# 2 R

• The version of R used to make this document is 4.2.1.

• The version of the rmarkdown package used to make this document is 2.19.

• The version of the magrittr package used to make this document is 2.0.3.

• The version of the CatDataAnalysis package used to make this document is 0.1.5.

• The version of the glmbb package used to make this document is 0.5.1.

# 4 Some Keywords

## 4.1 Functional Programming

Functional programming has been defined many different ways (Wikipedia page), but at the very least definitions require that functions are first-class objects. You can do with a function whatever you can do with any other object. R has this.

## 4.2 Closures

This [closures] is the best idea in the history of programming languages.

— Douglas Crockford

Crockford was talking about Javascript, but R has the same idea. Both inherited it from Scheme (a flavor of LISP). It is the function named function, which creates functions. Technically, they are called “closures”

typeof(function(x) x)
## [1] "closure"

because they capture variables in the environment where they are defined that are used in the function definition. If that environment is the R global environment, then this works as users (some of them anyway) expect

x <- 2.5
fred <- function(y) x + y
environment(fred)
## <environment: R_GlobalEnv>
fred(pi)
## [1] 5.641593
x <- 0
fred(pi)
## [1] 3.141593
rm(x)
fred(pi)
## Error in fred(pi): object 'x' not found

If that environment is another environment, then this works as users (most of them anyway) do not expect

fred_factory <- function(x) function(y) x + y
fred <- fred_factory(2.5)
environment(fred)
## <environment: 0x55f3f5b19480>
fred(pi)
## [1] 5.641593
x <- 0
fred(pi)
## [1] 5.641593
ls(envir = environment(fred))
## [1] "x"
environment(fred)$x ## [1] 2.5 environment(fred)$x <- 0
fred(pi)
## [1] 3.141593
rm(x, envir = environment(fred))
ls(envir = environment(fred))
## character(0)
fred(pi)
## [1] 3.141593
rm(x)
fred(pi)
## Error in fred(pi): object 'x' not found

Nevertheless, closures (or functions as most R users call them) are super important. They enable many good programming practices.

## 4.3 Pure Functional Programming

A functional programming language is “pure” if all functions act like math functions: for the same inputs (arguments) they always produce the same output (value). They cannot do assignment (except to local variables inside the function that are not visible to callers of the function), input, or output. They cannot use random numbers.

Some would say that a functional programming language is “pure” only if the whole language behaves that way. Pure functions are the only feature of the language.

R is not a pure functional programming language. In R functions do everything. Even assignment is really a function

x <- 2
x
## [1] 2
assign("x", 3)
x
## [1] 3
<-
## .Primitive("<-")
<-(x, 4)
x
## [1] 4

The last bit you do not ever want to use in real life. No reader of your code would understand it. But it does show that everything that happens in R happens via a function call.

So the assign function named “<-” is not pure. Its whole reason for existence is to have a side effect: assignment. Similarly all input, output, and graphics functions are not pure. Similarly, all random number functions are not pure. Similarly, R functions date and Sys.getenv and file.create and list.files and many other functions that allow R to act as a scripting language are not pure.

Nevertheless, most R functions used for computation are pure. The only thing most R functions do that is observable from the outside is return a value. They do not refer to global variables. If they are using variables defined in their environment, then they are perhaps not strictly pure (strictly speaking), but so long as users do not modify that environment — and if it is not the R global environment, then most users will not even know that environment exists — such a function is practically pure.

## 4.4 Anonymous Functions

A function does not need a name. Like every function, the R function named “function” returns a value. In this case the value happens to be a function. Other functions can also return functions as values. Usually, these functions are created by a call to the R function named “function” inside the function returning a function as a value (as we have already seen in some of the examples above).

Like every other R object, the value returned by the R function named “function” does not get associated with a name (symbol) except by assignment.

(function() 2)()
## [1] 2

We don’t want to do trickery like the above in real life. No readers of our code would understand. But anonymous functions are quite useful as arguments to other functions, as examples below will show.

## 4.5 Immutability

Until recently, you never heard programmers talking about immutability. Most programming languages don’t have it. Some languages like Javascript have recently gotten it as a feature but not as the default. In a few languages like Haskell and Clojure, it is the default.

In R immutability has always been the default. Most R objects cannot be changed once created. Only environments and reference classes, which most R users do not understand and do not explicitly use, are mutable. All assignments mutate an environment. R function rm also mutates environments. So users mutate environments all the time, but don’t think about environments while doing so.

