# Introduction

The aim of this article is to provide an overview of the GNU R programming language. It starts a series of articles devoted to programming with R. Its objective is to present, in an organized and concise manner, the elementary components of the R programming language. It is designed to help you understand R code and write your own. It is assumed that the reader has already some basic programming knowledge of R. If you are not familiar with any of R features it is recommended that you first read A quick GNU R tutorial to basic operations, functions and data structures.

# Expressions

An R **expression** is an elementary component of R code. Expression in R can be:

- assignment statement;
- conditional statement;
- arithmetic expression.

**Examples of R expressions**:

> y<-100 > if (1==1) 1 else 0 [1] 1 > 100/5 [1] 20

R expression are constructed from objects or functions. It is common to separate them with a new line, however, you can also separate expressions with semicolons as below.

> "LinuxConfig.org";sin(pi);5^7 [1] "LinuxConfig.org" [1] 1.224647e-16 [1] 78125

# Objects

An R **object **can be thought of as an elementary component (“thing”) of R programming language. For instance, the R objects are:

- numeric vector;
- character vector;
- list;
- function.

**Examples of objects in R:**

> c(1,5,2,7,9,0) [1] 1 5 2 7 9 0 > c("GNU R programming tutorial","LinuxCareer.com") [1] "GNU R programming tutorial" "LinuxCareer.com" > list("GNU R programming tutoial",c(1:5),"this is also an object in R") [[1]] [1] "GNU R programming tutoial" [[2]] [1] 1 2 3 4 5 [[3]] [1] "this is also an object in R" > function(a,b) {a/b} function(a,b) {a/b}

# Symbols

A **symbol** in R is a variable name. So if you assign an object to a variable name you actually assigning an object to a symbol. An **environment** in R, then, is a set of such symbols that are created for a given purpose.

**Example of symbols in R:**

> x<-3 > y<-"R tutorial"

Above, x and y are symbols.

# Functions

A **function** in R is an object that takes as arguments other objects and returns an object as a result. Did you know that the assignment operator ‘<-‘ is a function in R? You can instead of writing

> a<-1

call ‘<-‘ function with arguments ‘a’ and ‘1’ as indicated below

> '<-'(a,1)

Some examples of functions in R include:

- ‘<-‘ assignment operator;
- ‘+’ summation;
- ‘if’ statement;
- ‘[‘ vector reference.

**Examples of functions in R:**

> '+'(1,1) [1] 2 > 'if'(1>3,"one greater than three", "one less than three") [1] "one less than three" > '['(a,1) [1] 1

# Other properties of R language

## Objects are immutable in R

In R objects are immutable. This means that R will copy the object not just reference to the object. Consider the following example. We define a function that sets the ith element of vector ‘x’ to 4 as below

> f<-function(x,i){x[i]<-4}

Let’s see what happens if we define a vector ‘w’ and pass it as argument to the function ‘f’.

> w<-c(1,3,6,7) > f(w,1) > w [1] 1 3 6 7

What we just observed above is that the vector ‘w’ was copied when it was passed to the function so that the function does not modify this vector.

## Everything in R is an object

Everything in R is an object. Objects are not only defined to store data as in the case of vectors, lists or other data structures. Other examples of objects in R are functions, symbols or R expressions. For instance, function names in R are symbol objects that point to function objects as indicated below

> functionname<-function(x,y) x+y > functionname function(x,y) x+y

## Special values in R

There is a number of special values used in R. These are:

**NA**, used to represent missing values, means “not available”;**Inf**and**-Inf,**resulting in a calculation when the output number is too big or too small or when dividing by zero;**NaN,**resulting in a calculation that is not possible to compute such as division of zero by zero, means “not a number”;**NULL,**used often as an argument in functions, means that no value was assigned to that argument.

## Coercion

R often coerces values from one type to another. For instance, when you call a function with an argument of a wrong type, R will try to convert this argument to a different type so the function can work. Another example might be when we define a vector with numeric values, R will assign it a type “integer” as below

> x<-c(1:10) > typeof(x) [1] "integer"

Now, when we change the forth element of vector ‘x’ to four, R will automatically change the type of the vector to ‘double’ as indicated below

> x[4]<-4.1 > typeof(x) [1] "double"

## The R interpreter

An **interpreter** is a program that executes the written code. There is no need to compile R code into an object language as in the case of C, C++ or Java. This means that R is an interpreted language.

R interpreter evaluates R expressions in few steps. First, it parses an expression changing it into an appropriate functional form. Let’s call the **quote() **function to see how this happens.

> typeof(quote(if(1>3) "one is greater than three" else "one is less than three")) [1] "language"

The R expression above returned a “language” object. To see how R evaluates an expression we produce a parse tree.

> as(quote(if(1>3) "one is greater than three" else "one is less than three"),"list") [[1]] `if` [[2]] 1 > 3 [[3]] [1] "one is greater than three" [[4]] [1] "one is less than three"

Let’s also apply the **typeof() **function to the elements in such list, which shows how the expression is interpreted by R.

> lapply(quote(if(1>3) "one is greater than three" else "one is less than three"),typeof) [[1]] [1] "symbol" [[2]] [1] "language" [[3]] [1] "character" [[4]] [1] "character"

As you can see some parts of the** if** statement where not included in the parsed expression. That is, the **else **element. Additionally, it is interesting to note that the first item in the list is a symbol, which points to the **if()** function. Even though the syntax for the **if** statement differs from the function call, the R interpreter translates the expression into the function call with the name of the function as its first argument and other arguments as in the list above.

# Conclusion

This article is an introduction to the R programming language. In the forthcoming articles we will focus in detail on the defined here elements of the R language.

**GNU R tutorial series:**

**Part I: GNU R Introductory Tutorials:**

- Introduction to GNU R on Linux Operating System
- Running GNU R on Linux Operating System
- A quick GNU R tutorial to basic operations, functions and data structures
- A quick GNU R tutorial to statistical models and graphics
- How to install and use packages in GNU R
- Building basic packages in GNU R

**Part II: GNU R Language:**