R Tutorials

R Tutorial for Beginners

This R Tutorial is a beginner-friendly guide to R programming for data analysis, statistics, charts, and file handling. It starts with R installation and script basics, then moves through variables, data types, operators, conditions, loops, functions, strings, vectors, lists, matrices, data frames, data import, plots, and basic statistical functions.

Use this page as an ordered R learning path. New learners can follow the sections from top to bottom, while users who already know programming basics can jump directly to a specific R topic from the tutorial index.

What is R programming language?

R is an open-source programming language and software environment used for statistical computing, data analysis, and graphics. It is used to store data, clean data, run calculations, summarize results, and draw visualizations such as plots and charts.

An R program can be written as commands in the console or saved as an R script file. For practical learning, it is better to write small script files, run them, inspect the output, and then extend the examples step by step.

Features of R Programming for Data Analysis

• R is open source and free to use.
• R has built-in support for statistics, vectors, matrices, data frames, and plotting.
• R can be used from the command line, from R script files, or through IDEs such as RStudio.
• R can read and write common data formats such as CSV and Excel files with the right functions or packages.
• R supports packages, so you can extend it for data cleaning, visualization, reporting, machine learning, and domain-specific analysis.
• R makes it easier to keep analysis code, results, tables, and plots in a reproducible workflow.

First R Program Example

The following simple R program creates a numeric vector, calculates the mean value, and finds the highest value.

marks <- c(72, 85, 90, 66, 78); mean(marks); max(marks)

Output:

[1] 78.2 [1] 90

Recommended R Learning Order

R Tutorial Index

In our R Tutorial, we shall take you through the following topics.

Get Started with R Programming

The following tutorials help you set up R on your computer and understand the basic structure of an R script file.

• R Installation on Windows
• R Script File Basic Syntax

R Variables and Datatypes

Variables store values during program execution. Data types describe the kind of value stored in a variable, such as numeric, integer, logical, character, or missing value.

• R Data Types
• R Variables
• R NA – Missing Value
• R typeof() – Get type of Object
• R – Create Integer

R Operators for Calculations and Conditions

R operators are used for arithmetic calculations, comparisons, logical expressions, assignment, and other operations. Operators are required before learning conditions, loops, and data filtering.

• R Operators

R Decision Making with if, if-else, else-if, and switch

Decision making statements execute a block of code only when a condition is satisfied. In R programming, these statements are used to validate input, handle special cases, and choose different calculations based on data values.

• R Decision Making Statements
• R if
• R if-else
• R else-if
• R switch

R if statement with logical operators in condition

• R If Statement with AND Operator
• R If Statement with OR Operator

R Looping with repeat, while, and for

Looping statements execute a block of code repeatedly. In R, loops are useful when you need to process values one by one, repeat a calculation until a condition changes, or inspect items in a vector, list, matrix, or data frame.

• R Loops
• R repeat loop
• R while loop
• R for loop
• R break statement

R Functions for Reusable Code

A function is a set of statements that can take arguments, perform a task, and return a result. R has many built-in functions for statistics, mathematics, strings, dates, and plots. You can also create your own functions to avoid repeating the same code.

• R Functions

R Strings and Text Operations

A string is a sequence of characters. In R, strings are used for names, labels, categories, file paths, messages, and text data.

• R Strings
• R – Concatenate Strings
• R – String Length
• R – Substring
• R – Split String

R Data Frames for Tabular Data

R data frames store tabular data in rows and columns. A data frame can contain columns of different types, such as numbers, strings, logical values, and dates. For many beginner data analysis tasks in R, data frames are the main structure used after importing data.

students <- data.frame(name = c('Asha', 'Ravi', 'Meera'), marks = c(82, 76, 91), passed = c(TRUE, TRUE, TRUE)); students; summary(students$marks)

• R Data Frame
• R – Access Element at (i,j) in Data Frame
• R – Create Empty Data Frame
• R – Create Data Frame
• R – Add Column to Data Frame
• R – Reset Row Numbers of Data Frame
• R – Replace NA with 0 in Data Frame
• R – Rename Column(s) of Data Frame
• R – Delete Duplicate Rows in Data Frame
• R – Remove Row(s) in Data Frame
• R – Remove NA Rows in DataFrame
• R – Delete Column(s) in Data Frame
• R – Sort Data Frame by Column
• R – Apply Function For Each Row in Data Frame
• R – Filter Data Frame with NA rows
• R – Convert List to Data Frame
• R – Convert Data Frame to Matrix
• R – Convert Matrix to Data Frame
• R – Combine Data Frames
• R – Compare Data Frames
• R – Import Excel to Data Frame
• R – Structure of Data Frame
• R – Summary of Data Frame

R Type Conversion Between Common Data Types

R allows us to convert a value from one datatype to another. Type conversion is common when imported data stores numbers as strings or when a calculation needs a specific data type.

• R – Convert String to Integer

R Vectors for One-Dimensional Data

An R vector is a sequence of elements of the same datatype. Vectors are fundamental in R because many functions and operators work on all elements of a vector at once.

