slide_r_elements_4.Rmd

---
title: "Replication, Control Structures & Functions"
subtitle: "Elements of the R language"
author: "Marcin Kierczak, Nima Rafati, Miguel Redondo"
keywords: bioinformatics, course, scilifelab, nbis, R
output:
  xaringan::moon_reader:
    encoding: 'UTF-8'
    self_contained: false
    chakra: 'assets/remark-latest.min.js'
    css: 'assets/slide.css'
    lib_dir: libs
    include: NULL
    nature:
      ratio: '4:3'
      highlightLanguage: r
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
      slideNumberFormat: "%current%/%total%"
---

exclude: true
count: false

```{r,echo=FALSE,child="assets/header-slide.Rmd"}
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

---
name: contents

# Contents of the lecture

- variables and their types
- operators
- vectors
- matrices
- data frames
- lists
- **repeating actions: loops**
- **decision taking: `if` control structures**
- **functions**


---
name: repeating_actions_1

# Repeating actions

Sometimes you want to repeat certain action several times.  

There are few alternatives in R, for example:  
- `for` loop 
- `while` loop   

---
name: for_loop_0

# Repeating actions — for loop

One way to repeat an action is to use the **for-loop**. 

This is the general syntax:

```
for (var in seq) {
  expr
}
```
Where:
- var = variable that will take values from the sequence
- seq= sequence of values
- expr = expression to be executed

---
name: for_loop_1
# Repeating actions — for loop, an example

Example. 

```{r for.loop, echo=T}
for (i in 1:5) {
  print(paste('Performing operation on no.', i))
}
```

--
A slight modification of the above example.

```{r for.loop2, echo=T}
for (i in c(2,4,6,8,10)) {
  print(paste('Performing operation on no.', i))
}
```
The variable `i` <u> takes values </u> from the sequences.

---
name: for_loop_example

# Repeating actions &mdash; for loop, another example

Say, we want to add 1 to every element of a vector:

```{r for.loop.ex1, echo=T}
vec <- c(1:5)
vec

```

```{r for.loop.ex2, echo=T}

for (i in vec) {
  vec[i] <- vec[i] + 1
}
vec
```

--

The above can be achieved in R by means of **vectorization**.  
**Vectorization** is an element-wise operation where you perform an operation on entire vectors.

```{r for.loop.avoid, echo=T}
vec <- c(1:5)
vec + 1  
```

---
name: vectorization_benchmark
exclude:true
# Repeating actions &mdash; vectorization

Let us compare the time of execution of the vectorized version (vector with 10,000 elements):

```{r for.loop.avoid.timing, echo=T}
vec <- c(1:1e6)
ptm <- proc.time()
vec <- vec + 1
proc.time() - ptm # vectorized
```


to the loop version:

```{r for.loop.avoid.timing2, echo=T}
vec <- c(1:1e6)
ptm <- proc.time()
for (i in vec) {
  vec[i] <- vec[i] + 1
}
proc.time() - ptm # for-loop
```


---
name: for_loop_counter

# Repeating actions &mdash; for loop with a counter

To know the current iteration number on the loop, we can set an external counter:

```{r for.loop.cnt, echo=T}
cnt <- 1
for (i in c(2,4,6,8,10)) {
  cat(paste('Iteration', cnt,
            'Performing operation on no.', i), '\n')
  cnt <- cnt + 1
  
}
```

---
name: loops_avoid_growing

# Repeating actions &mdash; avoid growing data

Avoid changing dimensions of an object inside the loop:

```{r avoid.growing, echo=T}
v <- c() # Initialize
for (i in 1:100) {
  v <- c(v, i)
}
cat(head(v), " ... ", tail(v))
```

--

It is much better to do it like this:

```{r avoid.growing3, echo=T}
v <- rep(NA, 100) # Initialize with length
for (i in 1:100) {
  v[i] <- i
}
cat(head(v), " ... ", tail(v))
```

--

Always try to know the size of the object you are going to create!


---
name: while_loop

# Repeating actions &mdash; the while loop

There is also another type of loop in R, the **while loop** which is executed as long as some condition is true.
```{r loop.while, echo=T}
x <- 1
while (x < 5) {
  cat("x equals",x, "\n")
  x <- x + 1
}
```

---
name: recursion
exclude: true
# Any questions so far?
<!-- # Recursion

When we explicitely repeat an action using a loop, we talk about **iteration**. We can also repeat actions by means of **recursion**, i.e. when a function calls itself. Let us implement a factorial $!$:

