R is open source and free. R distribution is maintained by Comprehensive R Archive Network (CRAN) site at https://cran.r-project.org/. From this site, you can download the R engine for multiple platforms, including Windows, Linux, and macOS X. Besides CRAN, Microsoft also gives a free R download on the Microsoft R Application Network (MRAN) site at https://mran.revolutionanalytics.com/, which is where you can get Microsoft R Open. This is the open source R from Microsoft, which is the core R with additional Microsoft libraries. If you install Visual Studio 2017, you can also get Microsoft R Open—actually, you get what's called Microsoft R Client, which I will introduce in a minute.

After you install R, you can start working by using the R console client tool. With this tool, you can write code line by line. You can use also any of a plethora of additional...

R engine is an interpreter. Therefore, R is an interpreted language. In addition, R is a case-sensitive and functional language. Instead of typing commands, you call functions to perform an action. For example, to quit an R session, you need to call the q() function. You should extensively comment your code. A comment starts with a hash mark (#); you can use the comment anywhere in the line of code.

Any code after the start of the comment does not execute. A semicolon (;) is a command delimiter if you write more than one command in a single line. A new line is the command delimiter as well. The following code example shows a comment, displays my R version, and lists the authors and other contributors to the language using the contributors() function:

# R version and contributors
R.version.string
contributors()

Here...

Now let's write some code that actually executes something:

1 + 2
2 + 5 * 4
3 ^ 4
sqrt(81)
pi

This code first evaluates three mathematical expressions using the basic operators. As you might expect, R evaluates the expressions using the mathematical operator precedence. The code calls the sqrt() function to calculate and checks the value of the constant for the number pi (π). The base R installation, or the base package, has many built-in constants. You can search the help for all pages that mention constants with ??"constants".

There are many ways to generate sequences of numbers, as you can see from the following code:

rep(1, 5)
4:8
seq(4, 8)
seq(4, 20, by = 3)

The first command replicates the number 1 five times with the help of the rep() function. You can generate a sequence of numbers with the help of the...

As promised, I am now introducing the most important data structures in R. When you analyze the data, you analyze a dataset. A dataset looks like a SQL Server table: you can observe rows and columns. However, this is not a table in the relational sense, as defined in the Relational Model, which SQL Server follows. The order of rows and columns is not defined in a table that conforms to the Relational Model. However, in R, positions of cells as crossings of rows and columns are known. This is more like a matrix in mathematics.

In the R dataset, rows are also called cases or observations. You analyze the cases by using the values in their columns, also called variables or attributes of the cases. 

I will introduce the following data structures in this section:

• Matrices and arrays
• Factors
• Lists
• Data frames

A matrix is...

This chapter gave you an overview of the R language. You learned the basics, including how to write R expressions and create variables. The chapter introduced the data structures in R, with an emphasis on the most important one, the data frame. You saw how to read the data from SQL Server, store it in a data frame, and then retrieve it from the data frame. In Chapter 3, Getting Familiar with Python, before doing some additional manipulation of the data in a data frame to prepare it for further analysis, we will have a short introduction to the Python language.