The less said about S4 classes in general and reference classes in particular, the better.

So R environments and R names are mutable, but most R objects are not.

x <- y <- 1:5
x[3] <- NA
x
## [1]  1  2 NA  4  5
y
## [1] 1 2 3 4 5

After the first assignment, “x” and “y” are names for the same object.

The second assignment does not mutate an object, it creates a new object and assigns it to the name “x”.

In Java or C++ the analogous code would mutate the object named both “x” and “y”. Both “x” and “y” would name the same object before and after it was mutated. Their objects are mutable. R objects (except environments and reference classes) are immutable.

This is something most programmers who have been introduced to programming via Java or C++ think is bad about R. They think it is inefficient.

But it is actually something that is very good about R. It is what makes R a language that naive users can use. Even expert Java and C++ programmers make lots of mistakes that naive R programmers cannot make because of the nature of R.

## 4.6 Recursion

In R, like in most programming languages (even Java and C++), functions can call themselves. This is called recursion. It is a valuable programming technique that can simplify many problems. It is not widely used in R, but R does have it. R functions can call themselves.

This is not even a special feature of R. Function bodies can have any valid R code that can appear anywhere in R. Function calls are part of such. Hence functions can call other functions. If the function being called from inside another function happens to be the same function, then that is what other computer languages fuss about and call “recursion”. In R, recursive function calls are no different from any other function calls.

## 4.7 Lazy Evaluation (Promises)

Like some other functional programming languages (Haskell), R has lazy evaluation. Function arguments are not evaluated until needed. If not needed, they are not evaluated at all. Section 4.3.3 of the R Language Definition explains.

Mostly R users and programmers do not need to pay attention to lazy evaluation. But occasionally they do. Section 4.3.3 of the R Language Definition gives an example where it is necessary to force evaluation to get what one wants.

There is an R function named “force” that has no effect except forcing evaluation that is useful for making clear the programmers intent. I would change the example in the section cited in the preceding paragraph so that the statement

label

is changed to

force(label)

Both have the same effect, but the latter makes the intention clearer.

Understanding lazy evaluation is the key to understanding how default arguments to functions work in tricky situations. That is illustrated by the example cited in the preceding paragraph. It is also the point of the discussion of default arguments of R function svd which are discussed in Section 7.2 of my 3701 handout on the basics of R.

## 4.8 Higher-Order Functions

Any function that takes a function as an argument or returns a function as a value is called a higher-order function. This terminology is not widely used in R. Most R functions are not higher-order functions. Most higher-order functions in R are not called higher-order functions by users.

### 4.8.1 R Functions that work on mathematical functions.

• R functions D and deriv differentiate R expressions and return R expressions. Since R expressions are not R functions, these are not higher-order functions, strictly speaking, except that deriv can be made to return a function rather than an expression by use of an optional argument.

• R functions grad and hessian and jacobian in R package numDeriv differentiate R functions (approximately) and return the values of the derivatives evaluated at specified points. These are vector or matrix valued derivatives of vector-to-scalar or vector-to-vector functions.

• R function integrate does numerical integration of a mathematical function provided to it as an R function.

• R functions nlm and optim and optimize and many other R functions that do optimization, optimize a mathematical function provided to them as R functions.

• R function uniroot finds a root of a univariate mathematical function provided to it as an R function.

• R function boot in R recommended package boot (installed by default in every installation of R) approximately simulates the sampling distribution of any estimator provided to it as an R function applied to any data using Efron’s nonparametric bootstrap.

• R function metrop in CRAN package mcmc simulates the distribution of any continuous random vector whose log unnormalized probability density function is provided to it as an R function.

• R function glm that fits generalized linear models takes an argument named family that is a function that produces a family object, but this argument can also be a family object or the name of a family function, so this is not exactly a higher-order function but more R-ish. That is, glm(y ~ x, family = binomial) works and here argument binomial is a function, so this is a higher-order function call. But glm(y ~ x, family = "binomial") and glm(y ~ x, family = binomial()) also work, and those are not higher-order function calls because argument family is not, strictly speaking, a function.

So this is a very widely used R design pattern. To tell an R function about a mathematical function, provide it an R function that evaluates that mathematical function (and that function should be pure).

