⚒️ 🗜️ Writing functions in

# ⚒️ 🗜️ Writing functions in
## IASSL: 21/25 Feb, 2022
### 
### Dr Thiyanga Talagala

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## Functions in R

👉🏻 Perform a specific task according to a set of instructions.

--
👉🏻  Some functions we have discussed so far,

> `c`, `matrix`, `array`, `list`, `data.frame`, `str`, `dim`, `length`, `nrow`, `plot`

---

## Functions in R (cont.)

👉🏻 There are basically two types of functions:

> 💻 Built-in functions 
  
      Already created or defined in the programming framework to make our work easier.
> 👨 User-defined functions
           
      Sometimes we need to create our own functions for a specific purpose.

---

## Syntax

```r

name <- function(arg1, aug2, ...){

return(value)

}

```

]

### Example

```r
cal_power <- function(x){

a <- x^2; b <- x^3
out <- c(a, b)
names(out) <- c("squared", "cubed")
out # or return(out)

}
```

### Evaluation

```r
cal_power(2)
```

```
squared   cubed 
      4       8 
```

]

👉 Functions are created using the `function()`

---

## Basic components of a function

**1. Function name**

---

### Syntax

```r

name <- function(arg1, aug2, ...){

return(value)

}

```

]

### Example

```r
*cal_power <- function(x){

a <- x^2
b <- x^3
out <- c(a, b)
names(out) <- c("squared", "cubed")
out # or return(out)

}
```

]

---

- use verbs, where possible
 
 - should be meaningful
 
 - use an underscore (_) to separate words
 
 - avoid names of built-in functions
 
 - start with lower case letters. Note that R is a case sensitive language

---

## Basic components of a function

1. Function name

**2. Function arguments/ inputs**

---

### Syntax

```r

name <- function(arg1, aug2, ...){

return(value)

}

```

]

### Example

```r
*cal_power <- function(x){

a <- x^2
 b <- x^3
 out <- c(a, b)
 names(out) <- c("squared", "cubed")
 out # or return(out)

}
```

]

- value passed to the function to obtain the function's result.

---

## Basic components of a function

1. Function name

2. Function arguments/ inputs

**3. Function body**

---

### Syntax

```r

name <- function(arg1, aug2, ...){

return(value)

}

```

]

### Example

```r
cal_power <- function(x){

* a <- x^2
* b <- x^3
* out <- c(a, b)
* names(out) <- c("squared", "cubed")
* out # or return(out)

}
```

]

---

## Function with single line

### Mathod 1

```r
cal_sqrt <- function(x){

x^2

}
```

]

### Method 2

```r
cal_sqrt <- function(x) x^2
```

]

---

### Function body (Cont.)

- {} do not go in one single line, always two lines

```r
# Good ---
if(y == 2){
print("even")
}

# Bad ---
if(y == 2){ print("even")}
```

---

**Load the mozzie dataset**

```r
library(mozzie)
data(mozzie); head(mozzie, 2)
```

```
  ID Year Week Colombo Gampaha Kalutara Kandy Matale Nuwara Eliya Galle
1  1 2008   52      15       7        1    11      4            0     0
2  2 2009    1      44      23        5    16     21            2     0
  Hambantota Matara Jaffna Kilinochchi Mannar Vavuniya Mulative Batticalo
1          6     22      0           0      8        0        0         1
2          5     18      1           0      0        0        0         0
  Ampara Trincomalee Kurunagala Puttalam Anuradhapura Polonnaruwa Badulla
1      0           0          2        1            2           0       1
2      1           1         10        5            0           0       1
  Monaragala Ratnapura Kegalle
1          1         2      16
2          0         1      25
```

> Use Min-Max transformation to rescale all the districts variables onto 0-1 range.

> Min-Max transformation is `$\frac{x_i-min(x)}{max(x)-min(x)}$` where `$x=(x_1, x_2, ...x_n)$`.

---

** Min-Max transformation on `mozzie`**

```r
minmax.colombo <- (mozzie$Colombo - min(mozzie$Colombo, na.rm = TRUE)) /
  (max(mozzie$Colombo, na.rm=TRUE) - min(mozzie$Colombo, na.rm=TRUE))
head(minmax.colombo) # Colombo district
```

```
[1] 0.03157895 0.09263158 0.08210526 0.12000000 0.11157895 0.06105263
```

```r
minmax.gampaha <- (mozzie$Gampaha - min(mozzie$Gampaha, na.rm = TRUE)) /
  (max(mozzie$Gampaha, na.rm = TRUE) - min(mozzie$Gampaha, na.rm = TRUE)) 
head(minmax.gampaha) # Gampaha district
```

```
[1] 0.02734375 0.08984375 0.07421875 0.08984375 0.09375000 0.06640625
```

```r
minmax.kalutara <- (mozzie$Gampaha - min(mozzie$Kalutara, na.rm = TRUE)) /
  (max(mozzie$Kalutara, na.rm = TRUE) - min(mozzie$Kalutara, na.rm = TRUE))
head(minmax.kalutara) # Kalutara district
```

```
[1] 0.09333333 0.30666667 0.25333333 0.30666667 0.32000000 0.22666667
```

---
## Copying-and-pasting

> You could easily make errors.

> A mistake copied becomes a mistake repeated.

## When should you write a function?

- Whenever you need to copy and paste a block of codes many times

- A function is a reusable block of programming code designed to do a specific task.

- If you don't find a suitable built-in function to serve your purpose, you can write your own function

- To share your work with others

---

# Writing a function

### Step 1: Function name

```r
rescale_minmax

```
--

### Step 2: Assign your function to the name

```r
rescale_minmax <- 
```
--

### Step 3: Tell R that you are writing a function

```r_
rescale_minmax <- function(x) # Arguments/inputs should be defined inside ()

```
---

### Step 4: Curly braces define the start and the end of your work

```r
rescale_minmax <- function(x){

# Task

# output

}

```

---
## Step 5: Function inputs, task and outputs

**Find all the inputs that correspond to a given function output?**

```r
(x - min(x, na.rm = TRUE)) / (max(x, na.rm=TRUE) - min(x, na.rm=TRUE))
```

---

# Step 6: Complete your function

```r
rescale_minmax <- function(x){
  (x - min(x, na.rm = TRUE)) / (max(x, na.rm=TRUE) - min(x, na.rm=TRUE))
}
```

---

# Step 7: Check your function with a few different inputs

```r
rescale_minmax <- function(x){
  (x - min(x, na.rm = TRUE)) / (max(x, na.rm=TRUE) - min(x, na.rm=TRUE))
}
```

```r
rescale_minmax(c(1, 200, 250, 80, NA))
```

```
[1] 0.0000000 0.7991968 1.0000000 0.3172691        NA
```

---

# Your turn

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# Functions are for humans and computers

- Descriptive names for variables.

- Comment your code.

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# Your turn

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Write a function to compute z-score value of a A/L Mathematics student given the marks of the student. Assume

mean(Mathematics) = 60, sd(Mathematics) = 10,

mean(Chemistry) = 45, sd(Chemistry) = 20,

mean(Physics) = 55, sd(Physics) = 5.

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## Thank you!