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You're reading fromR Machine Learning By Example

Product type Book
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Pages 340 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Mr. Raghav Bali

R Machine Learning By Example
Credits
www.PacktPub.com
Preface
1. Getting Started with R and Machine Learning 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Controlling code flow

This section covers areas related to controlling the execution of your code. Using specific constructs such as `if-else` and `switch`, you can execute code conditionally. Constructs like `for`, `while`, and `repeat`, and `help` in executing the same code multiple times which is also known as looping. We will be exploring all these constructs in the following section.

Working with if, if-else, and ifelse

There are several constructs which help us in executing code conditionally. This is especially useful when we don't want to execute a bunch of statements one after the other sequentially but execute the code only when it meets or does not meet specific conditions. The following examples illustrate the same:

```> num = 5
> if (num == 5){
+     cat('The number was 5')
+ }
The number was 5
>
> num = 7
>
> if (num == 5){
+     cat('The number was 5')
+ } else{
+     cat('The number was not 5')
+ }
The number was not 5
>
> if (num == 5){
+     cat('The number was 5')
+ } else if (num == 7){
+     cat('The number was 7')
+ } else{
+     cat('No match found')
+ }
The number was 7
> ifelse(num == 5, "Number was 5", "Number was not 5")
[1] "Number was not 5"
```

Working with switch

The `switch` function is especially useful when you have to match an expression or argument to several conditions and execute only if there is a specific match. This becomes extremely messy when implemented with the `if-else` constructs but is much more elegant with the `switch` function, as we will see next:

```> switch(
+ "first",
+ first = "1st",
+ second = "2nd",
+ third = "3rd",
+ "No position"
+ )
[1] "1st"
>
> switch(
+ "third",
+ first = "1st",
+ second = "2nd",
+ third = "3rd",
+ "No position"
+ )
[1] "3rd"
> # when no match, default statement executes
> switch(
+ "fifth",
+ first = "1st",
+ second = "2nd",
+ third = "3rd",
+ "No position"
+ )
[1] "No position"
```

Loops

Loops are an excellent way to execute code segments repeatedly when needed. Vectorization constructs are, however, more optimized than loops for working on larger data sets, but we will see that later in this chapter. For now, you should remember that there are three types of loops in R, namely, `for`, `while`, and `repeat`. We will look at all of them in the following examples:

```> # for loop
> for (i in 1:10){
+     cat(paste(i," "))
+ }
1  2  3  4  5  6  7  8  9  10
>
> sum = 0
> for (i in 1:10){
+     sum <- sum + i
+ }
> sum
[1] 55
>
> # while loop
> count <- 1
> while (count <= 10){
+     cat(paste(count, " "))
+     count <- count + 1
+ }
1  2  3  4  5  6  7  8  9  10
>
> # repeat infinite loop
> count = 1
> repeat{
+     cat(paste(count, " "))
+     if (count >= 10){
+         break  # break off from the infinite loop
+     }
+     count <- count + 1
+ }
1  2  3  4  5  6  7  8  9  10
```
You have been reading a chapter from
R Machine Learning By Example
Published in: Mar 2016 Publisher: ISBN-13: 9781784390846
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