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.

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.

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 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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.