How R Programming works
R lets you interact and compute statistically and analytically as an interpreted language. R interprets and executes commands without needing a compilation phase, unlike C language or Java. Typing instructions, seeing findings instantaneously, and adapting analysis based on output makes it a great data exploration and development tool. R is an interpreted language that provides simple programming features including branching (conditionals), looping, and modular programming with functions.
The R User Interface and Execution Flow
Most R users use a terminal window for command-line interaction. Workflow is the same whether using the default R GUI, a terminal, or RStudio. The R interpreter interprets a command you input at the R prompt (typically >) and click Enter. As the computer performs the command, the console displays the results and a new prompt for the next instruction.
This interactive loop underpins R. R shows a continuation prompt (+) and waits for the user to finish a command that lacks a closing parenthesis or quote. The user can try again if the command is unrecognizable or contains an error with R’s error message and new prompt.
A plain text R script file with a.R extension is used to write and save complex commands. It’s recommended because it makes code easier to update and debug, makes it reproducible, and makes sharing easier. You can execute code directly from the script line-by-line or by running the full file using a command in an IDE like RStudio.
Core Components: Objects, Functions, and Operators
R’s perceived nature depends on a few crucial components:
Objects: Things are objects in R, including variables, data sets, functions, and analysis outcomes. An object is a name for computer memory storage and retrieval. An assignment operator, typically <-, creates objects. Variables in R are dynamically typed. Instead, reassigning a different R object changes a variable’s data type. Workspace is all session-created items.
Functions: R “verbs” are functions, which include code to do tasks and manipulate data. R provides several built-in functions, like mean() and sample(). The function constructor in R lets you design your own functions, making programming modular and reusable.
Operators: R’s operators include arithmetic (+, *), comparison (>, ==), and logical (&,!) operations. Crucially, these operators are functions. Two + three is syntactic sugar for “+”(2, 3).
The Interpretation Process
R’s interpreter starts a process to execute your command. This is fundamental to R’s vectorized and functional design.
Vectorizing and Element-Wide Execution: Vectorization distinguishes Rs. Vectors can be directly affected by many functions and operators without loops. R adds each element of x <- c(1, 2, 3), returning c(11, 12, 13) when running x + 10. Elemental execution is key to R’s data manipulation efficiency. Vector recycling lets R operate on unequal-length vectors. Elements of the shorter vector are “recycled,” until it matches the longer one. R warns if the longer vector is not multiple of the shorter one.
Environments and Lexical Scoping: Data, variables, and functions are managed by R environments. The environment stores names and items. Like computer file systems, these environments have a “parent” environment. Your primary workspace is the global environment while you operate interactively. A new item is stored there. Lexical scoping rules guide R’s object-finding. First, it searches the active environment for the object. If the object isn’t found, R examines the parent environment. The search continues up the chain of parent environments until the item is located or the empty environment, which has no parent. Search path is R’s sorted list of environments.
Function Evaluation and Runtime Environments: Environment is key to understanding how R interprets and executes functions. R generates a temporary runtime environment to execute function code when invoked. After the function finishes, this runtime environment is deleted.
Here’s how the process works:
- Empty runtime environment built.
- This new environment’s parent is the origin environment, where the function was defined, not called. It’s lexical scoping.
- This runtime environment copies function arguments as local variables.
- In this runtime environment, function body code is executed. Any new objects created by the function are saved here, preventing “side effects” by changing global objects.
- First, the function searches its runtime environment for an object. If the object is not found, R’s scoping rules search the parent (origin) environment, and so on.
- After the function returns, the temporary runtime environment is discarded.
R uses lazy evaluation to evaluate function arguments only when they are needed in the function body. Overcoming needless computations can boost efficiency.
Extensibility Through Packages
R programming is powerful and popular in statistical computing and data science due to its package system flexibility. R’s core is purposely lean, providing only fundamental features, but a robust ecosystem of user-contributed packages greatly expands its capabilities. A big community of developers, professors, and statisticians creates packages, which are preassembled functions, data sets, and documentation to accomplish a certain problem. Many thousands of these packages are free like the Comprehensive R Archive Network.
A package requires a two-step process to use. The install.packages() command downloads the package from a repository and installs it once. Second, each R session must load the package using library() after installation. The package’s functions, objects, and help files are immediately available in the workspace after this activity. This approach speeds up the main R installation and gives users access to a “huge wealth of existing libraries” that save programming time. Many of R’s most valuable utilities, such as the qplot function for rapid plots, are in these add-on packages, giving users access to cutting-edge tools for many jobs.
R programming is versatile and strong because it’s interpreted. Interactively executes user requests through its interpreter. A robust object-oriented architecture where everything is an object and a hierarchical environment system with lexical scoping rules underpin this approach. Contained and predictable code execution is achieved by creating temporary function call runtime contexts. Combined with its vectorized operations and user-contributed packages, R’s interpreted design is ideal for iterative and exploratory data science.