# 8. Flow of execution#

This open-access textbook is, and will remain, freely available for everyone’s enjoyment (also in PDF; a paper copy can also be ordered). It is a non-profit project. Although available online, it is a whole course, and should be read from the beginning to the end. Refer to the Preface for general introductory remarks. Any bug/typo reports/fixes are appreciated. Make sure to check outMinimalist Data Wrangling with Python[27] too.

The **ifelse** and **Map** functions are potent.
However, they allow us to process only the *consecutive* elements
in a vector.

Thus, below we will (finally!) discuss different ways to alter a program’s
control flow manually, based on some criterion, and to evaluate the same
expression many times, but perhaps on different data.
Nevertheless, before proceeding any further, let’s contemplate the fact
that we have managed *without* them for such a long time,
despite the fact that the data processing exercises we learnt to solve
were far from trivial.

## 8.1. Conditional evaluation#

Life is full of surprises, so it would be nice if we were able to adapt to any future challenges.

The following evaluates a given `expression`

*if and only if*
a logical `condition`

is true.

```
if (condition) expression
```

When performing some `other_expression`

is preferred
rather than doing nothing in the case of the `condition`

’s being false,
we can write:

```
if (condition) expression else other_expression
```

For instance:

```
(x <- runif(1)) # to spice things up
## [1] 0.28758
if (x > 0.5) cat("head\n") else cat("tail\n")
## tail
```

Many expressions can, of course, be grouped with curly braces,
`**{**`.

```
if (x > 0.5) {
cat("head\n")
x <- 1
} else { # do not put newline before else!
cat("tail\n")
x <- 0
}
## tail
print(x)
## [1] 0
```

Important

At the top level, we should not put a new line before **else**.
Otherwise, we will get an error like `Error: unexpected 'else' in "else"`

.
This is because the interpreter enthusiastically executes the statements
read line by line as soon as it regards them
as standalone expressions.
In this case, we first get an **if** without **else**,
and then, separately, a *dangling* **else** without the preceding
**if**.

This is not an issue when a conditional statement is part of an expression group as the latter is read in its entirety.

```
function (x)
{ # opening bracket – start
if (x > 0.5)
cat("head\n")
else # not dandling because {...} is read as a whole
cat("tail\n")
} # closing bracket – expression ends
```

As an exercise, try removing the curly braces and see what happens.

### 8.1.1. Return value#

`**if**` is a function (compare Section 9.3).
Hence, it has a return value: the result of evaluating the
conditional expression.

```
(x <- runif(1))
## [1] 0.28758
y <- if (x > 0.5) "head" # no else
print(y)
## NULL
y <- if (x > 0.5) "head" else "tail"
print(y)
## [1] "tail"
```

This is particularly useful when a call to `**if**`
is the last expression in a curly brace-delimited code block
that constitutes a function’s body.

```
mint <- function(x)
{
cond <- (x > 0.5) # could be something more sophisticated
if (cond) # the last expression in the code block
"head" # this can be the return value...
else
"tail" # or this one, depending on the condition
}
mint(x)
## [1] "tail"
unlist(Map(mint, runif(5)))
## [1] "tail" "head" "tail" "head" "head"
```

Add-on packages can be loaded using **requireNamespace**.
Contrary to **library**, the former does not fail
when a package is not available. Also, it does not attach
it to the search path; see Section 16.2.6.
Instead, it returns a logical value indicating if the package is available
for use. This can be helpful in situations where the availability
of some features depends on the user environment’s configuration:

```
process_data <- function(x)
{
if (requireNamespace("some_extension_package", quietly=TRUE))
some_extension_package::very_fast_method(x)
else
normal_method(x)
}
```

### 8.1.2. Nested **if**s#

If more than two test cases are possible, i.e.,
when we need to go beyond either `condition`

or
`!condition`

, then we can use the following construct:

```
if (a) {
expression_a
} else if (b) {
expression_b
} else if (c) {
expression_c
} else {
expression_else
}
```

This evaluates all conditions `a`

, `b`

, … (in this order)
until the first positive case is found
and then executes the corresponding `expression`

.

