A Beginner’s guide to “What is R Programming?”

Last updated on Jan 10,2024 4.4K Views
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A Beginner’s guide to “What is R Programming?”

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There are 2.72 million jobs available in the field of data science with R and Python are the two pillars that make playing with data easier. In this article on What is R programming, I’ll be concentrating on explaining the basic concepts of R.

I will cover the following topics in this blog:

Over the due course of the blog, you will be tasked with questions and tips to help you understand the concepts better. If you’re stuck with doubts, please post them in Edureka Community to brainstorm with other learners.

R is an open-source tool used for statistics and analytics. It has become popular in recent years with its applications in the field of Data Analytics, Data Science and Machine Learning among others.

Before we get into features and basics of R Programming, let’s see a scenario where R is used in companies.

Facebook, an online social media-based company aims at improving user engagement, creating and sharing posts. It uses R for exploratory analysis, user engagement analysis, etc. Facebook Data Science group had released a series of blogs that showed an analysis of timeline posts made by users who were Single versus those In a Relationship. The following graph shows the average number of timeline posts exchanged between two people who are about to become a couple.

The above graph shows the steady change in the number of timeline posts 100 days before and after the relationship. The below graph shows the positive emotions increasing by using tags, words expressing positive emotions.

Now that we have an idea of what is R, let’s move onto the features of R.

Features of R

Features of R are:

Let’s move ahead to install R and RStudio.

Installing R & RStudio

Go to the R download page and click on the respective OS, click on base subfolder. You will find the downloadable link on the top of the page. Run the .exe file and complete the installation by pressing next and install. When you run the R Gui app, the R Console page will be visible at the start.

RStudio is an IDE used for R Programming which is available as open-source and commercial software for Desktop and Server products. Download RStudio Desktop from the RStudio downloads page. On the successful download of the file, run the .exe file and complete the installation. Open the RStudio App and you will see that the entire window is divided into 4 panes as below.

Note: In case any of the 4 panes are closed or hidden, Go to View -> Panes -> Show All Panes to view all panes.


Let’s move forward to learn what is a package and how to load the packages in RStudio.

R package & Libraries

R packages are a group of functions bundled together. These functions are pre-compiled and used in R scripts by preloading them. As discussed above, we can find the list of packages installed in the packages tab at the bottom right window. Let’s learn how to install packages in RStudio.

To install a package, use the following syntax in R Source or R Console.

install.packages([package-name])

By default, RStudio installs the packages from CRAN Repository. We can use the functions by loading the package into memory.

To load the package, use the following syntax.

library([package-name])

Try Installing the dplyr package in your system and find out what is it used for.

Variables & Data types

R Variables

Variable is the name of the memory location where data is stored. In other words, we can access memory data using variables. 

In R, we can assign variables using any of the following syntaxes. The below-mentioned example assigns the value Edureka to the variable Company.

Note: R variables are case-sensitive.

Variables can be categorized into Continuous and Categorical. If a variable can take on any value between its minimum value and its maximum value, it is called a Continuous variable. Categorical variables (sometimes called a nominal variable) are those that have a fixed number of values or choices such as “Yes”, “No”, etc.

Datatypes

R consists of 5 main data types: List, Data frame, Vector, Array and Matrix. There are 2 other types called factor and tibble, which are not primary datatypes but will be discussed below.

Let’s discuss all the data types in detail.

List 

A list holds a list of elements. These elements could include either number, decimal number, character, or Boolean value (True/False). They are mutable, i.e., the elements in a list can be modified using the index. A list can also contain a combination of lists, vector, array, and matrix. Let’s learn various list operations –

Try adding symbols ( $ . / & ) into a list. [Hint: Escape characters]

Note : Check the data type of variable using class(variable_name).

Try to add NULL into a list at any desired position

Most of you would have noticed [[ ]] and [ ] in list outputs. Find what is the difference between [[ ]] and [ ].

Vector

A vector is like a list but stores similar types of data, i.e. Numeric, characters or strings, etc. It converts all the elements into a single type depending on the elements in the vector. We can categorize a vector into the below types as shown in the image.

Let’s learn vector operations.

Vector Operations

Note: R has built-in constants.  Ex: letters[1:3] = {“a” “b” “c”}, LETTERS[1:3] = {“A” “B” “C”}

The rest operations are the same as a list which brings us to the question: What is the difference between a list and a vector?

Difference between list and a vector

Array

Array store data in more than two dimensions. It takes vectors as input and uses the values in the dim parameter to create an array.

The basic syntax for creating an array in R is −

array(data, dim, dimnames)

Where,

Example:

v1 = c(9,1,3)
v2 = c(1,7,9,6,4,5)
#Take these vectors as input to the array.
result = array(c(v1,v2),dim = c(3,3,2))
result

Output: 

, , 1
     [,1] [,2] [,3]
[1,]    9   1   6
[2,]    1   7   4
[3,]    3   9   5
, , 2
     [,1] [,2] [,3]
[1,]    9   1   6
[2,]    1   7   4
[3,]    3   9   5

What is the difference between NA and NULL?

