Basics

Learn everything about files in R, including .RData, CSV, Excel, and text files. Discover how to read, write, and restore R objects using load(), save(), read.csv(), and more. Explore best practices for file handling in R and compare different file formats for efficient data management. Perfect for R programmers, data analysts, and researchers working with datasets in R.

What is a File in the R Language?

In R, a file refers to data stored on a computer storage device. The script written in R has an extension *.R that can read into R or write from R. R Files are essential for importing external data, saving results, and sharing work. The R script files contain code that can be executed within the R software environment.

Describe commonly used Files in R

For illustration purposes, I have categorized the commonly used files in R as code files, data files, and specialized data files.

Code Files:

• .R (R script files)
• .Rmd (R Markdown files)

Data Files:

• .csv (Comma Separated Values) – Most common for tabular data
• .txt (Plain text files)
• .xlsx or .xls (Excel files)
• .RData or .rda (R’s native binary format)

Specialized Data Formats:

• .json (for structured data)
• .xml (for hierarchical data)
• .sav (SPSS files)
• .dta (Stata files)

What are the best Practices for using Files in R?

• Use relative paths when possible for portability
• Check file existence before reading
• Close connections (when the database connection is open) after reading/writing certain file types
• Consider using the package here for more reliable file paths

What is .RData Files in R

An .RData (or .rda) file is a binary file format used by R. It is used to save multiple objects (variables, data frames, functions, etc.) in a compressed, space-efficient way. It is R’s native format for storing workspace data.

What are the Key Features of .RData Files?

The key features of .RData files in R are:

1. Stores Multiple Objects
   • The ..RData can save several R objects (e.g., data frames, lists, models) in a single file.
   • Example: save(df, model, list1, file = "mydata.RData")
2. Binary Format (Not Human-Readable)
   • Unlike .csv or .txt, .RData files are not plain text and cannot be opened in a text editor.
3. Compressed by Default
   • Uses compression to reduce file size (especially useful for large datasets).
4. Platform-Independent
   • Can be shared across different operating systems (Windows, macOS, Linux).
5. Preserves Attributes
   • Keeps metadata (e.g., variable labels, factors, custom classes).

Which command is used for restoring an R object from a file?

In R, one can restore the saved objects from a file using the load() function. The load() command loads all objects stored in the file into the current R environment. This command works with .RData or .rda files (these are binary files used by R). This command does not work with .csv, .txt, or xlsx, etc. files.

Explain the use of load() command with example.

The following example first creates objects $x$, $y$, and $z$. These objects will be saved in “my_work.RData” file. These objects will appear in the R workspace after loading.

x <- rnorm(10)
y <- 1:20
z <- "Level of Significance"

save(x, y, z, file = "my_work.RData")
load("my_work.RData")

How many ways are there to read and write files in R?

There are dozens of ways to read and write files in R. The best approach depends on the file type and size. Depending on the file format and the packages used, the following is a categorized breakdown of the most common methods:

Base R Functions

• Reading Files
  • read.table(): Generic function to read tabular data (e.g., .txt).
  • read.csv(): For comma-separated values (CSV) files.
  • read.delim(): For tab-delimited files (.tsv or .txt).
  • scan(): Low-level function to read raw data.
  • load(): Restores R objects from .RData or .rda files.
  • readRDS(): Reads a single R object from .rds files.
• Writing Files
  • write.table(): Writes data frames to text files.
  • write.csv(): Writes to CSV files.
  • write.delim(): Writes tab-delimited files.
  • save(): Saves multiple R objects to .RData or .rda.
  • saveRDS(): Saves a single R object to .rds.

Using Packages

• Reading Files

• Writing Files

Specialized Methods

For Large Datasets

• vroom (from the vroom package) – High-speed reading of large CSV/TSV files.
• arrow (Apache Arrow) – Efficient for big data (supports Parquet, Feather formats).

For Databases

• DBI + RSQLite/RMySQL/odbc: Read/write from SQL databases.

For Binary & Custom Formats

• feather: Fast binary storage (works well with Python).
• qs: A faster alternative to saveRDS() for large objects.

Statistics and Data Analysis

Functions in R programming are reusable blocks of code that perform specific tasks, improving efficiency and readability. This guide covers how to write functions in R, their key features (lexical scoping, closures, generics), and practical examples for data science & automation. It is perfect for beginners and advanced users!

What are Functions in R Language?

A function is a chunk of code written to carry out a specified task. It can or cannot accept arguments (also called parameters), and it can or cannot return one or more values. In R, functions are objects in their own right. Hence, we can work with them the same way we work with any other type of object.

Objects in the function are local to the function. One can return the object as any data type.

What is Function Definition?

An R function is created using the keyword function. The basic syntax of an R function definition is as follows –

Function_name <- function(arg_1, arg_2, …) {
    Function body 
}

What are the Components of R functions?

The different components of a function are:

• Function Name: Function Name is the actual name of the function because it is stored in the R environment as an object with this name.
• Arguments: An argument is a placeholder. When a function is invoked, we pass a value to the Argument. Arguments are optional; that is, a function may contain no arguments. Arguments can also have default values.
• Functions Body: In a function body, statements can be collected. It defines what the function does.
• Return Value: The return value of a function is the last expression in the function body to check.