It is worth stressing that the foregoing is nothing else than a series
of nested **if** statements but written in a more
readable[1] manner:

```
if (a) {
expression_a
} else {
if (b) {
expression_b
} else {
if (c) {
expression_c
} else {
expression_else
}
}
}
```

Write a function named **sign** that determines
if a given numeric value is `"positive"`

, `"negative"`

, or `"zero"`

.

### 8.1.3. Condition: Either `TRUE`

or `FALSE`

#

**if** expects a `condition`

that is a single, well-defined logical
value, either `TRUE`

or `FALSE`

.
Thence, problems may arise when this is not the case.

If the `condition`

is of length not equal to one,
we get an error:

```
if (c(TRUE, FALSE)) cat("spam\n")
## Error in if (c(TRUE, FALSE)) cat("spam\n"): the condition has length > 1
if (logical(0)) cat("bacon\n")
## Error in if (logical(0)) cat("bacon\n"): argument is of length zero
```

We cannot pass a missing value either:

```
if (NA) cat("ham\n")
## Error in if (NA) cat("ham\n"): missing value where TRUE/FALSE needed
```

Important

If we think that we are immune to writing code violating the preceding
constraints, just we
wait until the `condition`

becomes a function of data
for which there is no sanity-checking in place.

```
mint <- function(x)
if (x > 0.5) "head" else "tail"
mint(0.25)
## [1] "tail"
mint(runif(5))
## Error in if (x > 0.5) "head" else "tail": the condition has length > 1
mint(log(rnorm(1))) # not obvious, only triggered sometimes
## Warning in log(rnorm(1)): NaNs produced
## Error in if (x > 0.5) "head" else "tail": missing value where TRUE/FALSE
## needed
```

Chapter 9 will be concerned with ensuring
input data integrity so that such cases will either fail
gracefully or succeed bombastically.
In the above example, we should probably verify that `x`

is a single finite
numeric value. Alternatively, we might need to apply
**ifelse**, **all**, or **any**.

Interestingly, conditions other that logical are coerced:

```
x <- 1:5
if (length(x)) # i.e., length(x) != 0, but way less readable
cat("length is not zero\n")
## length is not zero
```

Recall that coercion of numeric to logical yields `FALSE`

if and
only if the original value is zero.

### 8.1.4. Short-circuit evaluation#

Especially for formulating logical conditions
in **if** and **while** (see below),
we have the *scalar* `**||**` (alternative) and
`**&&**` (conjunction) operators.

```
FALSE || TRUE
## [1] TRUE
NA || TRUE
## [1] TRUE
```

Contrary to their vectorised counterparts (`**|**`
and `**&**`),
the scalar operators are lazy (Chapter 17) in the sense
that they evaluate the first operand and then determine if the computing
of the second one is necessary
(because, e.g., `FALSE && whatever`

is always `FALSE`

anyway).

Therefore,

```
if (a && b)
expression
```

is equivalent to:

```
if (a) {
if (b) { # compute b only if a is TRUE
expression
}
}
```

and:

```
if (a || b)
expression
```

corresponds to:

```
if (a) {
expression
} else if (b) { # compute b only if a is FALSE
expression
}
```

For instance,
“**is.vector**`(x) && `

**length**`(x) > 0 && x[[1]] > 0`

”
is a risk-free test. It takes into account that `x[[1]]`

has
the desired meaning only for objects that are nonempty vectors.

Some other examples:

```
{cat("spam"); FALSE} || {cat("ham"); TRUE} || {cat("cherries"); FALSE}
## spamham
## [1] TRUE
{cat("spam"); TRUE} && {cat("ham"); FALSE} && {cat("cherries"); TRUE}
## spamham
## [1] FALSE
```

Recall that the expressions within the curly braces are evaluated one after another and that the result is determined by the last value in the series.

Study the source code of
**isTRUE** and **isFALSE**
and determine if these functions could be useful in
formulating the conditions within the **if** expressions.

