#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Extracting, Transforming, and Loading
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Using the replacement function

On some occasions in R, we may discover that we can assign a value to a function call, which is what the replacement function does. Here, we will show you how the replacement function works, and how to create your own.

### Getting ready

Ensure that you completed the previous recipes by installing R on your operating system.

### How to do it...

Perform the following steps to create a replacement function in R:

1. First, we assign names to data with the `names` function:

```> x <- c(1,2,3)
>names(x) <- c('a','b','c')
>x
a b c
1 2 3
```

What the `names` function actually does is similar to the following commands:

```> x <- 'names<-'(x,value=c('a','b','c'))
>x
a b c
1 2 3
```
2. Here, we can also make our replacement function:

```> x<-c(1,2,3)
> "erase<-" <- function(x, value){
+ x[!x %in% value]
+ }
>erase(x) <- 2
>x
[1] 1 3
```
3. We can invoke the `erase` function in the same way that we invoke the normal function:

```>x <- c(1,2,3)
> x <- 'erase<-'(x,value=c(2))
>x
[1] 1 3
```

We can also remove multiple values with the `erase` function:

```> x <- c(1,2,3)
>erase(x) = c(1,3)
>x
[1] 2
```
4. Finally, we can create a replacement function that can remove values of certain positions:

```> x <- c(1,2,3)
> y <- c(2,2,3)
> z <- c(3,3,1)
> a = list(x,y,z)
> "erase<-" <- function(x, pos, value){
+ x[[pos]] <- x[[pos]][!x[[pos]] %in% value]
+ x
+ }
>erase(a, 2) = c(2)
>a
[[1]]
[1] 1 2 3
[[2]]
[1] 3
[[3]]
[1] 3 3 1
```

### How it works...

In this recipe, we first demonstrated how we could use the `names` function to assign argument names for each value. This type of function method may appear confusing, but it is actually what the replacement function does: assigning the value to the function call. We then illustrated how this function works in a standard function form, which we achieved by placing an assignment arrow (`<-`) after the function name and placing the `x` object and value between the parentheses.

Next, we learned how to create our replacement function. We made a function named `erase`, which removed certain values from a given object. We invoked the function by wrapping the vector to replace within the `erase` function and assigning the value to remove on the right-hand side of the assignment notation. Alternatively, we can still call the replacement function by placing an assignment arrow after `erase` as the function name. In addition to removing a single value from a given vector object, we can also remove multiple values by placing a vector on the right-hand side of the assignment function.

Furthermore, we can remove the values of certain positions with the replacement function. Here, we only needed to add a position argument between the object and value within the parentheses. As our last step shows, we removed `2` from the second value of the list with our newly-created replacement function.

### There's more...

As mentioned earlier, `names<-` is a replacement function. To examine whether a function is a replacement function or not, use the `get` function:

```>get("names<-")
function (x, value) .Primitive("names<-")
```