Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
R Studio Fundamentals
Highest Rated
Rating: 4.7 out of 5(13 ratings)
45 students

R Studio Fundamentals

A beginner-friendly guide to using R and RStudio for data analysis, scripting, and project organization.
Created byGustavo Rosa
Last updated 4/2025
English
English [Auto],

What you'll learn

  • Use R and RStudio confidently for data analysis
  • Understand and apply basic R programming concepts
  • Use logical conditions and filtering techniques
  • Basic descriptive statistics

Course content

2 sections • 14 lectures • 1h 36m total length

  • Downloading and Installing R and R Studio1:45

    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.

  • Introduction to the Interface5:18

    Explore the four-quadrant RStudio interface, featuring the source, environment, console, and files panels. Resize quadrants, customize layouts, and use Ctrl-Shift plus quadrant numbers to maximize panels.

  • R Syntax: Key Elements to Know5:28

    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.

  • Console and Source Panel4:36

    Explore the console versus the source window in RStudio, where the console runs code immediately and the source window saves and runs scripts with Ctrl+Enter.

  • Creating and Deleting Objects6:43

    Create and delete objects in R using the assignment operator, build vectors and dataframes, and manage memory with rm and the environments tab.

  • Packages and Libraries2:38

    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.

  • Loading Datasets7:11

    Learn to load datasets in RStudio using CSV, Excel, and Stata files; assign data to a data frame, manage the working directory, and load required packages with library.

  • R Projects6:53

    Discover how to use an R project in RStudio to organize files, set a working directory, and rely on relative paths for portable, reproducible data analyses.

  • Selecting Data with Brackets and the Dollar Sign4:55

    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.

  • Basic Descriptive Statistics5:13

    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.

  • Logic Operators and Subsets7:47

    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.

  • Basic Plotting6:46

    Create a basic scatterplot of horsepower vs. mpg in the mtcars dataset using base R. Save as image or pdf and tailor titles, axes, and color with plot parameters.

Requirements

  • It is not necessary to have basic coding skills. We will start from scratch.

Description

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.

Who this course is for:

  • Students in social sciences, economics, or related fields who want to start working with data in R.

Report abuse