## 8.2. Exception handling#

Exceptions are exceptional, but they may happen and break stuff. For instance, we are in deep skit when the internet connection drops while we try to download a file, an optimisation algorithm fails to converge, or:

```
read.csv("/path/to/a/file/that/does/not/exist")
## Warning in file(file, "rt"): cannot open file '/path/to/a/file/that/does/
## not/exist': No such file or directory
## Error in file(file, "rt"): cannot open the connection
```

Three types of *conditions* are frequently observed:

*errors*stop the flow of execution,*warnings*are not critical, but can be turned into errors (see`warn`

in**option**),*messages*transmit diagnostic information.

They can be manually triggered using the
**stop**, **warning**, and **message** functions.

Errors (but warnings too) can be handled by means of the
**tryCatch** function, amongst others.

```
tryCatch({ # block of expressions to execute, until an error occurs
cat("a...\n")
stop("b!") # error – breaks the linear control flow
cat("c?\n")
},
error = function(e) { # executed immediately on an error
cat(sprintf("[error] %s\n", e[["message"]]))
},
finally = { # always executed at the end, regardless of error occurrence
cat("d.\n")
}
)
## a...
## [error] b!
## d.
```

The two other conditions can be ignored by calling
**suppressWarnings** and
**suppressMessages**.

```
log(-1)
## Warning in log(-1): NaNs produced
## [1] NaN
suppressWarnings(log(-1)) # yeah, yeah, we know what we're doing
## [1] NaN
```

At the time of writing this book,
when the **data.table** package is attached,
it emits a message.
Call **suppressMessages** to silence it.
Note that consecutive calls to **library** do not
reload an already loaded package. Therefore, the message will only
be seen once per R session.

Related functions include **stopifnot** discussed
in Section 9.1 and **on.exit** mentioned
in Section 17.4; see Section 9.2.4
for some code debugging tips.

## 8.3. Repeated evaluation#

And now for something completely different… time for the elephant in the room!

We have been able to manage without loops so far
(and will be quite all right in the second part of the book too).
This is because many data processing tasks can be written in terms
of vectorised operations such as
`**+**`,
**sqrt**,
**sum**,
`**[**`,
**Map**, and
**Reduce**.
Oftentimes, compared to their loop-based counterparts, they are more
readable and efficient. We will explore this in the coming exercises.

However, at times, using an explicit **while** or **for** loop
might be the only natural way to solve a problem,
for instance, when processing chunks of data streams.
Also, an explicitly “looped” algorithm may occasionally
have better[2] time or memory complexity.

### 8.3.1. **while**#

**if** considers a logical condition provided and determines
whether to execute a given statement. On the other hand:

```
while (condition) # single TRUE or FALSE, as in `if`
expression
```

evaluates a given `expression`

*as long as* the logical `condition`

is true.
Therefore, it is advisable to make the `condition`

dependent on
some variable that the `expression`

can modify.

```
i <- 1
while (i <= 3) {
cat(sprintf("%d, ", i))
i <- i + 1
}
## 1, 2, 3,
```

Nested loops are possible too:

```
i <- 1
while (i <= 2) {
j <- 1
while (j <= 3) {
cat(sprintf("%d %d, ", i, j))
j <- j + 1
}
cat("\n")
i <- i + 1
}
## 1 1, 1 2, 1 3,
## 2 1, 2 2, 2 3,
```

Implement a simple linear congruential pseudorandom number generator that, given some seed \(X_0\in [0, m)\), outputs a sequence \((X_1,X_2,\dots)\) defined by:

with, e.g., \(a=75\), \(c=74\), and \(m=2^{16}+1\)
(here, *mod* is the division remainder, `**%%**`).
This generator has poor statistical properties
and its use in practice is discouraged.
In particular, after a rather small number of iterations \(k\),
we will find a cycle such that \(X_k=X_1, X_{k+1}=X_2, \dots\).

