Data science is one of the newest, most sought-after fields in the information technology sector and with good reason. Big data is a relatively recent phenomenon that continues to experience exponential growth as more and more devices, and as such data points, connect to networks and gather many valuable data.
Data alone does not fix the problem, however. The data that is collected needs to be collated into information and one of the ways this can be accomplished is through R.
R programming, a derivative of the S programming language, has been around since 1995, with the first stable version released in 2000. As the advantages of big data became ever so clear, R, experienced exponential growth as is today one of the most sought-after programming languages in the statistical computing sphere.
This makes R programming a valuable skill to have in today’s world, with courses in R programming language being offered from Knowledgehut that will get you up to speed in this exciting field.
R is an object-oriented language (OOP), released under the GNU GPL license which means that it is free to download and use. It makes use of an extensive library that allows programmers to quickly extend the capabilities of the software they are designing.
R is also considered to be an interpreted language which means instructions are executed directly and freely without the need to compile the code every single time. There are also several IDE (Integrated Development Environments) available for programming in R, most notably R Studio which one can also download and use for free.
As datasets continue to get bigger, with the information derived from such large datasets contributing to competitive advantages in a very competitive world, a tool that allows users to use and manipulate data is much required. Whilst tools like Microsoft Excel can be easily used and have a lot of capabilities when the dataset grows large enough, R becomes one of the few options available.
Its versatility and the huge community behind it ensures that it is easy to use, whilst extending the functionality beyond what is available in tools such as Excel.
In a world where IoT (Internet of Things) is becoming ever more prevalent, datasets can grow into the hundreds of Gigabytes very quickly. Using R, users are able to easily manipulate these large datasets using packages that are freely available. Packages are essentially functions that have been pre-programmed for your use.
This saves a lot of time from having to write the code yourself and allows for quicker turnaround of projects that would otherwise take much longer to finish. Packages are available through CRAN which stands for Comprehensive R Archive Network which allows users to browse, learn about, and download from its library of packages of which there is over 6,000.
Of course, you can always develop your own packages, which can be done through R itself. Whilst the goal of data science tends to lean towards automation as much as possible, the goal of what you want to accomplish will determine the approach to be taken.
Data structures are perhaps one of the more fundamental things, one needs to know when it comes to programming in R. Whilst there are several ways to import data in R, all data is stored in the appropriate structures and as such a deep knowledge of this is well required.
Once the data is imported, which can be done through files, database connections, mining, and so forth, looping and control become the next logical step.
Not all data is created equal, and more often than not some sort of cleaning is required before data can be put to good use. It is well known that information is only as good as its source, and through the cleaning process, users are able to normalize, label and correct data so that any models or predictions derived from such data are as correct as possible. This is achieved once data is successfully stored in a data frame with suitably labeled columns and is representative of the value domain in which it is stored.
Functions including loop functions come next which is how actions, in the form of commands and arguments, are expressed to derive a result. There are 4 different components in a function including name, arguments, body, and return value.
Querying, filtering, string manipulation, and data visualization are other important aspects of learning how to use R effectively. Together, they allow users to manipulate these large data sets and derive valuable information which can be used in real-world scenarios.
This depends on the field in which R being used, with many industries and sectors now making use of big data to derive competitive advantages in their respective fields.
Projects such as Kaggle host a large variety of datasets, that can be downloaded and used in R for free. Datasets can vary a lot in their nature, from stars data collected by Nasa to news headlines, to dog breed pictures. The datasets can make R programming very exciting indeed with such variety available that everyone can find a dataset that deals with a favorite subject.
As the world continues to collect more and more data, data science will continue to become a highly desirable skill. Whilst there are a number of ways to do data science, R is one of the top contenders, making it a valuable skill to learn and have,
The value of learning R is compounded by the fact that the world is moving towards automation and one of the things that enable this is exactly the large amounts of data we are able to capture. R offers a window into the future with understanding and predicting data falling well within its remits.
The available functions and features make R relatively easy to learn and use and with the number of datasets available for free, there are endless projects you can embark on, all whilst learning a skill that might very well present you with new opportunities in the near future.
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