Note: Check out the number of rows and columns of R object using nrow(var) and ncol(var).

Matrix

A matrix is a collection of data elements arranged in a two-dimensional rectangular layout.

The syntax to create a matrix is –

matrix(data, nrow, ncol, byrow, dimnames)

Where:

Example:

A = matrix(c(2, 6, 3, 1, 5, 7),nrow=2,ncol=3,byrow = TRUE)
A

Output: 

     [,1] [,2] [,3]
[1,]   2    6    3
[2,]   1    5    7

Data Frame

A Data Frame is a table-like structure that contains rows and columns. A data frame can be created by combining vectors.

The basic syntax for creating a data frame using is –

data.frame(vect1, vect2, ...)

Example:

id = c(1:5)
names = c("Srinath","Sahil","Anitha","Peter","Siraj")
employees = data.frame(Id = id, Name = names)
employees

Output:

  Id Name
1 1 Srinath
2 2 Sahil
3 3 Anitha
4 4 Peter
5 5 Siraj

Characteristics of a data frame

Note: Check out description of any variable using str(variable)

Tibble

A Tibble is a table-like structure similar to a data frame. Create a tibble variable using the following syntax:

tibble(list1,list2, ... )

Example:

id = c(1:5)
names = c("Srinath","Sahil","Anitha","Peter","Siraj")
employees = tibble(Id = id, Name = names)
employees

Output:

# A tibble: 5 x 2
     Id Name
  <int> <chr>
1    1 Srinath
2    2 Sahil
3    3 Anitha
4    4 Peter
5    5 Siraj

Let’s find out what makes a tibble different from the data frame.

Differences between Tibble and Data Frame

Note: Check out dimensions of any variable using dim(var).

Factor

A factor is another data type that is created while reading data from external data sources. While loading CSV or text files, it converts any column with categorical values to factor. Any vector can be converted to factor using below syntax:

Syntax:

as.factor(vector)

A factor converts categorical values into a numerical vector with multiple levels.

Example:

as.factor(names)

Output:

[1] Rahul Nikita Sindhu Ram
Levels: Nikita Rahul Ram Sindhu

Now we have learned different data types of R. Let’s move ahead and learn about operators in R programming.

Operators

R supports the following operators,

NameOperatorDescriptionExample
Addition+Perform the sum of the variablesa = 1; b = 2; c = a+b;  c = 3
Subtraction–Return difference of variablesa = 5; b = 2; c = a-b; c = 3
Multiplication*Return product of variablesa = 3; b = 2; c = a*b; c = 6
Division/Divide left operand by right operanda = 1; b = 2; c = a+b; c = 3
Exponent**Performs exponential (power) calculation on operatorsa = 3; b = 2; c = a**b; c = 9
NameOperatorDescriptionExample
Equal to ==Return True if both operands are equala = 1;  b = 2;  a==b;  FALSE
Not Equal to!=Return True; If both operands are not equala = 5;  b = 2;  a!=b; TRUE
Greater/ Lesser than>; <Return True;If left operand  greater right operand and vice vera.a = 3;  b = 2;  a>b; TRUE
Greater than equal to>=Return True; If left operand greater than or equal to right operanda = 3;  b = 2;  a>=b;  TRUE
Less than equal to<=Return True; If left operand lesser than or equal to right operanda = 3;  b = 2;  a<= b;   FALSE
NameOperatorDescriptionExample
Logical OR |Return TRUE, if at least one element is TRUEa = 1;  b = 2;  a==b;  FALSE
Logical AND&Return TRUE, if both elements are TRUE.a = 5;  b = 2;  a!=b; TRUE
Logical NOT!Return opposite or negation of elementa = 3;  b = 2;  a>b; TRUE

Assignment operator assigns value or variable to operand.

The assignment operators are =, <-,  ->.

Examples:

10 -> b
a = 5
c <- a+b

We have covered different operators used in R Programming, now let’s understand various Conditional, Looping and Control statements.

Conditional statements

R comprises 3 conditional statements which are –

Lets us discuss them individually.

If Statement

The flow of If statement:

As shown in the above picture, if the condition is true, then execute If code else executes the statements that come after if body.

Syntax:

if(condition) {
If code
}
statements

Example:

Grade = "Good"
if(Grade == "Good") {
print("Good")
}

Output:

[1] "Good"

Else If Statement

The flow of Else If Statement:

As shown in the above picture, if the condition is true, then execute If code else executes Else code and then follow the statements that come after the if-else body.

Syntax:

if(condition) {
If code
}
else {
Else code
}
Statements

Example:

Grade = "Good"
if(Grade == "Good") {
print("Good") 
}
else {
print("Bad")
}

Output:

[1] "Good"

If Else If Statement

The flow of If Else If Statement:

As shown in the above picture, if the condition is true, then execute If code else checks the second condition. If the condition is true, execute Else If code otherwise executes Else code followed by statements that come after if-else-if body.

