R is one of the oldest statistical programming languages in the world. A group of statisticians created it to do complex statistics calculations with ease. Nowadays, R has become one of the most significant languages for data science and data analytics. And it is getting more popular with every passing year. It is competing with the simplest programming language in the world.

R is not the simplest programming language; it is a complex programming language for beginners. They always get frustrated when they start learning data science with R. The majority of learners struggle to learn R programming. R is the complex language for those who don’t have any programming background.

But once you start with R programming, it is becoming easy and convenient to perform data science operations using R. If you are struggling to learn data science with R, you should know the best approach to start learning. It would help if you did not master every single aspect of R programming for data science.

**Data Science With R**

It would help if you cleared all data science libraries and modules. If you also cleared the basics of R programming before digging into it. Please have a look at some key points that why we should perform data science with R.

- R has one of the most effective ecosystems for data science i.e., tidyverse. It is quite easy and convenient to perform data science tasks using tidyverse.
- R has one of the best support and features for data visualization. You can create almost any type of graphs and charts using R programming.
- It is specially designed for statistical computation. Therefore you can perform any statistical operations using R programming.
- R has one of the enormous programming communities in the world where you can find lots of programmers to help you.

It has become quite convenient to code in R programming using R studio. It is an integrated development environment for R programming. R studio provides the best in class GUI that allows you to code and get the same results. Rstudio cloud also allows you to code in R using your web browser.

It is just a few advantages to do data science with R. There are many other advantages of using R with data science. You can have a great career in data science if you learn R programming for data science. Most of the tech giants are preferring R for data science over python. It is also a crucial programming language for almost every tech giant to handle massive amounts of data using statistics operations. Let’s explore the best ways to learn data science with R:-

**Get Motivated to Learn R**

We have mentioned earlier that R is one of the complex programming languages for beginners. It is not easy for beginners to stay motivated to learn R programming. Therefore, they need to find our best motivation source to start learning data science with R. If you start learning R from a textbook, you may get demotivated.

That is why you should join the r programming online course where you can have the best learning methods, quizzes, and games. It will help you stay motivated while learning data science with R. You can also set the goal inside your mind and treat that goal as your motivation to learn R programming. Always try to clear all the concepts deeply rather than just focusing on being a data scientist.

The best way to get motivated is, “do what you love and love what you do”. It will help you to stick with your goal and stay motivated. You should start your Data Science project and keep the end goal in your mind. You can pick the data visualization project, predictive modeling, dashboard reports, and many other topics.

**Start with the Basic Syntax**

We have seen that most learners take the overview of R programming’s basic syntax and then start implementing the advanced syntax. Do you know that syntax is the soul of almost every programming language in the world? If you want to master any programming language, then you should clear its basic syntax first.

It would help if you were quite careful with the syntax because a single mistake in your syntax won’t let the computer interpret your programming. It would help if you spent enough time learning the syntax. Don’t try to be over smart; take your time to learn the syntax. The best way to do so is by implementing each syntax you learn to solve the real world problem with your project’s help.

Keep in mind that you have a project, and you are free to implement almost everything in your project. There are thousands of online courses, YouTube videos, books, journals, and other mediums where you can start learning the basic syntax of R programming. If somehow you stuck with the syntax, you have the online community’s help to get rid of the problem in your syntax. Try to clear the basic syntax within a week or a couple of weeks.

**Work on Structured Projects**

After done with R programming’s basic syntax, then the next step is to work on structured projects. It would help if you worked on the structured projects. It will help you to learn and discover more things. If you did not work on a completely new project until you could work independently on the project. Let’s have a look at some good resources for projects:

**Data science / Data analysis**

For a data analysis project, you can use TidyTuesday. It is a semi-structured, weekly social data project in R. In this, the R programmers clean, wrangle, tidy, and plot a new dataset every Tuesday. They weekly posted the new datasets, and the results show on Twitter using the hashtag #tidytuesday.

**Data visualization**

R is offering the best packages for data visualization. You can create the data visualization project in R using ggplot2. With the help of this package, you can create professional graphs easily. Apart from that, you can also use the rayshder package in R programming. These packages allow you to build 2d and 3d maps in R. Apart from that, you can also transform your graphic that you have developed in ggplot2 into 3d with this package.

**Predictive modeling/machine learning**

You can use Tidymodels packages in R programming that will allow you to perform machine learning operations and do modeling with ease. You can also build the predictive model using this package. This package has many tools that allow you to perform a grid search, nested resampling, and Bayesian methods. You can also perform a statistical analysis using this package.

**Build Projects on Your Own**

After the structured projects, the next step of learning data science with R is to build your own projects. Yes, it is because you can never judge your progress until you don’t implement what you have done. It would help if you tried to build those projects in which you are interested. While doing the project, you will face many challenges and find the opportunity to learn more.

There are thousands of learners building their data science project with R; it means you are not alone. Thousands of available resources will help you learn new techniques and find the best solution to your problems. There are lots of packages in R programming, and each package has its own role in data science.

When you start building a project, then there is always a probability that you can learn some new packages. Here is the list of a few online resources that will help you when you stuck in a problem with your R projects:-

- StackOverflow
- Google Answers
- R Community
- dev.to

**Ramp Up the Difficulty**

Working on a project is not the end. It may help you to clear a certain point. But all the projects have different challenges. It means that whatever you have implemented on your project may not work for the other project.

That is why you need to keep learning R programming. There are many possibilities in data science with R. You should create a couple of R projects, and each project should be a little tougher and a little more complex than the previous one.

It will allow you to learn something new in R programming every time. You can search over the internet from the easiest to moderate and then the complex Data Science with R projects.

**Never Stop to Learn New Trends **

Learning is a never-ending process. You can reach a certain comfort level using a programming language, but it is nearly impossible to master a programming language within a few years. There are always new things kept adding in programming languages. And the world of data science is also evolving with every passing year.

Therefore you should also learn the new trends and techniques in data science with R. You need to be curious to learn new things every time, never stop learning. It will help if you keep engaging with the new projects to test your skills and explore new data science challenges with R.