What are the Key Features of R Functions?

The following are key features of R functions:

• Generic Functions: Work differently based on input class (e.g., print(), plot()).
• First-class Objects: First-class Objects can be assigned, passed as arguments, and returned.
• Lexical Scoping: Variables are looked up where the function is defined.
• Flexible Arguments: Default values, optional args, and ... (variable-length args).
• Closures: Can remember their environment (useful in functional programming).

What are Generic Functions in R?

Generic Functions in R behave differently based on the class of their input arguments. They use method dispatch to call the appropriate version (method) of the function for a specific object type. The generic function allows one function name to work for different object types (e.g., print(), plot(), and summary()).

What is the Attribute Function in R?

To get or set a single attribute, you can use the attr() function. This function takes two important arguments. The first argument is the object we want to examine, and the second argument is the name of the attribute we want to see or change. If the attribute we ask for does not exist, R simply returns NULL.

What is an arbitrary function in R?

Arbitrary function means any function. Generally, an arbitrary function refers to a function that belongs to the same class of functions we are discussing (its freedom is limited). For example, when talking about continuous real-valued functions defined on the bounded closed interval of the real line, an arbitrary function may refer to a function of the same type.

What are the Types of Functions in R?

In R, the following are types of functions:

• Built-in Functions: R has many built-in functions such as sum(), mean(), and plot().

numbers <- c(2, 4, 6, 8)
mean(numbers)  

## Output: 5

• User-defined Functions: Custom functions created by users, for example,

# Define a function to add two numbers
add_numbers <- function(a, b) {
  return(a + b)
}

# Call the function
add_numbers(5, 3)  

## Output: 8

• Generic Functions (Polymorphic Behavior): Generic functions behave differently based on input class. For example, print() behaves differently for numbers and lm models.

• Recursive Functions: Recursive functions call themselves (useful for iterative algorithms).

# Recursive factorial function
factorial <- function(n) {
  if (n == 0) return(1)
  else return(n * factorial(n - 1))
}

factorial(5)  

## Output: 120

What are the Best Practices for Writing Functions in R?

The following are considered best practices when writing functions in R Programming Language.

✅ Use Descriptive Names (e.g., calculate_mean() instead of f1()).
✅ Keep Functions Short & Focused (Single Responsibility Principle).
✅ Add Comments for clarity.
✅ Use Default Arguments for flexibility.
✅ Test Functions with different inputs.

Functions in R make your code modular, reusable, and efficient. Whether you’re performing data analysis, building models, or creating visualizations, mastering functions will significantly improve your R programming skills.

Machine Learning Quiz

The post is about Interview Questions about R Language. It contains some basic questions that are usually asked in interviews.

What is R?

R is a programming language and environment for statistical computing and graphics. It is an open-source language that provides a wide variety of statistical and graphical techniques and is highly extensible. The strength of R is the ease with which well-designed publication-quality plots can be produced, including mathematical/statistical symbols and formulae where needed.

R language is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows, and Mac OS. The R command line interface (CLI) consists of a prompt, usually the > character. Data miners use it for developing statistical software and data analysis.

What is CLI in R?

CLI stands for Command Line Interface. In a command line interface, the user types the command that they want to execute and presses the Return key. For example, if you type the line 2+2 and press the return key, R will give you the result [1] 4.

What is GUI in R?

GUI stands for Graphical User Interface. R Language is a command line-driven program. The user enters instructions at the command prompt ( > by default ) and each command is executed one at a time. There have been a number of attempts to create a more graphical interface, ranging from code editors that interact with R, to full-blown GUIs that present the user with menus and dialog boxes.

Who is the Creator of R Language?

R language was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.

What are the Applications of the R Language?

• Many data analysts and researchers use R because R language is the most prevalent language. Hence, R is used as a fundamental tool for data analysis in various disciplines such as mathematics, economics, social sciences, natural sciences, technology and engineering, business and finance, etc.
• Many quantitative analysts use R as their programming tool. R helps in data importing and cleaning, depending on what manner of strategy the researchers are using.
• R is best for data Science because it gives a broad variety of statistics and data manipulation tools. In addition, R provides the environment for statistical computing and design. Rather R is considered as an alternate execution of S.

Why R is Important?

R language is a programming language and a leading tool for machine learning, artificial intelligence, data mining, natural language processing, statistics, and data analysis. By using R one can create objects, functions, and packages. R language is a platform independent, so one can use it on any operating system. The downloading and installation of R language is free, therefore, one can use it without purchasing a license.

R is open-source which means anyone can examine the source code to see what exactly is doing on screen. Anyone can add a feature and fix bugs without waiting for the vendor to do this. It also allows the user to integrate with other languages (such as C and C++). It also enables the user to interact with many data sources and statistical packages (such as SAS and SPSS). R has a large growing community of users working day by day to enhance its working and powers.

General Knowledge Quiz, MCQS Statistics with Answers