
Install the R language and RStudio, then open RStudio to start studying. Understand that R is a programming language and a statistical environment, while RStudio is an integrated development environment.
Explore R syntax basics: scripts, comments, packages, operators, and objects; learn to load packages with library, install.packages, assign objects, and use functions with parameters and arguments.
Install packages to extend R’s built-in functions, load them with library, and selectively call package functions with the package::function syntax, such as Readexcel from the Readexcel package.
Master bracket indexing and the dollar sign to select rows and columns in a data frame, using numeric positions or variable names, and leaving parts blank to access all.
Explore basic descriptive statistics in R with the built-in mtcars dataset, learning to compute mean, median, min, max, range, and standard deviation, and generate frequency and proportion tables.
Explore logical values and operators in R to filter and analyze data, using mtcars with subset and bracket indexing, and combine conditions with and/or for targeted insights.
Learn to export R Markdown documents to Word using the knit command, switch formats to html or pdf, and control code visibility with chunk options.
Welcome to R Studio Fundamentals!
This course is designed to be a very beginner-friendly course. It is structured to help you take your first steps with R and RStudio in a simple way.
In this course, we’ll walk through the essential tools and concepts you need to start working with data in R. No prior programming experience is required. Everything is explained step by step, with simple examples, using a clear and accessible language. You’ll learn how to install R and RStudio, understand the interface, write your first lines of code, and organize your projects properly from the start.
We’ll cover important topics like creating and managing objects, writing and running scripts, working with datasets, using logical values and operators, importing data from files, and exploring your data with basic descriptive statistics.
Each lesson is short, practical, and focused on helping you understand what’s happening on screen and why it matters. You’ll also get to see how small pieces of code fit together to form a real data analysis workflow.
Whether you’re preparing for a course in statistics or causal inference, starting a research project, or just curious about R, this course will give you a solid foundation to build on.