### 8.3.2. **for**#

The for-each loop:

```
for (name in vector)
expression
```

takes each element, from the beginning to the end,
in a given `vector`

, assigns it some `name`

, and evaluates the `expression`

.
For example:

```
fridge <- c("spam", "spam", "bacon", "eggs")
for (food in fridge)
cat(sprintf("%s, ", food))
## spam, spam, bacon, eggs,
```

Another example:

```
for (i in 1:length(fridge)) # better: seq_along(fridge); see below
cat(sprintf("%s, ", fridge[i]))
## spam, spam, bacon, eggs,
```

One more:

```
for (i in 1:2) {
for (j in 1:3)
cat(sprintf("%d %d, ", i, j))
cat("\n")
}
## 1 1, 1 2, 1 3,
## 2 1, 2 2, 2 3,
```

The iterator still exists after the loop’s watch has ended:

```
print(i)
## [1] 2
print(j)
## [1] 3
```

Important

Writing:

```
for (i in 1:length(x))
print(x[i])
```

is reckless. If `x`

is an empty vector, then we will observe undesired
behaviour because we ask to iterate over `1:0`

:

```
x <- logical(0)
for (i in 1:length(x))
print(x[i])
## [1] NA
## logical(0)
```

Recall from Chapter 5 that
`x[1]`

tries to access an out-of-bounds element here,
and `x[0]`

returns nothing.

We generally suggest replacing
`1:`

**length**`(x)`

with **seq_along**`(x)`

or
**seq_len**`(`

**length**`(x))`

.
wherever possible.

Note

The preceding model **for** loop is roughly equivalent to:

```
name <- NULL
tmp_vector <- vector
tmp_iter <- 1
while (tmp_iter <= length(tmp_vector)) {
name <- tmp_vector[[tmp_iter]]
expression
tmp_iter <- tmp_iter + 1
}
```

Note that the `tmp_vector`

is determined before the loop itself.
Hence, any changes to the `vector`

will not influence the execution flow.
Furthermore, due to the use of `**[[**`, the loop can also
be applied on lists.

Let `x`

be a list and **f** be a function.
The following code generates the same result
as **Map**`(`

**f**`, x)`

:

```
n <- length(x)
ret <- vector("list", n) # a new list of length `n`
for (i in seq_len(n))
ret[[i]] <- f(x[[i]])
```

Let `x`

and `y`

be two lists and **f** be a function.
Here is the most basic version of
**Map**`(`

**f**`, x, y)`

.

```
nx <- length(x)
ny <- length(y)
n <- max(nx, ny)
ret <- vector("list", n)
for (i in seq_len(n))
ret[[i]] <- f(x[[((i-1)%%nx)+1]], y[[((i-1)%%ny)+1]])
```

Note that `x`

and `y`

might be of different lengths.
Feel free to upgrade this code by adding a warning like
*the longer argument is not a multiple of the length of the shorter one*.
Also, rewrite it without using the modulo operator, `**%%**`.

### 8.3.3. **break** and **next**#

**break** can be used to escape the current loop.
**next** skips the remaining expressions and advances
to the next iteration (where the testing of
the logical condition occurs).

Here is a rather random example:

```
x <- c(10, 0.03, 0.04, 1, 0.001, 0.05)
s <- 0
for (e in x) {
if (e > 0.1) # skip the current element if it is greater than 0.1
next
print(e)
if (e < 0.01) # stop at the first element less than 0.01
break
s <- s + e
}
## [1] 0.03
## [1] 0.04
## [1] 0.001
print(s)
## [1] 0.07
```

We have used a frequently occurring design pattern:

```
for (e in x) {
if (condition)
next
many_statements...
}
```

which is equivalent to:

```
for (e in x) {
if (!condition) {
many_statements...
}
}
```

but which avoids introducing a nested block of expressions.