```{r rec.fact, echo=F}
factorial.rec <- function(x) {
  if (x == 0 || x == 1)
    return(1)
  else
    return(x * factorial.rec(x - 1)) # Recursive call!
}
factorial.rec(5)
```


# Recursion = iteration?
Yes, every iteration can be converted to recursion (Church-Turing conjecture) and vice-versa. It is not always obvious, but theoretically it is doable. Let's see how to implement *factorial* in iterative manner:
```{r rec.fact.iter, echo=T}
factorial.iter <- function(x) {
  if (x == 0 || x == 1)
    return(1)
  else {
    tmp <- 1
    for (i in 2:x) {
      tmp <- tmp * i
    }
    return(tmp)  
  }
}
factorial.iter(5)
```

# Recursion == iteration, really?
More writing for the iterative version, right? What about the time efficiency?  
The recursive version:
```{r rec.fact.timing, echo=F}
ptm <- proc.time()
factorial.rec(20)
proc.time() - ptm
```
And the iterative one:
```{r iter.fact.timing, echo=F}
ptm <- proc.time()
factorial.iter(20)
proc.time() - ptm
```
-->

---

name: if_clause

# Decisions, if-clause

Often, one has to take a different course of action depending on a flow of the algorithm. 

In R, we use the `if` clause for this purpose.

This is the general syntax:

```
if (condition) {
  expr
}
```

--
A simple example:

```{r example if, echo=T}

temp <- -2

if (temp < 0) {
  print("It's freezing!")
}

```

---
name: if examples

# Decisions, if-clause

Two more examples of using `if` inside of a loop:

Let's display only the numbers that are greater than 5 in the sequence $[1, 10]$
```{r simple if, echo=T}
v <- 1:10
for (i in v) {
  if (i > 5) { # if clause
    cat(i, ' ')
  }
}
```

--

Let's display only odd numbers in the sequence $[1, 10]$:

```{r if, echo=T}
v <- 1:10
for (i in v) {
  if (i %% 2 != 0) { # if clause
    cat(i, ' ')
  }
}
```

---
name:if_else

# Decisions, if-else

What if we want to perform an action when the first `if` condition is not met? 

If we want to print 'o' for an odd number and 'e' for an even, we could write either of:

.pull-left-50[

Only `if` clauses
```{r ifelse1, echo=T}
v <- 1:10
for (i in v) {
  if (i %% 2 != 0) { # if clause
    cat('o ')
  }
  if (i %% 2 == 0) { # another if-clause
    cat('e ')
  }
}
```

]

--

.pull-right-50[
Using `if-else`:      
```{r ifelse2, echo=T}
v <- 1:10
for (i in v) {
  if (i %% 2 != 0) { # if clause
    cat('o ')
  } else { # else clause
    cat('e ')
  }
}
```

]

---
name: elif
exclude: true

# Decisions, if-else-if for more alternatives

So far, so good, but we were only dealing with 2 alternatives. 

Let's say that we want to print '?' for zero, 'e' for even and 'o' for an odd number:

We can use the **if-else-if** clause for this!

```{r if.elseif, echo=T}
v <- c(0:10)
for (i in v) {
  if (i == 0) { #if clause
    cat('? ')
  } else if (i %% 2 != 0) { # else-if clause
    cat('o ')
  } else { # else clause
    cat('e ')
  }
}
```


---
name: switch
exclude: true
# Switch

If-else clauses operate on logical values. What if we want to take decisions based on non-logical values? Well, if-else will still work by evaluating a number of comparisons, but we can also use **switch**:

```{r switch, echo=T}
switch.demo <- function(x) {
  switch(class(x),
         logical = cat('logical\n'),
         numeric = cat('Numeric\n'),
         factor = cat('Factor\n'),
         cat('Undefined\n')
         )
}
switch.demo(x=TRUE)
switch.demo(x=15)
switch.demo(x=factor('a'))
switch.demo(data.frame())
```

---
name: fns

# Functions

Often, it is really handy to re-use some code we have written or to pack together the code that is doing some task. Functions are a really good way to do this in R:

This is the general syntax

```
function_name <- function(arg1, arg2, ...) {
  expr
  return(something)
}
```

--

Let's see a simple example of a function to add one to a number:

```{r functions1, echo=T,error=T}
add.one <- function(arg1) {
  result <- arg1 + 1
  return(result)
}
add.one(1)
```


---
name: fns_defaults

# Functions &mdash; arguments with default values

Sometimes, it is good to use default values for some arguments:

```{r functions2, echo=T, error=T}
add.a.num <- function(arg, num=1) {
  result <- arg + num
  return(result)
}
add.a.num(1) # skip the num argument

```

--

```{r functionresult, echo=T}
add.a.num(1, 5) # overwrite the num argument
add.a.num(1, num=5) # overwrite the num argument
```

--

```{r functionsresult2, echo=T, error=T}
add.a.num(num=1) # skip the first argument
```

---
name:fns_args

# Functions &mdash; order of arguments

