apply.Rmd

---
title: "Apply"
output:
  html_document
---


One of the weaknesses of R is that loops can be relatively slow to execute.
The apply family of functions attempts to address this.
Use `?apply` or `?lapply` for examples of other flavors of the apply command.

*****

## Use of apply()

Create a test matrix.

```{r}
tmp <- matrix(rep(1:3, times=3), ncol=3)
tmp
```


'Apply' the function 'sum' over rows.

```{r}
apply(tmp, MARGIN=1, sum)
```


'Apply' the function 'sum' over rows.

```{r}
apply(tmp, MARGIN=2, sum)
```


*****

## Use of custom functions


If the operation we wish to apply to a data structure exists as an R function, we can call it from the apply command.
We can also define our own functions to apply over a data structure.


In practice, if we wanted to get averages over a matrix, there are existing functions that should be used.
Here we'll create our own as an example.


```{r}
myMean <- function(x){
  sum(x)/length(x)
}

apply(tmp, MARGIN=1, myMean)

```


Through defining our own functions we can extract all sorts of summaries from data in a fairly efficient manner.


*****

## Homework


A large part of quality control of data sets is finding and mitigating missing data.
Here I'll create a larger toy data set and add some missing data.
As homework, create a custom function that will help you identify missing data.


```{r}
toy <- matrix(ncol=10, nrow=12)
set.seed(999)
toy[] <- rnorm(length(toy))

colnames(toy) <- paste("sample", 1:ncol(toy), sep="_")
rownames(toy) <- paste("variant", 1:nrow(toy), sep="_")

set.seed(999)
is.na(toy[round(runif(n=30, min=1, max=length(toy)))]) <- TRUE
toy
```


Now we create a custom function to process our matrix.


```{r}
my_fun <- function(x){
  length(x)
}
```


Lastly, we use apply to iterate the function over the matrix.


```{r}
apply(toy, MARGIN=1, my_fun)
```


Can you modify the function `my_fun()` so that it counts missing data in each sample and variant?