What is R Programming?
R programming is the language and environment for statistical computing, graphics, representation, and reporting initiated by the R Foundation for Statistical Computing. It includes time series, linear regression, machine learning algorithms, and statistical inference. Software programmers, data miners, and statisticians who are working on developing statistical software use R programming. It is used by large companies like- Uber, Facebook, Airbnb, and Google.
It is an open-source program available under the General Public License, and pre-compiled binary versions are provided by Linux, Windows, and Mac. Data analysis with R is done in a sequence of steps programming, modifying, discovering, modeling, and delivering the results. It is a popularly used artificial intelligence technique, and if one is keen on learning R programming, one can enroll in an artificial intelligence course or an AI specialist training course.
Source: Ed X
Data Types In R programming-
Data structures need to be understood and analyzed because there are objects that one will need to work with on an everyday basis in R programming. Object conversions especially can be challenging for beginners. Everything in R is considered an object. There are five basic data types, including character, numerical, integer, logical, and complex. Additionally, raw is another data type that is not used as frequently. These data types are consolidated to develop data structures identified as atomic vectors.
Character – In R programming, the text is represented as a sequence of letters, numbers, and symbols. The text sequences are known as a string. These elements are represented as character vectors—the data type R stores sequences of characters. Character strings within R programming can be represented with the help of single quotes or double quotes. Handling, cleaning, and processing character strings is an important requirement for those pursuing data analysis.
Complex Vectors – R programming sustains complex data types. These data types store numbers with imaginary components. All R objects have a class attribute. Those using R programming can understand how to deal with objects based on their class.
Logical– A logical vector only contains true or false values. In R, true values are assigned with TRUE, and false values with FALSE. A logical vector can be systematized with brackets. It functions as a filter for the vector it is indexed on. It only lets those contents of the vector pass through for which the logical vector is TRUE.
Numeric- Decimal values are referred to as numerics in R. It is the default data representation for numbers in R. If you specify a decimal value to a variable x as follows, x will be numeric. Even if an integer is assigned to a variable y, it is still being saved numerically.
Frequently Asked Questions (FAQs)-
1.Why is R programming a good fit for AI?
R programming is a widely-used programming language when it comes to AI. It is great when it comes to reading and analyzing large databases and crunching huge numbers. With the help of R, one can work on various programming subsets like object-oriented programming, vectorial computation, and functional programming.
How can one learn R programming?
R programming is becoming increasingly famous for choosing a programming language for data science and data analysis. Before beginning, watch tutorials, videos, and read free content available online to grasp what R programming entails. Next, think through why you want to learn R programming. Understand what type of data you aim to work with, what projects you want to undertake, and define your end goal. Next, pick a specific area of interest- data science, statistics, data analysis, predictive modeling, and more. There are a lot of options to choose from. After that, one will need to get into the real stuff, like learning syntax and goal-oriented coding. For this, one must enroll in an AI course and take professional help. This isn’t something that can be learned individually.
3.How long does it take to learn R Programming?
R programming keeps evolving, and even accomplished data scientists are never done with learning. It is like learning a language. There are always more words to learn, vocabulary to brush up on, and scope for improvement. Similarly, you can’t “complete” learning R Programming, but learning the simple and functional basics can be accomplished quickly.
Why should one learn R Programming?
R programming is one of the top choices when it comes to programming languages used by professionals. It is flexible and can be used in an array of fields, including academia, business, and finance. Even if you don’t want to take up a full-time job as a data scientist or a data analyst, you can learn R programming to become more familiar with coding, spreadsheets, and programming. According to payscale, even as a fresher in the field, one can make up to five lakh per annum in R programming.