R programming language 101: introductions and definitions

R-programming-language

Many programmers suggest that while they have developed software professionally in a plethora of programming languages, but the hardest language they have come across was R. While this statement may be debatable dividing the software developers’ community in the middle, as many others say that language is fairly easy to cope with. While the language may seem somewhat unconventional to learn initially, it is due to these factors that one with experience in languages like Java, Perl and C++ etc find it easier to handle. It has been developed keeping their abilities in mind.

What truly makes R programming stand-out from every other language is the fact that it is not just a programming language but also an environment for carrying out statistical analysis. Many experts suggest that they like to think that R is more of an environment consisting of a programming language component within that it being a programming language.

Most job sites these days are teeming with vacancies for R programmers, so it is highly recommendable to aspiring professionals to board the R train with a well-recognized R programming certification course.

When speaking about R programming it is safe to say, that is more like a scripting language for the R environment on similar lines as VBA is for MS Excel. This way some of the unconventional aspects of R can be explained when viewed in this perspective.

Understanding ‘sequences’ in R programming:

The reason behind using the expression seq(a, b, n) is used is to create a closed interval that starts from ‘a’ ends at ‘b’ and runs with step sizes of ‘n’. Taking a more realistic example, if we implement seq(1, 10, 3) returns with the following vectors – 1, 4, 7 and 10.

This command is somewhat similar to the range(a, b, n) in Python, except in Python only half-open intervals are used so, the vector 10 would not be included which was returned in case of the R example. The default step size augments in case of both R and Python is 1.

Boolean operators used in R:

The Boolean operators used in R are T or True for true values and F or False for false values.

As for the operators & and |, they are applied on the vectors element-wise. Conditional elements use && and || and they use lazy evaluations like in C. in such cases the operators do not use the second augment if the first augment works to determine the return value.

For more information on R programming we suggest that you take up a simple R programming course in Delhi, where the best professional courses are stationed at.

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