#    
## Overview

In R, iteration is not preferred because it's computationally expensive. 

Apart from the basic looping functions like `for` and `while`, R also supports the following vectorized looping functions:

- `apply()`
- `lapply()`
- `tapply()`
- `sapply()`
- `by()`

The primary goal of these functions is to avoid using loop structures explicitly. They can apply a function on an input list, matrix, or array. These functions accept any function as a parameter.

We'll be looking into three main functions in detail in this shot.

## The `apply()` function

The **`apply()` function** accepts a data frame, vector, list, or array as input and returns a data frame, vector, list, or array as output.

### Syntax
```
apply(X, MARGIN, FUN)
```

### Arguments

* **`X`**: This is a data frame, vector, list, or array.
* **`MARGIN`**:  This takes a value or range between `1` and `2` to define where to apply the function, e.g.:
    1) `MARGIN=1`: The manipulation is performed on rows.
    2) `MARGIN=2`: The manipulation is performed on columns.
    3) `MARGIN=c(1,2)` The manipulation is performed on rows and columns.
* **`FUN`**: This specifies which function should be used. Mean, median, sum, min, max, and other built-in functions, as well as user-defined functions, can be used.

### Code

Let's look at the following example.

df <- data.frame(x = 1:4, y = 5:8, z = 10:13)

#df x y  z
#   1 5 10 = 16 (sum by row)
#   2 6 11 = 19 (sum by row)
#   3 7 12 = 22 (sum by row)
#   4 8 13 = 25 (sum by row)

# Sum by row
apply(X = df, MARGIN = 1, FUN = sum)

# # Explanation

* Line 1: This defines the data frame named `df`.
* Line 10: This outputs the summed by row values as shown in lines 3-7.

##   
## The `lapply()` function

The **`lapply()` function** can be used to perform operations on a list or vector that returns a list or vector of the same length as the original with the resultant elements obtained after applying the specified `FUN`.

### Syntax

```
lapply(X, FUN)
```

### Arguments

* **`X`**: This is a list or vector.
* **`FUN`**: This is the function to be applied on each element of `X`.

### Code

Let's look at the following example.

movies <- c("SPYDERMAN","BATMAN","VERTIGO","CHINATOWN")
movies_lower <- lapply(movies, tolower)
str(movies_lower)

movies <- c("SPYDERMAN","BATMAN","VERTIGO","CHINATOWN")
movies_lower <-lapply(movies, tolower)
str(movies_lower)

# # Explanation

* Line 1: This defines the vector named `movies`.
* Line 2: This applies the `tolower` function and stores the results to `movies_lower`.
* Line 3: This displays the structure of `movies_lower`.

##   
## The `sapply()` function

The **`sapply()` function** accepts a vector, list, or array as input and returns a vector, list, or array as output.

### Syntax

```
sapply(X, FUN)
```

### Arguments

* **`X`**: This is a vector, list, or array.
* **`FUN`**: This is a function applied to each element of `X`.

### Code

Let's look at the following example.

data <- list(item1 = 1:5,
             item2 = seq(4,36,8),
             item4 = c(1,3,5,7,9))

# $item1
# [1] 1 2 3 4 5 = 15 (summed)
# $item2
# [1]  4 12 20 28 36 = 100  (summed)
# $item4
# [1] 1 3 5 7 9 = 25  (summed)

sapply(data, sum)

### Explanation

* Lines 1-3: This defines the list named `data`.
* Line 12: This sums and displays the values of `data` as shown in lines 5-10.

What are the different types of iterations in R?

R's vectorized iteration functions, apply(), lapply(), and sapply(), process data efficiently, avoiding loops. They apply functions to elements of data structures.

What are the different types of iterations in R?

Overview

The apply() function

Syntax

Arguments

Code

# Explanation

The lapply() function

Syntax

Arguments

Code

# Explanation

The sapply() function

Syntax

Arguments

Code

Explanation

The `apply()` function

The `lapply()` function

The `sapply()` function