R Tutorial for Beginners: Become an Expert in R Programming

The world today depends heavily on data for everything, especially to make decisions on future activities. The process of studying and analyzing data through scientific methods to extract insights from them is called Data Science. Data Science is a much sought after skill, useful to universities, individuals and companies across the globe. One of the ways you can begin data analysis and data science is by learning R. Learning R for beginners is more rewarding than challenging. The essential elements of R are very straightforward. 

Why is R so popular?

You might come across different software that gives you the same features as R, but R stands out. You might ask why? R is also called the language for statistical computing and is extremely popular for several reasons.

  1. Inexpensive: R is easy to download and completely free. It can be installed online, anytime and anywhere.
  1. Visualization Capabilities: Data visualization on R is more impressive than anywhere else. There are in-built R libraries that build displays and graphically present the data, making it more comfortable to understand.
  1. Easy to make packages: You can easily create a package on R. Packages are add-ons to the software, which allow you to do more than the basics.
  2. Dynamic Community: There are millions of people around the world who use R. They have online communities in the form of blogs and forums, which make interaction easier. 

Why should I learn R?

Amidst the several other data analytics tools, you might ask, “why should I learn R?” You should learn R because of how rewarding it is. Moreover, it is used for a lot of activities by huge companies globally and universities too. It is used for different purposes, like optimizing the financial portfolio, financial analysis and analyzing outcome trials. It has gained popularity and is recognized worldwide for data analytics and data science. 

Read: Python v R

How do I install R?

R is elementary to download and use online. You should ideally install R before downloading RStudio. RStudio is an environment developed for R. 

Will I face challenges while using R?

Learning R for beginners can be tricky, especially if you have no experience in programming. There is a common belief that R for beginners is challenging to master. One reason for this might be the fact that R is different from other data science tools. Another reason could be that there are several commands to be made for a single output. However, R is globally used and recognized. It can be more rewarding than challenging once the users get the hang of the data science tool.

What are the different data types in R?

To efficiently write programmes on R, you need to know the different data types. You will come across these terms frequently while using with R and interacting with the community that use R. The different data types on R are:

Character: A character is any letter inside “ ” or ‘ ’, like “a” or “total”.

Numeric: A decimal value on R is called a numeric. A numeric cannot be an integer. 

Integer: An integer is any whole number on R. An integer is an integer, but the opposite is not always true. “L” is generally added after a number to indicate that it is an integer. 

Logical: This implies a Boolean value, i.e., only True or False. These are often indicated with a T or F. 

Complex: This implies a complex number, which cannot be represented with integers or numeric alone. For example, 2+5i is a complex data type.

Other data frame functions are more complex and can be created. 

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What are the different programming features?

If you are a beginner, the very first things you need to know about R are the different programming features.

R Console: It is a part of the window where you can type in the commands. You can also view the results here. For example, if you type 1+2 here, you can view the result. You can use the console for experimentation and see the output. 

Variable: Variables are used to save numbers and values in R. You can save data for future use here. For example, if you want to store the dimensions of a rectangle, then you can enter width = 4 and height = 3. This will get stored in R for your future use. Also, these values can be changed later. 

Workspace: This is the area on your R that stores your variables. So, if you enter width and height here now, the values will show. It is your working environment on the data science tool.

R script: This is a text file with the commands that you have put in. You can always change your variable values.

Vector: This is the simplest data structure you can have on R. It is merely a sequence of data elements of the primary type. It is a 1-D array of data elements.

Matrix: This is only an extension of vectors. It is a 2-D array of data elements. So here, you can have your data in the form of rows and columns. 

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Wrapping Up

In a nutshell, learning R for beginners can be very interesting. You can use it for data analysis and leverage the increasing dependence on data and the availability of data sets. Other data analysis tools have their limitations, just like R. This statistical computing software is now being widely used across the globe.

How to learn R with a step-by-step approach?

R is a very popular programming language in the field of data science. If you are a beginner in this field, then people will often suggest you begin with the R programming language. It is relatively easy, but if you don't get onto learning it with a step-by-step approach, you might become frustrated.

Once you follow the right approach, you will stay on the right track and also learn R effectively. Find out your purpose of learning R to keep up with the motivation. Start learning the basic syntax through tutorials and online courses. Work on structured projects. Build some projects of your own to gain hands-on experience. Enhance the level of difficulty and improve your skills.

Is the R programming language worth learning?

The R programming language is a very useful one when it comes to data science and statistical analysis. There is a huge community of enthusiasts supporting both Python and R programming languages. If you have a decent knowledge of statistics, then you will find it pretty easy to get along with learning R.

You can rely on the R programming language no matter what type of analysis work you have to perform. There are several tools also provided to make your work simpler. On top of that, R is also considered to be the perfect fit for big data solutions. There are plenty of organizations looking for well-versed professionals with R. It is definitely worth it to learn R.

Is it possible to use R for big data?

There are plenty of built-in statistical commands in R, which makes it the most widely used programming language for data analysis. R is open-source software, which makes it a favorable choice for data analysts and statisticians.

There are several new packages being developed by the R community, along with constant updates being made to the old packages for supporting and handling big data.

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