index.Rmd

---
title       : Introduction to the R Language
subtitle    : Loop Functions - mapply
author      : Roger Peng, Associate Professor
job         : Johns Hopkins Bloomberg School of Public Health
logo        : bloomberg_shield.png
framework   : io2012        # {io2012, html5slides, shower, dzslides, ...}
highlighter : highlight.js  # {highlight.js, prettify, highlight}
hitheme     : tomorrow      # 
url:
  lib: ../../libraries
  assets: ../../assets
widgets     : [mathjax]            # {mathjax, quiz, bootstrap}
mode        : selfcontained # {standalone, draft}
---

## mapply

`mapply` is a multivariate apply of sorts which applies a function in parallel over a set of arguments.

```r
> str(mapply)
function (FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE,
          USE.NAMES = TRUE)
```

- `FUN` is a function to apply
- ... contains arguments to apply over
- `MoreArgs` is a list of other arguments to `FUN`.
- `SIMPLIFY` indicates whether the result should be simplified

---

## mapply

The following is tedious to type

`list(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))`

Instead we can do

```r
> mapply(rep, 1:4, 4:1)
[[1]]
[1] 1 1 1 1

[[2]]
[1] 2 2 2

[[3]] 
[1] 3 3

[[4]] 
[1] 4
```

---

## Vectorizing a Function

```r
> noise <- function(n, mean, sd) {
+ rnorm(n, mean, sd)
+ }
> noise(5, 1, 2)
[1]  2.4831198  2.4790100  0.4855190 -1.2117759
[5] -0.2743532

> noise(1:5, 1:5, 2)
[1] -4.2128648 -0.3989266  4.2507057  1.1572738
[5]  3.7413584
```

---

## Instant Vectorization

```r
> mapply(noise, 1:5, 1:5, 2)
[[1]]
[1] 1.037658

[[2]]
[1] 0.7113482 2.7555797

[[3]]
[1] 2.769527 1.643568 4.597882

[[4]]
[1] 4.476741 5.658653 3.962813 1.204284

[[5]]
[1] 4.797123 6.314616 4.969892 6.530432 6.723254
```

---

## Instant Vectorization

Which is the same as

```r
list(noise(1, 1, 2), noise(2, 2, 2),
     noise(3, 3, 2), noise(4, 4, 2),
     noise(5, 5, 2))
```