Coding practice in R : what are the advantages and

问题:

The recent questions regarding the use of require versus :: raised the question about which programming styles are used when programming in R, and what their advantages/disadvantages are. Browsing through the source code or browsing on the net, you see a lot of different styles displayed.

The main trends in my code :

• heavy vectorization I play a lot with the indices (and nested indices), which results in rather obscure code sometimes but is generally a lot faster than other solutions. eg: x[x < 5] <- 0 instead of x <- ifelse(x < 5, x, 0)

• I tend to nest functions to avoid overloading the memory with temporary objects that I need to clean up. Especially with functions manipulating large datasets this can be a real burden. eg : y <- cbind(x,as.numeric(factor(x))) instead of y <- as.numeric(factor(x)) ; z <- cbind(x,y)

• I write a lot of custom functions, even if I use the code only once in eg. an sapply. I believe it keeps it more readible without creating objects that can remain lying around.

• I avoid loops at all costs, as I consider vectorization to be a lot cleaner (and faster)

Yet, I've noticed that opinions on this differ, and some people tend to back away from what they would call my "Perl" way of programming (or even "Lisp", with all those brackets flying around in my code. I wouldn't go that far though).

What do you consider good coding practice in R?

What is your programming style, and how do you see its advantages and disadvantages?

回答1:

What I do will depend on why I am writing the code. If I am writing a data analysis script for my research (day job), I want something that works but that is readable and understandable months or even years later. I don't care too much about compute times. Vectorizing with lapply et al. can lead to obfuscation, which I would like to avoid.

In such cases, I would use loops for a repetitive process if lapply made me jump through hoops to construct the appropriate anonymous function for example. I would use the ifelse() in your first bullet because, to my mind at least, the intention of that call is easier to comprehend than the subset+replacement version. With my data analysis I am more concerned with getting things correct than necessarily with compute time --- there are always the weekends and nights when I'm not in the office when I can run big jobs.

For your other bullets; I would tend not to inline/nest calls unless they were very trivial. If I spell out the steps explicitly, I find the code easier to read and therefore less likely to contain bugs.

I write custom functions all the time, especially if I am going to be calling the code equivalent of the function repeatedly in a loop or similar. That way I have encapsulated the code out of the main data analysis script into it's own .R file which helps keep the intention of the analysis separate from how the analysis is done. And if the function is useful I have it for use in other projects etc.

If I am writing code for a package, I might start with the same attitude as my data analysis (familiarity) to get something I know works, and only then go for the optimisation if I want to improve compute times.

The one thing I try to avoid doing, is being too clever when I code, whatever I am coding for. Ultimately I am never as clever as I think I am at times and if I keep things simple, I tend not to fall on my face as often as I might if I were trying to be clever.

回答2:

I write functions (in standalone .R files) for various chunks of code that conceptually do one thing. This keeps things short and sweet. I found debugging somewhat easier, because traceback() gives you which function produced an error.

I too tend to avoid loops, except when its absolutely necessary. I feel somewhat dirty if I use a for() loop. :) I try really hard to do everything vectorized or with the apply family. This is not always the best practice, especially if you need to explain the code to another person who is not as fluent in apply or vectorization.

Regarding the use of require vs ::, I tend to use both. If I only need one function from a certain package I use it via ::, but if I need several functions, I load the entire package. If there's a conflict in function names between packages, I try to remember and use ::.

I try to find a function for every task I'm trying to achieve. I believe someone before me has thought of it and made a function that works better than anything I can come up with. This sometimes works, sometimes not so much.

I try to write my code so that I can understand it. This means I comment a lot and construct chunks of code so that they somehow follow the idea of what I'm trying to achieve. I often overwrite objects as the function progresses. I think this keeps the transparency of the task, especially if you're referring to these objects later in the function. I think about speed when computing time exceeds my patience. If a function takes so long to finish that I start browsing SO, I see if I can improve it.

I found out that a good syntax editor with code folding and syntax coloring (I use Eclipse + StatET) has saved me a lot of headaches.

Based on VitoshKa's post, I am adding that I use capitalizedWords (sensu Java) for function names and fullstop.delimited for variables. I see that I could have another style for function arguments.

回答3:

Naming conventions are extremely important for the readability of the code. Inspired by R's S4 internal style here is what I use:

• camelCase for global functions and objects (like doSomething, getXyyy, upperLimit)
• functions start with a verb
• not exported and helper functions always start with "."
• local variables and functions are all in small letters and in "_" syntax (do_something, get_xyyy), It makes it easy to distinguish local vs global and therefore leads to a cleaner code.

回答4:

For data juggling I try to use as much SQL as possible, at least for the basic things like GROUP BY averages. I like R a lot but sometimes it's not only fun to realize that your research strategy was not good enough to find yet another function hidden in yet another package. For my cases SQL dialects do not differ much and the code is really transparent. Most of the time the threshold (when to start to use R syntax) is rather intuitive to discover. e.g.

require(RMySQL)
# selection of variables alongside conditions in SQL is really transparent
# even if conditional variables are not part of the selection
statement = "SELECT id,v1,v2,v3,v4,v5 FROM mytable
             WHERE this=5
             AND that != 6" 
mydf <- dbGetQuery(con,statement)
# some simple things get really tricky (at least in MySQL), but simple in R
# standard deviation of table rows
dframe$rowsd <- sd(t(dframe))

So I consider it good practice and really recommend to use a SQL database for your data for most use cases. I am also looking into TSdbi and saving time series in relational database, but cannot really judge that yet.