10 Tips Data Science with R Programming

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R is a very popular choice when it comes to programming in data science and statistics. Both newbies and seasoned data scientists use R to crunch their data. Created in the 1990s by Ross Ihaka and Robert Gentleman, R has always been about statistics and it has only gotten better.

We often get asked why R is very instrumental in data science and why it is very popular. To help highlight the importance of R in data science, we thought it wise to pen this Guttulus blog on R in data science, its significance and some of the R courses you need to take if you are to make it in data science.

Here are 10 tips on R in data science and some of the R courses that you will need to take. Here are the tips:

10 Tips Data Science with R Programming

R is effective in Data Wrangling

Data wrangling is the process of untangling messy and complex data sets and arrange them into meaningful categories which can easily be analyses and represented. R has a very large library of databases and tools which help manipulate the stored data and wrangle it into meaningful strata.

R is very popular in Academia

The other reason why R has always been a key player in the world of data science, is the fact that it is used by researchers and scholars to experiment with data. A lot of reference material and popular learning resources on modern day data science use R as the primary programming language and this will not change any time soon.

R is crucial in data visualization

R has very many data visualization libraries and features which are needed in data science. Data visualization and presentation is very important in data science as it helps scientists to easily pick up trends and also debug their codes. Things like graphical representations are easier and more interactive when using R in data science.

R is bespoke for statistical analysis

R is a specifically designed for things such as statistical analysis and reconfiguration of data. The language is specifically designed to analyze data, present is visually and manipulate it with ease. A lot of statistical methods are enabled through R libraries which makes it perfect for all matters data science.

R is perfect for training systems and automation

The other reason why R is regarded as an important tool in the world of data science, is the fact that it is perfect for training computer systems and for automation. Automation is a key aspect in data science and R is making this possible thanks to its extensive package list.

R is also open source

The other thing that makes R ideal in the world of data science is the fact that it is open source. Anyone can therefore access it and the fact that it is cheap makes it ideal for any project. Anyone can access R and its large library and use it for whatever project.

R is scalable as well

Scalability is very important in data science and computer science at large. This is why it is very important for developers to opt for languages which offer scalability. R is highly scalable and faster when compared to the other programming languages in data science. It is therefore not surprising that most people prefer it to quickly develop applications which might need scaling in future.

R is easy to learn and use

Usability is very important in terms of programming languages especially in a technical field such as data science. It is pretty straight forward to learn and once you have grasped the basic concepts, advancing to fundamentals will be very simple. The syntaxes of R are also very relatable and don’t need to be commented out for you to understand them.

R has a large community

The community using a programming language matters a lot. Without a large interactive community, using a programming language becomes very difficult as you can’t consult on problems and brainstorm on possible improvements. Fortunately, R has one of the largest communities and user bases in the world.

R courses are available online for free

The beauty of R is that most of its basics and fundamentals courses are available online for free and anyone can advantage of them. If you are planning to get started on R, all you need is to hop online today, look for an ideal course to get started and in no time, you will be able to rock it in the data science world.

Give Guttulus a shout to get started in R programming

If you are looking for the best tips and guidance on R programming in data science, you have every reason to give Guttulus a shout. Join our mailing list today and ensure that you read our blog on a regular basis for more tips and guides on how to get started in R programming.

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