. I use CRAN packages a lot.

On the other hand, you should make sure an external package is really necessary for your code. Relying on a lot of packages can:

  • Make your code hard to maintain. Every time a package that you use changes, you run the risk that this breaks your code. And remember, those packages may in turn depend on still more packages, and so on, further increasing your exposure.

  • Your never can be completely sure that those packages do exactly what you want in all circumstances. Another package's edge case may be a central use case for your package. Subtle bugs can occur, hard to track down.

In many, probably most cases, the advantages of relying on a package will outweigh the above concerns. But one should approach this very carefully.

Error Checking

Wherever your code, for instance, extracts a subset from a vector or data frame, don't assume the result will be non-NULL! You should have code to check for this whenever you are not completely sure a non-NULL result will occur.

Call stop() for serious errors, warning() for "iffy" cases.

It may be quite useful to use tryCatch() in many of these cases. Instead of your code blowing up, it can give the user a chance to fix her input error, or if the code does blow up, it may print out some helpful information.

Functional Programming

Recently there has been a lot of interest in the R world in functional programming (FP). But of course, a desire to be fashionable should not take priority over RED principles.

Typically FP will enable one to replace an entire loop with a single line of code. This can be beneficial to the RED-ness of your code. For instance, it can aid in making code "top-down" as described earlier here. I often use apply() and lapply() in my own code (and sometimes Map(), Reduce() and Filter(). (I don't use purrr, not having a need for it, but it is certainly a powerful package.)

On the other hand, it must be kept in mind that FP may increase the complexity of your code, which may run counter to our RED goals.

One should be especially aware of the possibility that an FP version of your code may be difficult to debug (very un-RED!). I like the comments in this blog post:

One annoying thing about using map (or apply) in place of loops is that it can make debugging much harder to deal with. With a loop, it’s easy to see where exactly an error occurred and your loop failed (e.g. look at the index of the loop when the error occurred). With map, it can be much harder to figure out where the problem is, especially if you have a very large list that you’re mapping over.

The author then recommends the safely() function in the case of purrr code. For base-R functions such as lapply() one can use tryCatch() as explained above. But these approaches will only make things somewhat easier, and in the end a loop may be easier to debug.

Note carefully that in the computer science world, FP is considered an advanced, abstract concept. An interesting discussion of the topic is in Charavarty and Keller. They believe FP in its standard form in introductory programming classes is unsuitable even for CS majors. R users, with generally less sophistication, may find FP to be harder to code.

So, in many cases, using a loop rather than FP may be RED-der. Don't feel that you "must" avoid loops. Again, if you browse through your favorite CRAN packages, you'll see lots of loops.


R is blessed with a number of different class structures, notably S3, S4 and R6. My own view of R6 is that it is far too abstract for writing RED code. Simpler is often better! I recommend sticking mainly to S3, or if you prefer a bit more tightness, S4.

Also, resist the temptation to make a class out of everything. Once again, the principle should be whether the class would improve the RED-ness of your code. Adding structure means adding complexity, which may result in weaker RED-ness. My own code does often have S3 classes, but it is not top-heavy with them.

Shorter Code May Not Be Better Code

One often sees discussions on the Web in which people post various solutions to a given problem, and in which the tacit goal is to make the code as compact as possible. This is very different from the RED goals, and in my experience, is generally counter to it.

Other R Style Guides

The above recommendations stem from my experience as a software developer and teacher of programming. But in the end, style is a matter of taste. The reader should look at some of the others:

Debugging Tools

Make sure to use one! R's internal browser is not bad, and RStudio, ESS and StatET have visual ones. Don't use print() lines to debug your code!

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