Note

(*) There is a third loop type,

```
repeat
expression
```

which is a shorthand for

```
while (TRUE)
expression
```

i.e., it is a possibly infinite loop. Such constructs are invaluable when
expressing situations like *repeat*-something-*until*-success,
e.g., when we want to execute a command at least once.

```
i <- 1
repeat { # while (TRUE)
# simulate dice casting until we throw "1"
if (runif(1) < 1/6) break # repeat until this
i <- i+1 # how many times until success
}
print(i)
## [1] 6
```

What is wrong with the following code?

```
j <- 1
while (j <= 10) {
if (j %% 2 == 0) next
print(j)
j <- j + 1
}
```

What about this one?

```
j <- 1
while (j <= 10);
j <- j + 1
```

### 8.3.4. **return**#

**return**, when called from within a function,
immediately yields a specified value and goes back to the caller.

For example, here is a simple recursive function that flattens a given list:

```
my_unlist <- function(x)
{
if (is.atomic(x))
return(x)
# so if we are here, x is definitely not atomic
out <- NULL
for (e in x)
out <- c(out, my_unlist(e))
out # or return(out); not necessary as it's the last expression
}
my_unlist(list(list(list(1, 2), 3), list(4, list(5, list(6, 7:10)))))
## [1] 1 2 3 4 5 6 7 8 9 10
```

**return** is a function: the round brackets are obligatory.

### 8.3.5. Time and space complexity of algorithms (*)#

Analysis of algorithms can give us a rough estimate of their run time
or memory consumption as a function of the input problem size,
especially for *big* data (e.g., [14, 43]).

In scientific computing and data science, we often deal with vectors
(sequences) or matrices/data frames (tabular data).
Therefore, we might be interested in determining how many
*primitive operations* need to be performed as a function
of their length \(n\) or the number of rows \(n\) and columns \(m\),
respectively.

The \(O\) (Big-Oh) notation can express
the upper bounds for time/resource consumption in asymptotic cases.
For instance, we say that the time complexity is \(O(n^2)\),
if for large \(n\), the number of operations to perform
or memory cells to use will be proportional to *at most* the square of
the vector size (more precisely, there exists \(m\) and \(C>0\)
such that for all \(n>m\), the number of operations is \(\le Cn^2\)).

Therefore, if we have two algorithms that solve the same task, one that has \(O(n^2)\) time complexity, and other of \(O(n^3)\), it is better to choose the former. For large problem sizes, we expect it to be faster.

Moreover, whether time grows proportionally to \(\log n\), \(\sqrt{n}\), \(n\), \(n\log n\), \(n^2\), \(n^3\), or \(2^n\), can be informative in predicting how big the data can be if we have a fixed deadline or not enough space left on the disk.

The **hclust** function determines a hierarchical clustering
of a dataset. It is fed with an object that stores the distance
between all the pairs of input points.
There are \(n(n-1)/2\) (i.e., \(O(n^2)\)) unique point pairs for any given \(n\).
One numeric scalar (`double`

type) takes 8 bytes of storage.
If you have 16 GiB of RAM, what is the largest dataset that you
can process on your machine using this function?

Oftentimes, we can learn about the time or memory complexity
of the functions we use from their documentation;
see, e.g., **help**`("findInterval")`

.

A course in data structures in algorithms, which this one is not, will give us plenty of opportunities to implement many algorithms ourselves. This way, we can gain a lot of insights and intuitions.

For instance, this is a \(O(n)\)-time algorithm:

```
for (i in seq_len(n))
expression
```

and this is one runs in \(O(n^2)\) time:

```
for (i in seq_len(n))
for (j in seq_len(n))
expression
```

as long as, of course, the `expression`

is rather primitive (e.g.,
operations on scalar variables).

R is a very expressive language. Hence, complex and lengthy operations can look pretty innocent. After all, it is a glue language for rapid prototyping.

For example:

```
for (i in seq_len(n))
for (j in seq_len(n))
z <- z + x[[i]] + y[[j]]
```

can be seen as running in \(O(n^3)\) time if each element in the lists
`x`

and `y`

as well as `z`

itself are atomic vectors of length \(n\).

Similarly,

```
Map(mean, x)
```

is \(O(n^2)\) if `x`

is a list of \(n\) atomic vectors, each of length \(n\).

