R Programming for Complete Beginners (Part 1)

Learn how to download and install the R language on Windows, select default settings in English, install version 4.5.1, and prepare to use RStudio.

Learn to download and install the RStudio open source edition on Windows, install R, choose the 64-bit rendering engine, and launch RStudio for a ready-to-use IDE.

Learn to check and switch the current directory in the studio, open the correct Arabic or English project, and understand how each project keeps its own files, scripts, and data.

Check and switch your R version in RStudio. Verify updates with sessionInfo and decide between 4.0.3 and 4.2.1 for project compatibility.

Explore how R packages deliver reusable code and data, with corpus for text, geospatial tools, and tidyverse for tabular tasks; learn to install, load, and remove them.

Install the R kernel in Jupyter notebooks using conda-forge, then run R code directly in a notebook. Learn to create variables and print results in R.

Create variables in r by naming and assigning age, gender, and university with quotes. Use the type function and capital L to distinguish double, integer, and character.

Explore the core types of operators in R, including arithmetic, relational, logical, and assignment operators, and see how they perform math, comparisons, and variable assignment.

Explore arithmetic, logical, and relational operators in r, practice with true/false vectors, and master assignment using <- or =, including and, or, not, plus comment handling and console output.

Practice basic arithmetic in R by creating variables x and y, assigning 25 and 5 respectively, then apply addition, subtraction, multiplication, and division to compute their results.

Master basic operators in R by solving a beginner exercise: assign x 25 and y 5, then add, subtract, multiply, and divide to get 30, 20, 125, and 5.

Explore loops in R, mastering for and while structures, iteration, and conditions through examples like counting from 1 to 5 and simulating a phonebook task.

Explore loops in R by building for and while loops, iterating over vectors and names, printing each value, and counting characters to reinforce iteration basics.

Create a for loop that prints numbers from 1 to 10, then build a vector of movie names and print each title with a loop as a two-step exercise.

Learn how if and else statements in R use test expressions and boolean logic to run specific code blocks, illustrated by x > 5 and positive or negative numbers.

Practice constructing if and else statements in R by assigning x = 12 and printing messages based on whether x is greater than five or smaller than five.

Use an if statement to determine if the x variable is positive or negative, first with 15 and then with -5, printing the result accordingly.

Learn to write an if statement in R that checks if x is greater than or equal to zero and prints whether it's positive or negative.

Learn how case sensitive rules treat uppercase and lowercase letters as different in R, how variables are named, and why using lowercase letters is best practice.

Explore case sensitivity and how to create variables like salary, income, and expenses, then use ls() to list all current variables in the environment.

Explore the difference between data types and data structures, with examples like integer, double, float, string, and logical in R, and learn primitive versus non primitive containers.

Create variables, list them with ls, and delete specific variables with rm, or clear the workspace with the broom to manage your R environment.

Explore operators in r: the colon to generate a sequence, the end operator to test membership, and the star operator with percent signs to multiply a matrix by its transpose.

Learn miscellaneous operators in R, including the colon operator for sequences, the RN operator for membership checks, and the transpose operator for matrices, culminating in matrix multiplication with %*%.

Practice miscellaneous operators in R by using the colon and in operators to build a 1 to 20 series and test membership, then perform transpose multiplication on a 5-column matrix.

Explore data structures in R and differentiate homogeneous and heterogeneous types. See how vectors, matrices, and arrays offer homogeneous storage while lists and data frames support multiple data types.

Learn how vectors in R are homogeneous one-dimensional data structures that store a single type, create a vector with c(20, 30, 60, 80, 120), and note indexing starts at one.

Create three vectors in R named laptops, ratings, and price, then print each vector's type and its element types. Finally convert the price vector to integer and check the solution.

Explore how lists in R differ from vectors by holding multiple data types and even nested lists, using the list function to build complex data structures.

Explore the difference between vectors and lists in R. Build a character vector using c and a labeled list, then inspect class and structure.

Learn to create and nest lists in R by building an Avengers list, adding a cast names sublist with names and genders, and inspecting the list structure.

Explore how functions in R take inputs, perform operations, and return results, including built-in functions like print, mean, and length, and how to create user defined ones.

define and test simple R functions: an addition function with three arguments that returns the sum, and a mul function that multiplies two arguments, returning results.

Explore how matrices in R store data as a two-dimensional four by three grid, with one-based indexing, and how to create them with matrix, c, nrow, ncol, and by row.

Create a 2x3 matrix with values 1 to 6 and store in mat one, then build a 4x4 matrix with 1 to 16 arranged by row in mat two.

Learn to create matrices in R using matrix with a vector, nrow, and ncol; compare c(...) with direct values, and order by row with byrow, then print.

Explore arrays in R, see how they differ from vectors and matrices as homogeneous, multi-dimensional storage, and learn to create them with the array function.

Create and print multi-dimensional arrays in R, building a 2x3x3 array from 1 to 18 across three matrices, and reinforce concepts with a 2x3x2 array from 1 to 12.

Demonstrate creating arrays and matrices in R using the assignment operator, combining values with c, and specifying dimensions for multi-dimensional arrays (2x2 with 3 matrices; 3x3 with 4 matrices).

Learn what a data frame is in R, a two-dimensional heterogeneous data structure for tabular data, and how to create it with data.frame using vectors for id, name, and age.

Create and print a data frame in R by combining vectors into student_id, student_name, and age columns, and learn to import Excel or CSV data.

Create vectors for movie_id, movie_name, and movie_rating, then assemble them into a data frame in R and print the dataset to view the results.

Learn to create factors in R for nominal data, explore automatic alphabetical levels, and enforce a custom order with ordered = TRUE and explicit levels.

Create a factor from the data bachelors, masters, doctorate, store it in a degree variable, and ensure it is ordered as written.

Explore how a Jupyter notebook functions as an open-source, browser-based tool that combines live code, equations, text, and visualizations for beginner-friendly scientific computing.

Install the Anaconda distribution, including Python 3.9 and data science packages like pandas, numpy, matplotlib, and scikit-learn, with IDEs such as Jupyter Notebook and Jupyter Lab.

Learn to open a Jupyter notebook via Anaconda prompt or PowerShell, set the working directory, and launch notebooks in a browser, then check Python version and libraries.

Switch between edit mode and command mode in a Jupyter notebook by using the green pencil for editing and the blue mode for commands, including moving, deleting, and running cells.

Master variable autocomplete and the order of operations in a Jupyter notebook, learning how to run cells, print variables, and track execution with cell numbers.

Learn what markdown is—a markup language used by developers and data scientists and data analysts, similar to HTML, and essential for organizing and commenting within Jupyter notebooks.

Learn to write markdown blockquotes in Jupyter Notebook using the greater-than sign and indent quotes. Render with shift-enter to capture insights and quotes within cells.

Discover how to insert line breaks in markdown between sentences, using markdown syntax and the br tag, and see how shift enter impacts line separation in Jupyter Notebook.

Learn how to create an ordered list in markdown, using numbered items to present programming languages like Python, R, and Julia.

Learn to create unordered lists in Markdown using dash or star bullets, and compare them with ordered lists to organize content in a Jupyter notebook.

Learn to add an external link in a Jupyter notebook using markdown, including the anchor, href, and clickable url, and verify it opens the site.

Learn two ways to insert images in a Jupyter Notebook: use the insert image option for original size, or use markdown with img src to set width and height.

Learn to embed a video in a Jupyter notebook with markdown, using a video tag, source, width, height, and controls, for the Test Video.mp4 file.