### 4.8.2 R Functions of the Apply Family

R function apply and friends (eapply, lapply, mapply, rapply, sapply, tapply, and vapply in R package base and mclapply and clusterApply in R package parallel) apply an arbitrary function (provided as an R function) to each element of some compound object (vector, list, data frame, table, etc.) or on each node of a cluster in parallel programming.

These also are not usually called higher-order functions by R users (although, strictly speaking, they are higher-order functions).

### 4.8.3 R Functions of the Higher-Order Family

In languages much newer than R a bunch of terminology for higher-order functions has grown up, and R has copied it. See help("funprog") for the list. The most widely used are Map, Reduce, and Filter. Some of these are built on functions of the apply family.

### 4.8.4 Some Odds and Ends

Some other R functions take functions as arguments like sweep, aggregate, outer, and kronecker.

### 4.8.5 Functionals

This is Hadley Wickham’s name for functions that take a function argument and perhaps some other arguments and return a vector. It includes many of the higher-order functions discussed above.

### 4.8.6 Function Operators

This is Hadley Wickham’s name for functions that take a function argument and return a function value. That AFAICS doesn’t include any widely used R higher-order functions.

Oops! R function Vectorize is an example of a function operator.

### 4.8.7 Function Factories

This is Hadley Wickham’s name for functions whose only job is to create other functions, like R function fred_factory in an example above.

In Section 7.5.4 of my 3701 handout on the basics of R the R function make.logl defined in its example is a function factory (which makes log likelihood functions for gamma shape families), although I didn’t call it that when I wrote those notes.

# 5 Functional Programming versus Imperative Programming

R has the stuff of imperative programming languages, such as loops and assignment. So it isn’t just a functional programming language.

The way users trained in Java or C++ and unfamiliar with the way of R program R, it looks just like C or Java or C++. But expert R programmers familiar with the way of R use functions instead of loops. They even use much less assignment.

I used to think I used far fewer loops than most R programmers, and I was right. But since writing the 3701 notes cited above, I use even fewer loops and even more functions. I am trying to exemplify the way of R.

# 6 More on Functions, Closures, Promises, Names, Objects, and Environments

## 6.1 Objects

To understand computations in R, two slogans are helpful:

• Everything that exists is an object.
• Everything that happens is a function call.

— John Chambers, quoted in Section 6.3 of Advanced R by Hadley Wickham

Unlike in many so-called object-oriented programming languages where not everything is an object, in R everything is an object, even expressions of the language itself.

Section 2 Objects of The R language Definition covers all the kinds of R objects, but that is more than we want to know about here.

## 6.2 Names

In R, objects can have names, also called symbols,

an <- as.name("arrg")
is.name(an)
## [1] TRUE
mode(an)
## [1] "name"
typeof(an)
## [1] "symbol"
storage.mode(an)
## [1] "symbol"
class(an)
## [1] "name"

These correspond to what many users call “variables”. Where many users say the R statement

x <- 2

creates a variable x which is assigned the value 2, the pedantically correct thing to say is that the R name or symbol x has been associated with the R object which is the numeric vector having one component 2.

A quotation from the R help for R function make.names

A syntactically valid name consists of letters, numbers and the dot or underline characters and starts with a letter or the dot not followed by a number. Names such as ‘“.2way”’ are not valid, and neither are the reserved words.

The definition of a letter depends on the current locale, but only ASCII digits are considered to be digits.

To understand this we have to know what “reserved words” are, and the R help page titled “Reserved” shows them (do ?Reserved or help("Reserved") to see that page).

For example, function is a reserved word, so it cannot be a valid R symbol. So when we say “the R function named function” that is pedantically incorrect (more on this below).

Anything can be a symbol if it is put in backquotes. So

get("function")
## .Primitive("function")
function
## .Primitive("function")

But just saying function without the quotes is an incomplete R statement (R requires a function definition to follow the reserved word function).

