Can someone give me explanation for the below sentence highlighted in Bold.
"Primitive functions are only found in the base package, and since they operate at a low level, they can be more efficient (primitive replacement functions don’t have to make copies), and can have different rules for argument matching (e.g., switch and call). This, however, comes at a cost of behaving differently from all other functions in R. Hence the R core team generally avoids creating them unless there is no other option.
Source Link:http://adv-r.had.co.nz/Functions.html#lexical-scoping
Related
TLDR
Does rigorous best practice recommend that a proactive R developer explicitly disambiguate all base functions — even the ubiquitously common functions like c() or cat() — within the .R files of their package, using the package::function() convention?
Context
Though a novice developer, I need to create a (proprietary) package in R. The text R Packages (by the authoritative authors Hadley Wickham and Jenny Bryan) has proven extremely helpful (if occasionally deprecated).
I am keen on following best practices from the start, so as to save myself time and effort down the road. As described in the text, the use of the :: operator can prevent current and
future conflicts, by disambiguating functions whose names are overloaded. To wit, the authors are careful to introduce each function with the package::function() convention, and they recommend its general use within the .R files of one's package.
However, their code examples often call functions that hail from the base package yet are unaccompanied by base::. Many base functions, like the ubiquitous c() or cat(), are used by R programmers in their sleep and (I imagine) are unlikely to ever be overloaded by a presumptuous developer. Nonetheless, it is confusing to see (for example) the juxtaposition of base::with() against (the base function) print(), all within a few lines of text.
...(These functions are inspired by how base::with() works.)
f <- function(x, sig_digits) {
# imagine lots of code here
withr::with_options(
list(digits = sig_digits),
print(x)
)
# ... and a lot more code here
}
I understand that the purpose of base::with() is to unambiguously introduce the with() function to the reader. However, the absence of base:: (within the code itself) seems to stick out like a sore thumb, when the package is explicitly named for any function called from any other package. Given my inexperience, I don't feel comfortable assuming the authors' intent.
Question
Are the names of base functions sufficiently unique that using this convention — of calling base::function() for every function() within the base package — would not be worth it? That the risk of overloading the functions (at some point in the future) is far outweighed by the inconvenience (and sheer ugliness) of
my_vector <- base::c(1, 2, 3)
throughout one's .R files? If not, is there an established convention that would balance unambiguity with elegance?
As always, I am grateful for any help, especially on this, my first post to Stack Overflow.
There are some cases where the magrittr pipe (%>%) doesn't behave quite as may be expected, and that therefore take a little time to debug, generate reprex's, discuss, and eventually refactor.
E.g.
piping to ls() and
piping to return()
Question
In the interest of proactively educating oneself, is there some compilation of known base (and perhaps tidy) R functions with which the pipe doesn't behave as one may expect?
Current solutions
We can periodically read the magrittr issues (and probably issues with other related tidy packages), and
Read relevant stack overflow questions.
Although these sources are somewhat disparate.
Is there a way to generate a list, or perhaps one is maintained somewhere, perhaps in documentation, on github, or elsewhere, so that it can be periodically reviewed?
There isn’t a way to generate such a list automatically, since you effectively need to know the semantics of a function to know why (and therefore whether) it would work inside a pipeline.
As a heuristic, any function that takes an envir parameter that’s set to a default (as in the case of ls, bit also get, exists, etc.) will behave oddly with pipes.
That said, if you understand how function evaluation works in R it is usually pretty obvious for most functions whether they will work. I therefore suggest reading up on the R model of evaluation. In particular, read the relevant chapters in Hadley Wickham’s Advanced R:
Chapter 6 Functions
Chapter 7 Environments
And, potentially, parts of
Part IV Metaprogramming
To be fair, that’s a lot of material. But having a good handle of how function evaluation and scoping (environments) work in R is crucial for a solid understanding of R anyway. Metaprogramming is more advanced and it’s more important to be aware of its existence than to immediately have a solid understanding of it.
In Python we have chance to import a certain function from a library with a command "import function from library as smth. Do we have something similar in R?
I know that we can call the function like "library::function()", my question mostly refers to the "as" part.
