R is an increasingly popular programming language, particularly in the world of data analysis and data science. But learning R can be a frustrating challenge if you’re not sure how to approach it.

R is an increasingly popular programming language, particularly in the world of data analysis and data science. But learning R can be a frustrating challenge if you’re not sure how to approach it.

If you’ve struggled to learn R or another programming language in the past, you’re definitely not alone. And it’s not a failure on your part, or some inherent problem with the language. Usually, it’s the result of a mismatch between what’s motivating you to learn and *how* you’re actually learning.

This mismatch causes big problems when you’re learning any programming language, because it takes you straight to a place we like to call the cliff of boring.

What is the cliff of boring? It’s the mountain of boring coding syntax and dry practice problems you’re generally asked to work through before you can get to the good stuff — the stuff you actually want to do.

Nobody signs up to learn a programming language because they love syntax. Yet many learning resources, from textbooks to online courses, are written with the idea that students need to master all of the key areas of R syntax before they can do any real work with it.

That’s where new learners tend to drop off in droves. You get excited about learning a programming language because you want to *do* something with it, and but then you’re immediately led to this huge wall of complicated, boring stuff that’s between you and what you actually *want* to be doing. It’s no surprise that lots of students give up or drop off at points along their climb up this “cliff.”

There’s no way around learning syntax, in R or any other programming language. But there *is* a way to avoid the cliff of boring.

It’s a shame that so many students drop off at the cliff, because R is absolutely worth learning! In fact, R has some big advantages over other language for anyone who’s interested in learning data science:

- The R tidyverse ecosystem makes all sorts of everyday data science tasks very straightforward.
- Data visualization in R can be both simple and very powerful.
- R was built to perform statistical computing.
- The online R community is one of the friendliest and most inclusive of all programming communities.
- The RStudio integrated development environment (IDE) is a powerful tool for programming with R because all of your code, results, and visualizations are together in one place. With RStudio Cloud you can program in R using RStudio using your web browser.

Pick up some top tips for learning R from Shelmith Kariuki, a certified Tidyverse instructor and a leader in the Africa R community.

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Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.

We are going to learn the introduction of machine learning and linear regression in R 4.0 programming. We will start with the introduction of machine learning then we will discuss the introduction of linear regression. I will also discuss types of linear regression and use cases of linear regression. there are two types of linear regression; simple linear regression and multiple linear regression. Use cases of linear regression are in house price prediction, stock price prediction, Twitter popularity prediction. I will thereafter show you how to analyze the Boston housing dataset. We will analyze dataset variables to understand the variable dependency for the linear regression model. I will show you the linear and non-linear regression models. Thereafter, I will show how you can improve the accuracy of a linear regression model.

Implementations regarding all of above experiments alongside the different result plots are provided in GitHub repository.