Similarly for other bits of R syntax

+
## function (e1, e2)  .Primitive("+")
[<-
## .Primitive("[<-")

Sometimes names can be provided as character strings as arguments to other functions (like we saw in get("function") above) but this is just what certain particular functions require. That function undertakes to turn character strings into symbols or their corresponding objects. There is no automatic conversion of character strings to symbols.

outer(1:3, 4:5)
##      [,1] [,2]
## [1,]    4    5
## [2,]    8   10
## [3,]   12   15
outer(1:3, 4:5, "+")
##      [,1] [,2]
## [1,]    5    6
## [2,]    6    7
## [3,]    7    8

## 6.3 The Other Kind of Names

Names, also called symbols, are not to be confused with names, not also called symbols. Any R object can have an attribute named “names” which is helpful in indexing

x <- seq(along = letters)
names(x) <- letters
x["c"]
## c
## 3
y <- as.list(x)
y[["c"]]
## [1] 3
y$c ## [1] 3 There is a weak connection between names of the first kind and names of the second kind in that in order for y$c to work it is necessary that c be a valid symbol name (y$function is invalid, even if "function" is the name of some component of y). ## 6.4 Environments Environment is a type of R object typeof(globalenv()) ## [1] "environment" A quotation from Section 2.1.10 Environments of The R Language Definition Environments can be thought of as consisting of two things. A frame, consisting of a set of symbol-value pairs, and an enclosure, a pointer to an enclosing environment. When R looks up the value for a symbol the frame is examined and if a matching symbol is found its value will be returned. If not, the enclosing environment is then accessed and the process repeated. Environments form a tree structure in which the enclosures play the role of parents. The tree of environments is rooted in an empty environment, available through emptyenv(), which has no parent. So environments are where objects can be looked up by name, and the lookup is not just in the specified environment, but in the parent, parent of parent, parent of parent of parent, and so forth, of the specified environment. R environments are very different from all other kinds of R objects. When an environment is changed (by assignment or by R function rm) the object is modified. Unlike all other kinds of R objects, environments are mutable (being mutable is their whole raison d’etre). Compare the example below with the one in the section on immutability above. foo <- bar <- new.env(parent = emptyenv()) foo$x <- 1:5
bar$x ## [1] 1 2 3 4 5 A very special environment is the R global environment, which is returned by R function globalenv or by the R global variable (name, symbol) .GlobalEnv. This is where assignments done at top level (on the R command line) are done x <- "This is the assignment we just made" x ## [1] "This is the assignment we just made" globalenv()$x
## [1] "This is the assignment we just made"
.GlobalEnv\$x
## [1] "This is the assignment we just made"

The parent, parent of parent, and so forth, of the R global environment depends on what packages have been loaded

search()
##  [1] ".GlobalEnv"        "package:rmarkdown" "package:stats"
##  [4] "package:graphics"  "package:grDevices" "package:utils"
## [10] "package:base"
library("MASS")
search()
##  [1] ".GlobalEnv"        "package:MASS"      "package:rmarkdown"
##  [4] "package:stats"     "package:graphics"  "package:grDevices"
##  [7] "package:utils"     "package:datasets"  "package:methods"
## [10] "Autoloads"         "package:base"

## 6.5 Function Execution Environment

When a function is called (one also says invoked), local variables go in the function evaluation environment, whose parent is the function environment. By local variables we mean those that are defined in the function body and created by execution of statements in the function body.

## 6.6 Promises

A promise is another kind of R object, but not one that can be accessed by users. They can be used by users and in fact are used whenever one invokes a function that has one or more arguments.

A quotation from Section 4.3.3 Argument Evaluation of The R Language Definition

One of the most important things to know about the evaluation of arguments to a function is that supplied arguments and default arguments are treated differently. The supplied arguments to a function are evaluated in the evaluation frame of the calling function. The default arguments to a function are evaluated in the evaluation frame of the function.

It is this latter fact that makes some tricky definitions of default arguments work, like those of R function svd as discussed in Section 7.2 of my 3701 handout on the basics of R which was already referred to above in the previous section on promises.

## 6.7 Pipes

To repeat part of the quotation from John Chambers above, “everything that happens is a function call”, but that depends on what “everything” means. There is also parsing. The R parser (the part of R that figures out what to do from the code you write) does some of the work. It is true that

2 + 2
## [1] 4
+(2, 2)
## [1] 4

are equivalent R statements. They are equivalent because the R parser turns the first into the second. The code 2 + 2 means call R function + with arguments 2 and 2.

Similarly, + 2 (that is unary plus) means call R function + with one argument.

+ 2
## [1] 2
+(2)
## [1] 2

But R function + is not R function sum.

+(1, 2, 3)
## Error in +(1, 2, 3): operator needs one or two arguments
sum(1, 2, 3)
## [1] 6

The only purpose of R function + is to be the function that backs up the plus operation in code.