It is not common and not necessary to do this in R. The assignment operator <- can be used to give a new name to an existing function. For example, one could define a function that does exactly the same as lubridate's, year() function with:
asYear <- lubridate::year
One could argue that, by doing so, the year() function has been "imported" from the lubridate package and that it is now called asYear(). In fact, the new function does just the same (which is no surprise, simply because it is the same):
asYear(Sys.Date())
#[1] 2016
So it is possible to construct an analogy to "from package import as", but it is not a good idea to do this. Here are a few reasons I can think of:
Debugging a code where library functions have been renamed will be
much more difficult.
The documentation is not available for the renamed function. In this example, ?asYear won't work, in contrast to ?lubridate::year or library(lubridate); help(year).
The function is not only renamed but it is copied, which clutters the environment and is inefficient in terms of memory usage.
The maintenance of the code becomes unnecessarily difficult. If another programmer (or the original programmer a few years later) looks at a code containing such a redefinition of a function, it will be harder for her or him to understand what this function is doing.
There are probably more reasons, but I hope that this is sufficient to discourage the use of such a construction. Different programming languages have different peculiarities, and as a programmer it is necessary to adapt to them. What is common in Python can be awkward in R, and vice versa.
A simple and commonly used way to handle such a standard situation in R is to use library() to load the entire namespace of the package containing the requested function:
library (lubridate)
year(Sys.Date())
However, one should be aware of possible namespace clashes, especially if many libraries are loaded simultaneously. Different functions could be defined with the same name in different packages. A well-known example thereof are the contrasting implementations of the lag() function in the dplyr and stats package.
In such cases one can use the double colon operator :: to resolve the namespace that should be addressed. This would be similar to the use of "from" in the case of "import", but such a specification would be needed each time the function is called.
lubridate::year(Sys.Date())
#[1] 2016
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?
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.
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.
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.
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.
I've just started learning Common Lisp--and rapidly falling in love with it--and I've just moved onto the type system. I seem to be developing a particular fondness for applicative programming.
As I understand it, in CL strings and lists are both sequences, but there don't seem to be any standard functions for mapping over a sequence, only lists. I can see why they would be supplied for lists, what with them being the fundamental datatype and all, but why was it not designed to work with sequences? As they are a more general type, it would seem more useful to target applicative functions at them rather than lists. Or am I completely misunderstandimatifying how it works?
Edit:
What I was feeling particularly confused about was the way that sequences -- the abstraction -- and lists -- an implementation -- seem to be muddled up in CL. The consensus seems to be that this is for historical reasons; lisp has been around so long that you can pretty much map out the development of software engineering practices through its functions and macros; which functions apply to sequences and which to lists seems arbitrary at first glance because CL has a mixture of pre-sequence-abstraction functions that operate only on lists, and functions that do the same thing in a more general way on sequences. As someone who is just learning CL at the moment, I think it would be useful if authors introduced sequences first as the cleaner abstraction, and then bought in lists as the most fundamental implementation of that abstraction. Lists would still be needed as syntax of course, but by the time it is necessary to state this explicitly many readers would have worked this out by themselves, which would be quite an ego boost when starting out.
Why, there are a lot of functions working on sequences. Mapping over a sequence is done with MAP or MAP-INTO.
Look at the sequences section of the CLHS to find out more.
There is also a quick reference that is nicely organized.
Well, you are generally correct. Most functions do indeed focus on lists (mapcar, find, count, remove, append etc.) For a few of these there are equivalent functions for sequences (concatenate, some and every come to mind), and some, where the list-equivalent is outdated (eg. nth for lists only vs. elt for all sequences). Some functions simply work on sequences (length, for example).
CL is a bit of a mess. It's a big language, as in huge. Over 700 functions, AFAIK. And it's old. Some of these functions are deprecated by convention, and others are rarely, if ever, used.
Yes, it would be more useful to have mapping functions be methods, that applied as intended on all sequences. CL was simply not built that way. If it were to be built again today, I'm sure this would be considered, and it would look very different.
That said, you are not left completely in the cold. The loop macro works on sequences, as does iterate (a separate looping macro, which i happen to like more). This will get you far. For most practical purposes you will be using lists, and this won't be more than a pragmatic problem. If you do happen to lack a mapping function for vectors (or sequences in general), who's to stop you from writing it?