```{r functions3, echo=T}
args.demo <- function(x, y, arg3) {
  print(paste('x =', x, 'y =', y, 'arg3 =', arg3))
}
args.demo(1,2,3)

```
--

```{r functions3b, echo=T}
args.demo(x=1, 2, 3)

```

--
```{r functions3c, echo=T}
args.demo(x=1, y=2, arg3=3)

```

--

```{r functions3d, echo=T}

args.demo(arg3=3, x=1, y=2)
```


---
name: variable_scope

# Functions &mdash; variable scope

.pull-left-50[
Functions 'see' not only what has been passed to them as arguments:
```{r fns.varscope, echo=T}
x <- 7
y <- 3
xyplus <- function(x) {
  x <- x + y
  return(x)
}
xyplus(x)
x

```
]

--

.pull-right-50[
Everything outside the function is called **global environment**. There is a special operator `<<-` for working on global environment:

```{r fns.varscope.glob, echo=T}
x <- 1
xplus <- function(x) {
  x <<- x + 1
}
xplus(x)
x
xplus(x)
x
```
]

---
name: fns_ellipsis

# Functions &mdash; the `...` argument

There is a special argument **...** (ellipsis) which allows you to give any number of arguments or pass arguments downstream:

```{r fns.3dots, echo=T, fig.height = 3, fig.width = 6}
# Any number of arguments
my.plot <- function(x, y, ...) { # Passing downstream
  plot(x, y, las=1, cex.axis=.8, ...)
}

par(mfrow=c(1,2),mar=c(4,4,1,1))
my.plot(1,1)
my.plot(1, 1, col='red', pch=19)
```

- A function enclosing a function is a **wrapper function**

---
name: ellipsis_trick
exclude:true
# Functions &mdash; the ellipsis argument trick

What if the authors of, e.g. plot.something wrapper forgot about the `...`?

```{r fns.3dots.trick, echo=T, fig.height = 5, fig.width = 5}
my.plot <- function(x, y) { # Passing downstrem
  plot(x, y, las=1, cex.axis=.8, ...)
}
formals(my.plot) <- c(formals(my.plot), alist(... = ))
my.plot(1, 1, col='red', pch=19)
```

---
exclude:true
<!--  
name: lazy_eval

# R is lazy!

In R, arguments are evaluated as late as possible, i.e. when they are needed. This is **lazy evaluation**:

```{r lazy.eval, echo=F, eval = T}
h <- function(a = 1, b = d) {
  d <- (a + 1) ^ 2
  c(a, b)
}
#h()
```

> The above won't be possible in, e.g. C where values of both arguments have to be known before calling a function **eager evaluation**.  
-->
---

name: everything_is_a_fn
exclude:true

# In R everything is a function

Because in R everything is a function

```{r fns.everything_1, echo=T}
`+`
```


we can re-define things like this:


```{r fns.everything_2, echo=T}
`+` <- function(e1, e2) { e1 - e2 }
2 + 2
```


and, finally, clean up the mess...


```{r fns.everything_3, echo=T}
rm("+")
2 + 2
```

---
name: infix_fns
exclude:true
# Infix notation

Operators like `+`, `-` or `*` are using the so-called **infix** functions, where the function name is between arguments. We can define our own:

```{r infix, echo=T}
`%p%` <- function(x, y) {
  paste(x,y)
}
'a' %p% 'b'
```

---
name:anatomy_of_a_fn

# Anatomy of a function

A function consists of: *formal arguments*, *function body* and *environment*:

```{r fns.formalsbodyenv, echo=T}
formals(add.one)
```

--

```{r fns.formalsbodyenvb, echo=T}
body(add.one)
```

--
```{r fns.formalsbodyenvc, echo=T}
environment(add.one)
environment(sd)
```

---
name: base_fns

# Base functions

When we start R, the following packages are pre-loaded automatically:

```{r preloaded.packages, echo=T}
# .libPaths() # get library location
# library()   # see all packages installed
search()    # see packages currently loaded
```

Check what basic functions are offered by packages: *base*, *utils* and we will soon work with package *graphics*. If you want to see what statistical functions are in your arsenal, check out package *stats*.

<!-- --------------------- Do not edit this and below --------------------- -->

---
name: end_slide
class: end-slide, middle
count: false

# See you at the next lecture!

```{r, echo=FALSE,child="assets/footer-slide.Rmd"}
```

```{r,include=FALSE,eval=FALSE}
# manually run this to render this document to HTML
#rmarkdown::render("presentation_demo.Rmd")
# manually run this to convert HTML to PDF
#pagedown::chrome_print("presentation_demo.html",output="presentation_demo.pdf")
```