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Learning R Programming
Learning R Programming

Learning R Programming: Language, tools, and practical techniques

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Learning R Programming

Chapter 2. Basic Objects

The first step of learning R programming is getting familiar with basic R objects and their behavior. In this chapter, you will learn the following topics:

  • Creating and subsetting atomic vectors (for example, numeric vectors, character vectors, and logical vectors), matrices, arrays, lists, and data frames.
  • Defining and working with functions

"Everything that exists is an object. Everything that happens is a function." -- John Chambers

For example, in statistical analysis, we often feed a set of data to a linear regression model and obtain a group of linear coefficients.

Provided that there are different types of objects in R, when we do this, what basically happens in R is that we provide a data frame object that holds the set of data, carry it to the linear model function and get a list object consisting of the properties of the regression results, and finally extract a numeric vector, which is another type of object, from the list...

Vector

A vector is a group of primitive values of the same type. It can be a group of numbers, true/false values, texts, and values of some other type. It is one of the building blocks of all R objects.

There are several types of vectors in R. They are distinct from each other in the type of elements they store. In the following sections, we will see the most commonly used types of vectors including numeric vectors, logical vectors, and character vectors.

Numeric vector

A numeric vector is a vector of numeric values. A scalar number is the simplest numeric vector. An example is shown as follows:

1.5
## [1] 1.5

A numeric vector is the most frequently used data type and is the foundation of nearly all kinds of data analysis. In other popular programming languages, there are some scalar types such as integer, double, and string, and these scalar types are the building blocks of the container types such as vectors. In R, however, there is no formal definition of scalar types. A scalar...

Matrix

A matrix is a vector represented and accessible in two dimensions. Therefore, what applies to vectors is most likely to apply to a matrix. For example, each type of vector (for example, numeric vector or logical vectors) has its matrix version, that is, there are numeric matrices, logical matrices, and so on.

Creating a matrix

We can call matrix() to create a matrix from a vector by setting up one of its two dimensions:

matrix(c(1, 2, 3, 2, 3, 4, 3, 4, 5), ncol = 3)
##      [,1] [,2] [,3]
## [1,]   1    2    3
## [2,]   2    3    4
## [3,]   3    4    5

By specifying ncol = 3, we mean that the provided vector should be regarded as a matrix with 3 columns (and 3 rows automatically). You may feel the original vector is not as straightforward as its representation. To make the code more user-friendly, we can write the vector in multiple lines:

matrix(c(1, 2, 3,  4, 5, 6,  7, 8, 9), nrow = 3, byrow = FALSE)
##     [,1] [,2] [,3]
## [1,]  1    4    7
## ...

Array

An array is a natural extension to a matrix in its number of dimensions. More specifically, an array is a vector that is represented and accessible in a given number of dimensions (mostly more than two dimensions).

If you are already familiar with vectors and matrices, you won't be surprised to see how arrays behave.

Creating an array

To create an array, we call array() by supplying a vector of data, how this data is arranged in different dimensions, and sometimes the names of the rows and columns of these dimensions.

Suppose we have some data (10 integers from 0 to 9) and we need to arrange them in three dimensions: 1 for the first dimension, 5 for the second, and 2 for the third:

a1 <- array(c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), dim = c(1, 5, 2))
a1
## , , 1
## 
##     [,1] [,2] [,3] [,4] [,5]
## [1,]  0    1    2    3    4
## 
## , , 2
## 
##     [,1] [,2] [,3] [,4] [,5]
## [1,]  5    6    7    8    9

We can clearly see how we can access these entries by looking...

Lists

A list is a generic vector that is allowed to include different types of objects, even other lists.

It is useful for its flexibility. For example, the result of a linear model fit in R is basically a list object that contains rich results of a linear regression such as linear coefficients (numeric vectors), residuals (numeric vectors), QR decomposition (a list containing a matrix and other objects), and so on.

It is very handy to extract the information without calling different functions each time because these results are all packed into a list.

Creating a list

We can use list() to create a list, as the function name suggests. Different types of objects can be put into one list. For example, the following code creates a list that contains a single-element numeric vector, a two-entry logical vector, and a character vector of three values:

l0 <- list(1, c(TRUE, FALSE), c("a", "b", "c"))
l0
## [[1]]
## [1] 1
## 
## [[2]]
## [1] TRUE FALSE
## 
## [[3...

Vector


A vector is a group of primitive values of the same type. It can be a group of numbers, true/false values, texts, and values of some other type. It is one of the building blocks of all R objects.

