M4Functions.Rmd

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```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval=FALSE)
```
#<span style="color:cadetblue">Functions</span>

This module will cover: 

  - function syntax
  - control statements
  - logic statements
  
  
which will require the following skills already covered:

  - data structure 
  - assigning an object

***
##<span style="color:cadetblue">What is a function</span> 
Functions incorporate sets of instructions that you want to use repeatedly or that, because of their complexity, are better self-contained in a script and called when needed.

![](geeks.png)

&nbsp;

You can write your own functions, but `R` and its packages come with "pre-made" functions as well.

A useful function is ```rm(list=ls())```, which clears `R`’s brain.

Others include seq(), log(), sin()...

A typical function has the follwoing architecture
```{r function, eval=FALSE}
func.name = function (arglist)  {body}
```

###<span style="color:orangered">**Let's copy and paste them into the console**:</span> 

##<span style="color:cadetblue">Example 1a:</span> Simple function
```{r function1}
my.function1<-function(a,b,c){
 tmp<-a+b
 tmp2<-tmp/c
 return(tmp2)
}

#Using this function we have:
 out1<-my.function1(4,7,2)
 out1
```

###<span style="color:orangered">**Practice**:</span> Choose other sets of numbers
```{r function2} 
out2<-my.function1(,,)
out2
```


###<span style="color:orangered">**Practice**:</span> Create a function that will return the sum of 2 integers
```{r function15} 

```

##<span style="color:cadetblue">Example 1b:</span> Rounding  function
```{r function13}
Roundme <- function(x){
   #this function uses the floor function to 
   #do what the round function does
   x <- x+0.5
   floor(x)
 }
```


##<span style="color:cadetblue">Example 2a:</span> Control statements **if**

```{r function11}
x <- 8

if (x < 9) print("x is less than nine")
 
x <- 10

if (x < 9) print("x is less than nine")

```

##<span style="color:cadetblue">Example 2b:</span> Control statements **if else**

```{r function3}
 a<-5
 if(a<1){
 b<-5
 c<-4
 } else {
 b<-10
 }
 b
```


###<span style="color:orangered">**Practice**:</span> Uncomment and fill in a simple function with a nested if statement will print alternative outcomes

```{r function14}

#Isit <- function(x){
# if (x == ??)
 #print("???!")
 #else print("???")
#}


#Isit(?)

#Isit(?)

```

##<span style="color:cadetblue">Example 2c:</span> Control statements **while**
```{r function10}
x <- 2
while(x < 10){
 print(x)
 x <- x+1
}

print("The end")
```

##<span style="color:cadetblue">Example 3a:</span> **for** loop

```{r function4}
a<-c() # creating an empty vector
 for(i in 1:10){ # i becomes 1 through 10 iteratively 
a[i]<-i^3  # this is the operation
 }
 a # seeing what the vector a becomes
```



##<span style="color:cadetblue">Example 4:</span> Random number generation

Here is an example, there are a number of distributions available
```{r function5} 

random_numbers<-rnorm(3,mean=5,sd=3) # normal distribution generates 3 numbers with a mean and a sd
random_numbers
```
 
##<span style="color:cadetblue">Example 5:</span> Descriptive stats 
```{r function6}  
dat<-rnorm(30,mean=5,sd=3) # creating a vector

dat # check it

sort (dat) #sort vector

unique (dat) #unique values

max(dat) #max

min(dat) #min

mean(dat) # taking the mean

sum(dat) # sum the vector elements

var(dat) # taking the variance

length(dat) # length of vector

sqrt(var(dat)/length(dat)) # standard error
```

###<span style="color:orangered">**Practice**:</span> Write a function to compute standard error of dat

```{r function12}

```



##<span style="color:cadetblue">Example 6:</span> The apply  function
```apply``` are functions to manipulate slices of data from matrices, arrays, lists and dataframes in a repetitive way 
```{r function7}  
X <- matrix(rnorm(30), nrow=5, ncol=6) # creating a matrix

# Sum the values of each column with `apply()`
apply(X, 2, sum)


```

###<span style="color:orangered">**Practice**:</span> How do you calculate the sum of each row using apply?

##<span style="color:cadetblue">Example 7:</span> The lapply  function
```lapply``` applies a given function to every element of a list and obtain a list as result
```{r function8}  
# create a list with 2 elements
l <- list(a = 1:10, b = 11:20)

# the mean of the values in each element
lapply(l, mean)

# the sum of the values in each element
lapply(l, sum)

```

##<span style="color:cadetblue">Example 8:</span> The sapply  function
```sapply``` will return either a vector, with elements [[‘a’]] and [[‘b’]], or a matrix with column names “a” and “b”
```{r function9}  
# create a list with 2 elements
l <- list(a = 1:10, b = 11:20)
# mean of values using sapply
l.mean <- sapply(l, mean)
# what type of object was returned?
class(l.mean)

# it's a numeric vector, so we can get element "a" like this
l.mean[['a']]

```



## <span style="color:cadetblue">Going further</span>
rapply, tapply, parallel computing in R (https://www.r-bloggers.com/how-to-go-parallel-in-r-basics-tips/)