Similar syntax transformations are involved in turning all R code into R function calls.

All of this is preliminary to discussing a new bit of R syntax transformation that is introduced in R 4.1.0. This is the pipe operation, which is denoted |>. This is part of base R that replaces the pipe operation from R package magrittr, which appeared on CRAN in 2014 and since then has become very popular. As I write this, https://cran.r-project.org/package=magrittr lists 2203 R packages that need R package magrittr

• 58 reverse depends (48 CRAN, 10 Bioconductor)
• 1999 reverse imports (1803 CRAN, 265, Bioconductor)
• 301 reverse suggests (265 CRAN, 36 Bioconductor)

The new base R pipe operation replaces the main functionality of R package magrittr (but not all of the functionality). But it does it in a completely different way. The magrittr pipe operator is an R function %>% that uses nonstandard evaluation to do what it does (pipe the output of the function call on the left-hand side into the function call on the right-hand side). The new R pipe operation is not implemented as an R function but rather as a syntax transformation, as the help page for this new operation (help("|>")) shows in some of its examples

# the pipe operator is implemented as a syntax transformation:
quote(mtcars |> subset(cyl == 4) |> nrow())
## nrow(subset(mtcars, cyl == 4))
quote(x |> (function(x) x + 1)())
## (function(x) x + 1)(x)

Or more simply

quote(x |> f(y))
## f(x, y)

The R code x |> f(y) is literally turned into the code f(x, y) before anything else happens. The right-hand side of of a pipe expression must be a function call. It is an error for it to be a function definition or a function name.

mtcars |> class
## Error: The pipe operator requires a function call as RHS

The way to do this is to turn the right-hand side into a function call rather than a function name.

mtcars |> class()
## [1] "data.frame"

Note that this applies to anonymous functions too. You cannot put an anonymous function in a pipeline, only the evaluation of an anonymous function

"Foo!" |> function(x) x
## Error: function 'function' not supported in RHS call of a pipe
"Foo!" |> (function(x) x)()
## [1] "Foo!"

New in R-4.2.0 is the “placeholder” option in the right-hand-side (RHS) of a pipe expression. The placeholder character is the underscore character, and this can be used exactly once in the RHS as the value of a named argument (the argument must be named) so say that the input is piped into this argument rather than the first argument. This is a magritter feature that the core R pipe operator did not have before R-4.2.0 and was one of the most frequent reasons for needing an anonymous function, Use of the placeholder is illustrated in the following example.

So why is this popular? R function %>% in R package magrittr is already very popular. R operation |> in base R is much cleaner, much easier to debug, and much more efficient. So it should eventually replace most usages of %>%. It allows complicated nested function calls to be read left to right rather than inside out. Here is the first example from the magrittr package vignette. Here it is in magrittr

library(magrittr)
car_data <-
mtcars %>%
subset(hp > 100) %>%
aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
transform(kpl = mpg %>% multiply_by(0.4251)) %>%
print
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

And here it is rewritten to use |> instead of %>%.

car_data <-
mtcars |>
subset(hp > 100) |>
aggregate(. ~ cyl, data = _,
FUN = function(x) x |> mean() |> round(2)) |>
transform(kpl = mpg |> (function(x) x * 0.4251)()) |>
print()
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

and this is (supposedly) easier to read (see below) than without pipes

print(car_data <-
transform(aggregate(. ~ cyl,
data = subset(mtcars, hp > 100),
FUN = function(x) round(mean(x), 2)),
kpl = mpg * 0.4251))
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

Our rewritten example is a bit clumsy because the magrittr example uses pipes pointlessly in some cases just to be using them. If we get rid of some of the pipes, we can simplify our example.

car_data <-
mtcars |>
subset(hp > 100) |>
aggregate(. ~ cyl, data = _,
FUN = function(x) mean(x) |> round(2)) |>
transform(kpl = mpg * 0.4251) |>
print()
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

But we still have an anonymous function definition that magrittr does not need (it is hidden inside R function %>%). Along with the pipe operation R 4.1.0 introduced a new syntax for anonymous function definitions that replaces function ( with \(. Using this, our example can be shortened to

car_data <-
mtcars |>
subset(hp > 100) |>
aggregate(. ~ cyl, data = _,
FUN = \(x) x |> mean() |> round(2)) |>
transform(kpl = mpg * 0.4251) |>
print()
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

Whether you like this stuff or not is a matter of taste. Pipes are function composition

quote(x |> f() |> g() |> h())
## h(g(f(x)))

And function composition is the most important method of structuring programs, far more important than object orientation and other such fads. In fact, favor composition over inheritance has become a slogan of object-oriented design. It says: don’t use the object-orientated feature (inheritance), instead use composition (which is not particularly object-oriented), because inheritance, despite being a key feature of object orientation, has many undesirable properties.