There are several types of vectors in R. They are distinct from each other in the type of elements they store. In the following sections, we will see the most commonly used types of vectors including numeric vectors, logical vectors, and character vectors.

Numeric vector

A numeric vector is a vector of numeric values. A scalar number is the simplest numeric vector. An example is shown as follows:

1.5
## [1] 1.5

A numeric vector is the most frequently used data type and is the foundation of nearly all kinds of data analysis. In other popular programming languages, there are some scalar types such as integer, double, and string, and these scalar types are the building blocks of the container types such as vectors. In R, however, there is no formal definition of scalar types. A scalar number...

Matrix


A matrix is a vector represented and accessible in two dimensions. Therefore, what applies to vectors is most likely to apply to a matrix. For example, each type of vector (for example, numeric vector or logical vectors) has its matrix version, that is, there are numeric matrices, logical matrices, and so on.

Creating a matrix

We can call matrix() to create a matrix from a vector by setting up one of its two dimensions:

matrix(c(1, 2, 3, 2, 3, 4, 3, 4, 5), ncol = 3)
##      [,1] [,2] [,3]
## [1,]   1    2    3
## [2,]   2    3    4
## [3,]   3    4    5

By specifying ncol = 3, we mean that the provided vector should be regarded as a matrix with 3 columns (and 3 rows automatically). You may feel the original vector is not as straightforward as its representation. To make the code more user-friendly, we can write the vector in multiple lines:

matrix(c(1, 2, 3,  4, 5, 6,  7, 8, 9), nrow = 3, byrow = FALSE)
##     [,1] [,2] [,3]
## [1,]  1    4    7
## [2,]  2    5    8
## [3,] ...

Array


An array is a natural extension to a matrix in its number of dimensions. More specifically, an array is a vector that is represented and accessible in a given number of dimensions (mostly more than two dimensions).

If you are already familiar with vectors and matrices, you won't be surprised to see how arrays behave.

Creating an array

To create an array, we call array() by supplying a vector of data, how this data is arranged in different dimensions, and sometimes the names of the rows and columns of these dimensions.

Suppose we have some data (10 integers from 0 to 9) and we need to arrange them in three dimensions: 1 for the first dimension, 5 for the second, and 2 for the third:

a1 <- array(c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), dim = c(1, 5, 2))
a1
## , , 1
## 
##     [,1] [,2] [,3] [,4] [,5]
## [1,]  0    1    2    3    4
## 
## , , 2
## 
##     [,1] [,2] [,3] [,4] [,5]
## [1,]  5    6    7    8    9

We can clearly see how we can access these entries by looking at the notations...

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Key benefits

  • Explore the R language from basic types and data structures to advanced topics
  • Learn how to tackle programming problems and explore both functional and object-oriented programming techniques
  • Learn how to address the core problems of programming in R and leverage the most popular packages for common tasks

Description

R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techniques.

Who is this book for?

This is the perfect tutorial for anyone who is new to statistical programming and modeling. Anyone with basic programming and data processing skills can pick this book up to systematically learn the R programming language and crucial techniques.

What you will learn

  • Explore the basic functions in R and familiarize yourself with common data structures
  • Work with data in R using basic functions of statistics, data mining, data visualization, root solving, and optimization
  • Get acquainted with R's evaluation model with environments and meta-programming techniques with symbol, call, formula, and expression
  • Get to grips with object-oriented programming in R: including the S3, S4, RC, and R6 systems
  • Access relational databases such as SQLite and non-relational databases such as MongoDB and Redis
  • Get to know high performance computing techniques such as parallel computing and Rcpp
  • Use web scraping techniques to extract information
  • Create RMarkdown, an interactive app with Shiny, DiagramR, interactive charts, ggvis, and more
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Table of Contents

15 Chapters
1. Quick Start Chevron down icon Chevron up icon
2. Basic Objects Chevron down icon Chevron up icon
3. Managing Your Workspace Chevron down icon Chevron up icon
4. Basic Expressions Chevron down icon Chevron up icon
5. Working with Basic Objects Chevron down icon Chevron up icon
6. Working with Strings Chevron down icon Chevron up icon
7. Working with Data Chevron down icon Chevron up icon
8. Inside R Chevron down icon Chevron up icon
9. Metaprogramming Chevron down icon Chevron up icon
10. Object-Oriented Programming Chevron down icon Chevron up icon
11. Working with Databases Chevron down icon Chevron up icon
12. Data Manipulation Chevron down icon Chevron up icon
13. High-Performance Computing Chevron down icon Chevron up icon
14. Web Scraping Chevron down icon Chevron up icon
15. Boosting Productivity Chevron down icon Chevron up icon
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