---
title: "Vectorization in R"
author: "Marcin Kierczak"
image: "assets/featured.webp"
format: revealjs
---

## {visibility="hidden"}

```{r}
#| echo: false
library(tidyverse)
library(emo)
library(peakRAM)
# library(ggplot2)
```

## Learning Outcomes

By the end of this module, you will:

- understand how to write more efficient loops
- be able to vectorize most loops
- understand how the `apply*` functions work
- be aware of the `purrr` package
- understand what a recursive call is

## The simplest of all `for` loops

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

```{r for.loop.ex1}
vec <- c(1:5)
vec
for (i in vec) {
  vec[i] <- vec[i] + 1
}
vec
```

. . .

Exactly the same can be achieved in R by means of **vectorization**:

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

Which is better? `r emo::ji('confused')`

## 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: true

vec <- c(1:1e6)
peakRAM::peakRAM(vec <- vec + 1)
```

. . .

to the loop version:

```{r for.loop.avoid.timing2}
#| echo: true

vec <- c(1:1e6)
loop <- function(vec) {
  for (i in vec) {
    vec[i] <- vec[i] + 1
  }
  return(vec)
}
peakRAM::peakRAM(loop(vec))
```

## Vectorization &mdash; the problem

```{r is_a_droid}
#| error: true
is_a_droid <- function(x) {
  droids <- c("2-1B", "4-LOM", "8D8", "0-0-0", "AP-5", "AZI-3", "Mister Bones", "BB-8", "BB-9E", "BD-1", "BT-1", "C1-10P", "C-3PO", "R2-D2")
  if (x %in% droids) {
    return(T)
  } else {
    return(F)
  }
}

test <- c("Anakin", "Vader", "R2-D2", "AZI-3", "Luke")
is_a_droid(test)
```

## Vectorization &mdash; the solution(s)

The `base::Vectorize` way:

```{r vec_is_a_droid}
vectorized_is_a_droid <- base::Vectorize(is_a_droid, vectorize.args = c("x"))
vectorized_is_a_droid(test)
```

. . .

The `apply*` way:

```{r, error=T}
apply(as.matrix(test), FUN = is_a_droid, MARGIN = 1)
```

. . .

```{r}
lapply(test, FUN = is_a_droid) %>% unlist() # list apply
```

. . .

```{r}
sapply(test, is_a_droid) # simplified lapply
```

## Vectorization &mdash; the solution(s)

The `vapply`:

```{r}
vapply(test, is_a_droid, FUN.VALUE = TRUE) # value type-safe sapply
vapply(test, is_a_droid, FUN.VALUE = 1)
```

. . .

```{r}
#| error: true
vapply(test, is_a_droid, FUN.VALUE = "a")
```

. . .

Or the `purrr` way:

```{r}
purrr::map(test, is_a_droid) %>% unlist()
```

## Recursion

When we explicitly 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}
factorial_rec <- function(x) {
  if (x == 0 || x == 1) {
    return(1)
  } else {
    return(x * factorial_rec(x - 1))
  } # Recursive call!
}
factorial_rec(5)
```

## To recurse or to iterate?

    Comparing recursion to iteration is like comparing a phillips head screwdriver to a flat head screwdriver. For the most part you could remove any phillips head screw with a flat head, but it would just be easier if you used the screwdriver designed for that screw right?

    Some algorithms just lend themselves to recursion because of the way they are designed (Fibonacci sequences, traversing a tree like structure, etc.). Recursion makes the algorithm more succinct and easier to understand (therefore shareable and reusable)

 -- [StackOverflow](https://stackoverflow.com/questions/72209/recursion-or-iteration)
 
## Loops &mdash; avoid growing data

Avoid changing dimensions of an object inside the loop:

```{r avoid.growing}
v <- c() # Initialize
for (i in 1:100) {
  v <- c(v, i)
}
```

. . .

It is much better to do it like this:

```{r avoid.growing2}
v <- rep(NA, 100) # Initialize with length
for (i in 1:100) {
  v[i] <- i
}
```

## {background-image="/assets/images/network.svg"}

::: {.v-center .center}
::: {}

[Thank you!]{.largest}

[Questions?]{.larger}

[{{< meta current_year >}} • [SciLifeLab](https://www.scilifelab.se/) • [NBIS](https://nbis.se/) • [RaukR](https://nbisweden.github.io/raukr-2026)]{.smaller}

:::
:::

## 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}
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.mem}
#| echo: true
#| error: true
peakRAM::peakRAM(factorial_rec(1000))
```

And the iterative one:

```{r iter.fact.mem}
#| echo: true
#| error: true
peakRAM::peakRAM(factorial_iter(1000))
```