Andrie de Vries

R is more than just a statistical programming language. It’s also a powerful tool for all kinds of data processing and manipulation, used by a community of programmers and users, academics, and practitioners.To get the most out of R, you need to know how to access the R Help files and find help from other sources.

The rbind() function in the R programming language conveniently adds the names of the vectors to the rows of the matrix. You name the values in a vector, and you can do something very similar with rows and columns in a matrix.For that, you have the functions rownames() and colnames(). Guess which one does what?

In the R programming language, a conversion from a matrix to a data frame can’t be used to construct a data frame with different types of values. If you combine both numeric and character data in a matrix, for example, everything will be converted to character.You can construct a data frame from scratch, though, using the data.

One of the very attractive features of R is that it contains a large collection of third-party packages (collections of functions in a well-defined format). To get the most out of R, you need to understand where to find additional packages, how to download and install them, and how to use them. Poking around the nooks and crannies of CRAN The Comprehensive R Archive Network (CRAN) is a network of web servers around the world where you can find the R source code, R manuals and documentation, and contributed packages.

In addition to R For Dummies, there are many online resources for the aspiring or experienced R programmer. They'll help you find answers or learn valuable tips and tricks. (Or, they're just fun!) Cookbook for R The goal of the Cookbook for R is to provide solutions to common tasks and problems in analyzing data.

R has many functions that allow you to import data from other applications. The following table lists some of the useful text import functions, what they do, and examples of how to use them. Function What It Does Example read.table() Reads any tabular data where the columns are separated (for example by commas or tabs).

Vectors, lists, and data frames play an important role in representing data in R, so being able to succinctly and correctly specify a subset of your data is important. There are three main operators that you can use to subset your data: $: Extracts a single element by name from a list or data frame. For example, iris$Sepal.

Even with good introductory books on R, you'll need to use the R Help files. The R Help files provide detailed information about the use of different functions and their peculiarities. R has excellent built-in help for every function that explains how to use that function. Just about every Help page has some examples that demonstrate how to use that function.

Like any programming language, R makes it easy to compile lists of sorted and ordered data. To find substrings, you can use the grep() function, which takes two essential arguments: pattern: The pattern you want to find. x: The character vector you want to search. Suppose you want to find all the states that contain the pattern New.