Book Image

Modern R Programming Cookbook

By : Jaynal Abedin
Book Image

Modern R Programming Cookbook

By: Jaynal Abedin

Overview of this book

R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. The objective of this book is to show how to work with different programming aspects of R. The emerging R developers and data science could have very good programming knowledge but might have limited understanding about R syntax and semantics. Our book will be a platform develop practical solution out of real world problem in scalable fashion and with very good understanding. You will work with various versions of R libraries that are essential for scalable data science solutions. You will learn to work with Input / Output issues when working with relatively larger dataset. At the end of this book readers will also learn how to work with databases from within R and also what and how meta programming helps in developing applications.
Table of Contents (10 chapters)

Converting a matrix to a data frame and a data frame to a matrix

A matrix is a two-dimensional arrangement of data with rows and columns where each row/column is of the same data type, either all numeric, all character, or all logical. Moreover, the number of elements in each column should be the same, and the number of elements in each row should also be the same.

A data frame is also a two-dimensional arrangement of data with rows and columns, but each column could be of very different types; for example, a data frame may contain both character and numeric columns. However, the number of elements in each column should be the same. Since both data structures share some common properties, they could be converted from one structure to another. In this recipe, you will learn to convert a matrix to a data frame and a data frame to a matrix.

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