Syntax:

f(condition) {
If code
}
else if (condition){
Else if code
}else {
Else code}

Example:

Grade = "OK"
if(Grade == "Good") {
print("Good")
}
else if(Grade == "OK") {
print("Ok")
}
else {
print("Bad")
}

Output:

[1] "Ok"


Switch statement

A switch is another conditional statement used in R. If statements are generally preferred over switch statements. The basic syntax of the switch statement is –

Syntax:

switch (expression, list)

Example:

switch(2,"GM","GA","GN")

Output:

[1] "GA"

Looping statements

Looping statements reduce the work of a user to perform a task multiple times. These statements execute a segment of code repeatedly until the condition is met.

R comprises 3 looping statements which are,

Lets us discuss each in detail.

For Loop

For loop is the most common looping statement used for repeating a task. A for loop executes statements for a known number of times. Define a for loop using the following syntax:

Syntax:

for(var in range){
statements
}

Example:

for(x in 1:10){
print(x)
}

Output:

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10

While Loop

A while loop repeats a statement or group of statements until the condition is true. It tests the condition before executing the loop body. A while loop is created using the following syntax:

Syntax:

while(condition) {
Statement
}

Example:

a = 5
while(a>0) {
a=a-1
print(a)
}

Output:

[1] 4
[1] 3
[1] 2
[1] 1
[1] 0

Repeat

Repeat loop is the best example of an exit controlled loop where the code is first executed and then the condition is checked to determine if the control should be inside the loop or exit from it. Create a repeat loop using the following syntax:
Syntax:

repeat {
statements
if(condition) {
statements
}
}

Example:

m=5
repeat {
m= m+2
print(m)
if(m>15) {
break
}
}

Output:

[1] 7
[1] 9
[1] 11
[1] 13
[1] 15
[1] 17

Control statements

R has the following control statements,

Lets us discuss each in detail.

Break

A break statement is used to stop or terminate the execution of statements. When the break statement is encountered inside a loop, the loop is immediately terminated and program control resumes at the next statement following the loop. If else and switch statements contain break statements usually to stop the execution. The syntax to use the break statement is –

Syntax:

break

Example:

m=5
repeat {
m= m+2
print(m)
if(m>15) {
break
}
}

Output:
[1] 7
[1] 9
[1] 11
[1] 13
[1] 15
[1] 17

Next

The next statement is used to skip the current iteration of a loop without terminating or ending it. The syntax of the next statement is –

Syntax:

next

Example:

for(i in c(1:6)) {
  if (i == "3") {
next
  }
  print(i)
}

Output:

[1] 1
[1] 2
[1] 4
[1] 5
[1] 6

Functions

A function is a set of statements to perform a specific task. R has in-built functions and also allows the user to create their own functions. A function performs a task and returns a result into a variable or print the output in the console.

R contains two types of functions,

Built-in Functions

Built-in functions are those pre-defined in R such as mean, sum, median, etc.

User-Defined Functions

User-Defined functions are defined as per the requirements. Define a function using the following syntax:

Function definition

function_name <- function(arg_1, arg_2, ...) {
Function body
}

Store the function definition in a variable and call the function using variable followed by optional parameters inside the parenthesis ( ).

Example

factorial <- function(n) {
if(n<= 1) { return(1) 
} 
else {
return(n * factorial(n-1)) 
}
}
factorial(3)

Output:

[1] 6

Scope of R programming

In this busy world, everybody learns a new language or technology for the sake of career, fame or salary. Before learning or taking up any course, this question would come to anyone’s mind “What is R Programming and why to learn R over other technologies and tools?”.

R has an excellent growth in various aspects such as Career growth, Job aspect, Business requirements, Cost, Salary, etc. It is open source and has been gaining a lot of audiences lately. It reduces half the burden to buy a licensed product. R is an All in one tool that not only performs analysis but is also used in making reports, dashboards, applications, etc. let’s discuss a few aspects of “why to learn R?’.

Salary

The need for people with R skills is increasing and so is the salary. Salary of engineers or programmers working with R varies between 3.9LPA to 20LPA. As shown in the image below.

Source: Payscale.

Job roles

The number of jobs available for R Programmers is increasing in recent years. There are different roles available for people with R Programming skills such as –

  1. Data Scientist
  2. Data Analyst
  3. R Programmer/ Developer
  4. Business Analyst
  5. Data Science Engineer
  6. ML Engineer

Career growth & Job opportunities

According to the various forums, data analysts will be in high demand in companies around the world. R is the most used analytics tool across the world which is estimated to have a wide range of users. Various companies such as Infosys, Wipro, Accenture, etc have grown in this domain to hire talented people as well as provide training to their employees.

I hope readers found this article “What is R Programming” helpful. Ask any queries related to this article or R Programming in the comments section or here. We will get back to you ASAP.

If you wish to learn R Programming and build a colorful career in Data Analytics, then check out our Data Analytics using R which comes with instructor-led live training and real-life project experience. This training will help you understand data analytics and help you achieve mastery over the subject.

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