R is not only a program for data analysis. It has unique features that make it very powerful, probably the most important being the notion of vectors. These vectors make it possible to perform sometimes complex operations on a set of values in a single command. Performing multiple calculations with vectors R is a vector language.
Vector is like a row or column of numbers or text. For example, the list of numbers {1,2,3,4,5} could be a vector. Unlike most other programming languages, R lets you apply functions to the entire vector in a single operation without the need for an explicit loop.
First, assign the values 1:5 to a vector called x: > x x [1] 1 2 3 4 5 Next, add the value 2 to each element of the vector x; x + 2 [1] 3 45 6 7 16
You can also add one vector to To add element-level 6:10 values to x, do the following: andgt; x + 6: 10 [1] 7 9 1113 15 To do this, in most other programming languages you would need an explicit loop to execute each value of x. However, R is designed to do a lot of things in one step. This feature is one of the features that make R so useful and powerful for data analysis.
Dealing with more than just statistics R was developed by statisticians to simplify the analysis of statistical data. This legacy lives on, making R a very powerful tool for performing virtually any statistical calculation.
When R started to grow from its origins in statistics, many people who would describe themselves as programmers rather than statisticians have involved with R. As a result, R is now perfectly suited for a wide variety of non-statistical tasks, including data processing, graphing, and analysis of all kinds.
R is used in finance, natural language processing, genetics, biology, and market research, to name a few.(However, not all activities are easy to program in R.)
Running code without a compiler R is an interpreted language, which means that unlike compiled languages like Cand Java: you do not need a compiler to create a program from the code before you can use it. R directly interprets the provided code and converts it into lower-level calls to precompiled code/functions. Basically, this just means writing the code and submitting it to R, and the code is executed, simplifying the development cycle.
The downside of an interpreted language is that the code generally runs slower than the equivalent compiled code. If you have experience in other languages, please note that R is not C or Java. Although it is possible to use R as a procedural language like C or as an object-oriented language like Java, R relies primarily on the functional programming paradigm.
Forget what you know about other languages and get ready for something completely different.
Erzsebet Frey (Eli Frey) is an ecologist and online entrepreneur with a Master of Science in Ecology from the University of Belgrade. Originally from Serbia, she has lived in Sri Lanka since 2017. Eli has worked internationally in countries like Oman, Brazil, Germany, and Sri Lanka. In 2018, she expanded into SEO and blogging, completing courses from UC Davis and Edinburgh. Eli has founded multiple websites focused on biology, ecology, environmental science, sustainable and simple living, and outdoor activities. She enjoys creating nature and simple living videos on YouTube and participates in speleology, diving, and hiking.