In the data science world, R is a massively popular programming language. However, learning R can be challenging if you aren’t sure how to approach it. If you have struggled to learn R or other programming languages earlier, you are definitely not alone. And it isn’t a failure on your part; it’s usually the outcome of a mismatch between what is motivating you and how you are actually learning.
This mismatch causes huge problems when you are learning any programming language as it takes you directly to the cliff of boring. What’s the cliff of boring, you must be wondering. Well, it’s the cliff of boring coding syntax & dry practice issues you are usually asked to work for before getting to the great stuff.
Plenty of students drop off at the cliff as R is totally worth learning. In fact, R has some enormous benefits over another language for anyone who is excited to learn data science:
- The R tidyverse ecosystem makes everyday data science tasks very simple.
- R was developed to perform statistical computing.
- Data visualisation in R can be powerful and simple.
- The online R community is friendly and most inclusive of all programming communities.
- IDE (Integrated Development Environment) is a potent tool for programming with R since all of your code, results, and visualisations are in one place.
Learning R can be helpful for your career. Data science is a growing field with high average salaries. Most of the top technological firms recruit R coders for data science-related job roles. R is used in firms around the world, in every industry that does analytics. Therefore, let’s jump in the 5 steps of learning the R programming language rightly.
1) Find Your Inspiration For Learning The R Language
Before cracking a textbook, register for a learning platform or click play on your first tutorial video; invest some time to really think about why you wish to learn R and what you would like doing with it.
- What data are you keen on working with?
- What questions do you want to respond to?
- What projects would you enjoy creating?
Find something that inspires you in the process. This will help you realize your end goal and help you reach that end goal without boredom.
Try getting deeper than becoming a data scientist. There are all sorts of data scientists who work on various problems and projects. Are you excited about assessing language? Forecasting the stock market? Diving deeper into sports stats?
Choose one or two things to interest you and you are willing to stick with. Speed up your learning towards them and create projects with your interests in mind. Determine what motivates you will help you figure out an end goal. You don’t have to determine an exact project, just a general area you are interested in as you are ready to learn R.
2) Learn The Basic Syntax
Syntax is a programming language that is even more important than syntax in human language. Learning syntax can be pretty dull, so you must aim to invest as little time as possible doing syntax learning. Rather, learn as much of the syntax as you can while working on real-world issues that interest you so that there is something to keep you stimulated even though the syntax isn’t all that exciting.
Below are some resources to learn the basics of R:
Dataquest: Introduction to R Programming language
- R Style Guide
- RStudio Cloud Primers
- R for Data Science
- RStudio Education
The faster you can get working on projects, the quicker you will learn R. You can always refer to various learning & double-checking syntax resources if you get stuck later.
3) Work On Structured Projects
Once you have got adequate syntax, you are prepared to move on to structured projects more freely. Projects are excellent ways to learn as they let you implement what you have already learned while usually also challenging you to learn new things and resolve issues as you go. Moreover, creating projects will help you put together a portfolio you can display to future employers down the line.
You surely don’t want to jump into unique projects so soon. You’ll get stuck too much, and the process could get frustrating. Rather look for structured projects until you can develop a bit more experience and increase your comfort level.
There are manifold structured projects out there to work on. Let us look at some good resources for projects in every area.
- Data science or data analysis
- Data visualisation
- Machine learning or predictive modeling
- Reproducible reports
- Dashboard reports
4) Create Projects Yourself
After finishing some structured projects, you are probably ready to move to the next stage of learning R. Working on unique projects that fascinates you will give you a great idea of how far you have come and what you might want to learn next. Although you will be creating your project, you won’t be working alone. You will still be referring to resources for help & learning new strategies as you work.
Below are some excellent resources for finding help if your R projects:
- Dataquest’s Learning Community
5) Increase The Difficulty
Working on projects is incredible, but if you wish to learn R, you must ensure that you keep learning. For instance, you can do too much with data visualisation, but that does not mean you should develop 20 projects consecutively that only use your data visualisation skills. Every task should be a little more complicated and a bit more complex than the previous one. If you aren’t sure exactly how to do that, below are some questions to ask yourself:
- Can you train a beginner how to make this project writing a tutorial?
- Can you enhance its performance? Could it run quicker?
- Can you enhance the visualisation? Can you make it engaging?
- Can you ramp up your project to handle more data?
Keep Learning R
Learning R is indeed a challenge, even if you take this approach. However, if you can find the right motivation and keep yourself involved, anyone can reach a high level of expertise. Hope this guide has been a great help for your journey towards learning the R programming language. HData Listed One of the Trusted Big Data Analytics Companies by Top Mobile App Development Companies.