Note

A quite common statistical scenario involves generating a data buffer of a fixed size:

```
ret <- c() # start with an empty vector
for (i in seq_len(n))
ret[[i]] <- generate_data(i) # here: ret[[length(ret)+1]] <- ...
```

This notation, however, involves growing the `ret`

array
in each iteration. Luckily, since R version 3.4.0,
each such size extension has amortised \(O(1)\) time
as some more memory is internally reserved for its
prospective growth (dynamic arrays; see, e.g., Chapter 17 of [14]).

However, it is better to preallocate the output vector
of the desired final size.
We can construct vectors of specific lengths and types
in an efficient way (more efficient than with **rep**) by calling:

```
numeric(3)
## [1] 0 0 0
numeric(0)
## numeric(0)
logical(5)
## [1] FALSE FALSE FALSE FALSE FALSE
character(2)
## [1] "" ""
vector("numeric", 8)
## [1] 0 0 0 0 0 0 0 0
vector("list", 2)
## [[1]]
## NULL
##
## [[2]]
## NULL
```

Note

Not all data fit into memory, but it does not mean that we should start installing Apache Hadoop and Spark immediately. Some datasets can be processed chunk by chunk. R enables data stream handling (some can be of infinite length) through file connections. For example:

```
f <- file("https://github.com/gagolews/teaching-data/raw/master/README.md",
open="r") # a big file, the biggest file ever
i <- 0
while (TRUE) {
few_lines <- readLines(f, n=4) # reads only four lines at a time
if (length(few_lines) == 0) break
i <- i + length(few_lines)
}
close(f)
print(i) # the number of lines
## [1] 90
```

Many functions support reading from/writing to already
established connections of different types, e.g., **file**,
**gzfile**, **textConnection**, batch by batch.

A frequent scenario involves reading a very large CSV, JSON, or XML file only by thousands of lines/records at a time, parsing and cleansing them, and exporting them to SQL databases (which we will exercise in Chapter 12).

## 8.4. Exercises#

From now on, we must stay alert. Many, if not all, of the undermentioned tasks,
can still be implemented without the explicit use of the R loops but
based only on the operations covered in the previous chapters.
If this is the case, try composing both the looped
and loop-free versions.
Use **proc.time** to compare their run times[3].

Answer the following questions.

Let

`x`

be a numeric vector. When does “**if**`(x > 0) ...`

” yield a warning? When does it give an error? How to guard ourselves against them?What is a dangling

**else**?What happens if you put

**if**as the last expression in a curly braces block within a function’s body?Why do we say that `

**&&**` and `**||**` are lazy? What are their use cases?What is the difference between `

**&&**` and `**&**`?Can

**while**always be replaced with**for**? What about the other way around?What is wrong with “

**return**`(1+2)*3`

”?

Verify which of the following can be safely used as logical conditions
in **if** statements. If that is not the case for all `x`

, `y`

, …,
determine the additional conditions that must be imposed
to make them valid.

`x == 0`

,`x[y] > 0`

,**any**`(x>0)`

,**match**`(x, y)`

,**any**`(x %in% y)`

.

What can go wrong in the following code chunk, depending on
the type and form of `x`

? Consider as many scenarios as possible.

```
count <- 0
for (i in 1:length(x))
if (x[i] > 0)
count <- count + 1
```

Implement **shift_left**`(x, n)`

and
**shift_right**`(x, n)`

.
The former function gets rid of the first \(n\) observations in `x`

and adds \(n\) missing values at the end of the resulting vector,
e.g., **shift_left**`(`

**c**`(1, 2, 3, 4, 5), 2)`

is **c**`(3, 4, 5, NA, NA)`

.
On the other hand,
**shift_right**`(`

**c**`(1, 2, 3, 4, 5), 2)`

is
**c**`(NA, NA, 1, 2, 3)`

.

Implement your version of **diff**.

Write a function that determines the longest ascending trend
in a given numeric vector, i.e.,
the length of the longest subsequence of consecutive increasing elements.
For example, the input **c**`(1, 2, 3, 2, 1, 2, 3, 4, 3)`

should yield 4.

Implement the functions that round down and round up each element in a numeric vector to a number of decimal digits.

This concludes the first part of this magnificent book.