An Guide to Learning Data Science in R from Scratch

Imagine your shoe is torn, and you are complaining about it to your friend. The next thing you know, you get a pop-up notification from amazon advertising new, comfortable footwear. I know this sounds extremely scary, but do you realize the power of data in today’s world?

In the present time, data is the new age gold. Many depend on the data stored, from your personal to national security. This naturally makes data analysis a very crucial task to do, and thus we need r programming help more efficient data analysts today who can make fewer errors and give us more useful yields. 

Of course, you are interested in the field, and that is why you are here. You should know that for efficient data analysis, you need to ace suitable programming languages. As per the data, scientists’ R programming language is one of the best coding languages that you can use for efficient data analysis. I will tell you how to use R for data analysis via this blog. It will tell you everything in detail from scratch. So keep reading. 

Disclaimer: It is okay if you do not have any prior knowledge about R. 

An Overview of R

When it comes to data analysis or statistical computing, there can be no better language than R.  Developed in the early 90s; it has constantly evolved since then. The user interface of R today is better than ever before. It is way more interactive than a programmer can expect it to be. Although initially programmers only considered it to be efficient for statistical computing, today they agree that R has more in it stored for the programmers. Programmers around the globe have collectively agreed to the fact that R has enough provisions via which one coder can implement machine learning algorithms conveniently.

Why Learn R?

If you want to learn data analytics, you have two options: Python or R.

Here are the benefits of choosing R – 

• Coding is no big deal.
• There is no concept of paying subscription charges.
• You can get instant access to 7800 packages and more. You can use these packages for several computation tasks in R.
• The computing experience is something you have never experienced before.
• You can get several forums that can help you in case you are stuck at any point. 

To use R, the first step you must take is to install R. Here are the details for the same.

How to install R?

R studio is available for Windows Vista and above versions. Here are the steps to install R – 

• Visit the website
• Here click on the Downloads option; most preferably, the site will show you a ‘DOWNLOAD R FOR WINDOWS’ button. Click on the same.
• You have to make sure that you download the executable file, and for that, tap on the ‘INSTALL R FOR THE FIRST TIME’ option.
• The very next thing to do is to run the executable file. This will begin the installation process. You will get the usual pop-up from your device “whether you want to allow the app to make changes to your device” click on the yes option and then select the installation language.
• You will have a set of instructions in front, and follow them till you reach the FINISH button. 
• With this, you will have R successfully installed on your machine, ready to compile the tricky codes you write. 

In case you want to download it for MacO X, the initial steps are the same. Only make sure to – 

• Download the latest version of the R GUI and 
• Run the .pkg file.

After this, you only have to follow the installation instructions.

As you open the R Studio, the interface will surely seem foreign to you, but read to understand the R interface.

Now let’s directly move into the basic computations in R.

Essentials of R programming – 

• Read this section very carefully because this is the basic building block of your learning of R. 
  • Logical First things first, everything you see or create in R is referred to as an object. Everything in R is an object from a variable to a data frame. 
  • R has five classes of objects – 

• Character 
• Numeric 
• Integer 
• Complex 

Note: The numeric refers to the ‘Real Numbers’; integer refers to the ‘Whole Numbers’; Logical refers to ‘True or false’. 

• These objects can have the following dimensions – 

• Name
• Particular dimension
• A class
• Definite length

Remember that attributes of any object can be accessed using the function ‘attributes ()’.

Pay attention to this part; it might seem confusing, but it is crucial. 

R has a very basic object. It is called a vector. To create an empty vector, you can use vector (). These vectors are used to have the objects belonging to the same class.

The next thing on the list is the

R Data Types – 

Data types in R include 

• Vector – 

As mentioned earlier in this blog, vector mainly stores objects of the same class. But one can also store objects of a different class, but in that case, the different types of objects are strictly converted into one class. Know that to covert the class of a vector; you need to use the command ‘as’. 

• Matrices – 

When you introduce a vector with a dimension attribute that is a row and column, it is referred to as a matrix. It usually contains objects of the same class. To obtain the dimensions of any matrix, you have to use any of the following commands –  dim () or attributes ().

• Data Frames – 

Data frames are used to store tabular data. Highlight it for this is one of the most used data types in R. it might sound similar to the matrix, but it is not the same. In a matrix, you can only store elements of the same class, whereas in a data frame, you can input vector lists from different classes. 

• List – 

A list is also a type of vector, but it only contains objects of different data types. 

Moving on, we have 

Control Structures in R

If you are familiar with programming, you must know what control structures are. But for those who are new in the coding world, control structures are responsible for controlling the flow of commands within a function. 

Here is the list of all control structures in R – 

• If, else – 

This is mainly used to test a condition.

The syntax is shown in the image below – 

• for – 

This control structure is used while executing an iterative statement or a loop, as you may call it. The main criterion here is that the loop must be executed a fixed number of times.

The syntax is shown in the image below – 

• while – 

This is also an iterative statement but unlike for it starts with a testing condition. The loop executes only if the testing condition is true. It continues the process until and unless the testing condition is false. 

The syntax is shown in the image below – 

These three are the main control structures, but R also has other control structures which are less frequently used. They are – repeat. Next, break, return. 

Lastly, let’s have a look at a few of the most useful packages of R – 

• Data Visualization – 

This package of commands intends to help you in creating graphs and all other plotting activities. But beware – do not try using it while setting up advanced graphics. 

• Importing Data – 

Data in whichever format can be imported using these packages. But then, this can get a bit complex while importing larger files.

• Modeling – 

If you need to create a machine learning model, then R can provide you with a package for the same. It is referred to as the care package. 

• Data Manipulation – 

Want to do the computations in a hassle-free way? Data manipulation packages in R are the simplest way to do so. 

Parting Thoughts – 

This is not the end. You still need to know several other things to call yourself an advanced R coder. But these are the basics. Until and unless you master these, you can never ever ace data analytics through this programming language. To know more about R and its programming, 

About the author – 

Anne Gill, a computer science educator from UK, is associated with MyAssignmenthelp.co.uk to guide students with their assignments. In addition, she has her own page on social media platforms, using which she offers computer science assignment help uk on various topics of her subject.