• R Vectors
• R – Create Vector
• R – Get Vector Length
• R – Access Items in Vector
• R – Iterate over Items of Vector
• R – Sort Vectors
• R – Reverse Vector
• R – Check if Specific Item is present in Vector
• R – Logical Vectors
• R – Integer Vectors
• R – Double Vectors
• R – Character Vectors
• R – Get Type of Vector
• R – Check if type of Vector is Logical
• R – Check if type of Vector is Integer
• R – Check if type of Vector is Double
• R – Check if type of Vector is Character
• R – Convert Logical Vector into Integer Vector
• R – Convert Character Vector into Integer Vector
• R – Vector Recycling

R Lists for Mixed Data

An R list is a sequence of elements where the elements can be of different datatypes. Lists are useful when you need to group related values such as vectors, strings, numbers, data frames, or model results into one object.

• R – Lists
• R – Create Empty List
• R – Create List
• R – Get List Length
• R – Append Item to List
• R – Loop through Items in List
• R – Check if Specific Item is present in List
• R – Reverse List
• R – Join Lists

R Matrix Operations

An R matrix stores data in rows and columns where all elements have the same datatype. Matrices are commonly used for numerical calculations, linear algebra, and structured two-dimensional data.

• R Matrix
• R – Create Matrix
• R – Check if R Object is a Matrix
• R – Get Element at Given Row, Column of Matrix
• R – Get Specific Row of Matrix
• R – Get Specific Column of Matrix
• R – Get Multiple Rows of Matrix
• R – Get Multiple Columns of Matrix
• R – Matrix Multiplication
• R – Transpose Matrix
• R – Inverse Matrix
• R – Correlation Matrix

R Date and Time Values

Date and time values are used when data contains timestamps, calendar dates, transaction dates, log times, or time-based measurements.

• R Date and Time
• R – Current Date
• R Matrix
• R – Current Time

Data Import and Export in R from CSV and Excel Sources

The following tutorials deal with reading different file types and loading data from those files into R data objects, and how to write data from R objects to files.

sales <- read.csv('sales.csv'); head(sales); write.csv(sales, 'sales_cleaned.csv', row.names = FALSE)

• R CSV Files
• R Read Excel / XLS / XLSX Files
• R – Import Excel Data into Dataframe

Charts, Plots, and Graphs in R

R can create charts and graphs directly from vectors, matrices, and data frames. Start with base plotting functions such as plot() and barplot() before moving to advanced visualization packages.

• R Pie Charts
• R Bar Plots
• R Line Graphs

R plot() Function Tutorials

• R – Draw Plot from two Vectors X, Y
• R – Draw Line Plot
• R – Change Color of Plot
• R – Set Plot Line Width or Thickness
• R – Set Plot Title
• R – Set X, Y axes Labels

R barplot() Function Tutorials

• R Bar Plot – From Matrix
• R Bar Plot – Set Main Title
• R Bar Plot – Set X, Y axes Labels
• R Bar Plot – Set Width for Bars
• R Bar Plot – Set Space between Bars
• R Bar Plot – Set Color for Bars
• R Bar Plot – Set Names for Bars
• R Bar Plot – Draw without Axes
• R Bar Plot – Set Border Color for Bars
• R Bar Plot – With Horizontal Bars

Statistical Analysis in R

R includes functions for descriptive statistics and statistical analysis. Beginners can start with values such as mean and median from vectors and then apply the same idea to columns in a data frame.

• R Mean of a Vector
• R Median of a Vector

R Mathematical Functions

R has built-in mathematical functions for common tasks such as finding absolute value, maximum value, minimum value, square root, and more. These functions work with individual values and, in many cases, with vectors.

• R abs() – Absolute Value
• R max() – Maximum Value
• R min() – Minimum Value
• R sqrt() – Square Root

R Packages and Help Commands for Beginners

R can be extended with packages. A package is a collection of functions, data, documentation, and examples. Beginners should first learn base R syntax and then install packages only when a task needs them.

install.packages('readxl'); library(readxl); ?read_excel

Common R Programming Mistakes to Avoid

• Forgetting that R indexes from 1: The first item in a vector is at position 1, not 0.
• Mixing data types in a vector: A vector stores values of the same basic type. If numbers and strings are mixed, R may coerce the values to one type.
• Ignoring NA values: Many calculations return NA when missing values are present unless they are handled properly.
• Confusing a matrix with a data frame: A matrix holds one data type, while a data frame can hold different column types.
• Skipping script files: Commands typed only in the console are harder to reuse and debug than code saved in an R script file.

QA Checklist for Reviewing This R Tutorial Page

• Confirm that every new R code block uses valid R syntax and the PrismJS class language-r.
• Confirm that output-only blocks use the output class.
• Check that beginner topics are ordered from setup and syntax to data structures, file import, plots, and statistics.
• Verify that vectors, lists, matrices, and data frames are described accurately.
• Make sure the existing R tutorial image and existing tutorial URLs remain present in the content.
• Check that FAQ answers are specific to learning R programming.

R Tutorial FAQs for Beginners

Official R Documentation and Beginner Learning References

You may refer to the following references for R documentation and additional beginner learning material.

• https://www.rdocumentation.org/
• The R Project for Statistical Computing
• CRAN R Manuals

What You Can Do After Completing This R Tutorial

After working through this R tutorial index, you should be able to write basic R scripts, use R data types and operators, control program flow, create functions, work with vectors, lists, matrices and data frames, import CSV and Excel data, create simple plots, and calculate common statistical values.