The oldest AFAIK method of structuring programs (at least higher-level language programs as opposed to assembly language programs, which no one writes today) is assignment. Consequently, it is the most familiar method, especially to R users. We can structure this example as

foo <- subset(mtcars, hp > 100)
bar <- aggregate(. ~ cyl, foo, FUN = function(x) round(mean(x), 2))
car_data <- transform(bar, kpl = mpg * 0.4251)
car_data
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

And we note

• the silliness of having one column rounded differently than the rest and

• the silliness of storing rounded data, where it may lead to inaccuracy if ever used.

Should be

foo <- subset(mtcars, hp > 100)
bar <- aggregate(. ~ cyl, foo, mean)
car_data <- transform(bar, kpl = mpg * 0.4251)
round(car_data, 2)
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb   kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.01
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.39
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.42

So now that we have used assignment to clean up this magrittr example we can turn it back into pipes and it stays cleaner.

car_data <- mtcars |> subset(hp > 100) |> aggregate(. ~ cyl, data = _, mean) |>
transform(kpl = mpg * 0.4251)
round(car_data, 2)
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb   kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.01
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.39
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.42

Like it or not, use it or not, you will be seeing this in other people’s code.

A pure functional language does not have assignment. Its only method of program structuring is definition of functions and composition of functions. Pipelines are just composition in disguise. So pipelining is more “functional” than anything else you can do in R (except actual composition: function calls as arguments to function calls).

As we can see from the example (not my example, magrittr’s example) pipelines are not really easier to read than code with assignments, especially for naive users. They are more functional. They do avoid the clutter of a lot of unmotivated temporary variables. But whether you use them or not is up to you. It’s just a matter of taste.

I myself have not gotten on the pipeline bandwagon (in R, I have been using UNIX pipes for 35 years). This is partly because I do not like to require very recent versions of R for my CRAN packages (some users are slow to update). But also I do not like the ugly syntax required to use anonymous functions in pipes (but a lot of this ugliness is no longer required because of the new placeholder syntax). I expect that I will eventually start using pipes and they will be base R pipes rather than magrittr pipes (and this despite the super cool name of that package).

It might be an interesting exercise to point out where in my notes pipelines might make code a lot neater.

One more example, this time from my categorical data analysis notes but redone to use pipes.

library(CatDataAnalysis)
library(glmbb)
data(table_8.1)
gout <- table_8.1 |>
transform(lake = factor(lake,
labels = c("Hancock", "Oklawaha", "Trafford", "George"))) |>
transform(gender = factor(gender, labels = c("Male", "Female"))) |>
transform(size = factor(size, labels = c("<=2.3", ">2.3"))) |>
transform(food = factor(food,
labels = c("Fish", "Invertebrate", "Reptile", "Bird", "Other"))) |>
glmbb(big = count ~ lake * gender * size * food,
little = ~ lake * gender * size + food,
family = poisson, data = _)
## Warning: glm.fit: fitted rates numerically 0 occurred

## Warning: glm.fit: fitted rates numerically 0 occurred
summary(gout)
##
## Results of search for hierarchical models with lowest AIC.
## Search was for all models with AIC no larger than min(AIC) + 10
## These are shown below.
##
##   criterion  weight    formula
##   288.0      0.903304  count ~ lake*food + size*food + lake*gender*size
##   293.7      0.050072  count ~ lake*food + gender*food + size*food + lake*gender*size
##   294.9      0.028388  count ~ lake*gender*size + lake*size*food
##   296.8      0.010643  count ~ size*food + lake*gender*size + lake*gender*food
##   297.5      0.007593  count ~ gender*food + lake*gender*size + lake*size*food

(But I have not changed my 5421 notes to use the pipe and placeholder syntax. I don’t want to require students to understand that.)

## 6.8 Named Arguments, Default Values for Arguments, Missing Arguments, Dot-Dot-Dot Arguments

These are covered in Section 7 of the 3701 lecture notes cited above.