R Programming 101 Courses

Ggplot2 Data Visualization with Geometries in R

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Master the art of data visualization in R with ggplot2's geometries (geoms) to create compelling scatter plots, line graphs, bar charts, histograms, density plots, and box plots for effective data presentation.

• YouTube
• 14 minutes
• Self-Paced
• Free Video

Today I Learned in R Programming - RDS Files and the Here Package

Discover how to save files as RDS in R, preserving attributes, and explore the here package for efficient file path management in just 10 minutes.

• YouTube
• 10 minutes
• Self-Paced
• Free Video

Importing Data from Excel into R Studio

Discover how to import CSV files and Excel spreadsheets into R using the readxl package, set working directories, and handle missing values in this beginner-friendly guide.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Interactive Tables Using the DT Package in R

Discover how to create interactive tables in R using the DT package, perfect for beginners in data visualization with easily replicable examples.

• YouTube
• 14 minutes
• Self-Paced
• Free Video

Today I Learned in R Programming - Tips and Tricks for Data Science

Discover essential R programming tips and tricks for data science, statistics, and coding in this quick 12-minute tutorial episode.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

Advanced ggplot - Using ggplot2 and Lubridate Together for Data Visualization

Discover how to effectively combine ggplot2 and lubridate packages for time-based data visualization in R, enhancing your ability to create insightful temporal graphics.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

Multiple Regression - Making Sure That Your Assumptions Are Met

Master linear regression diagnostics in R by validating model assumptions and performing multiple regression analysis to ensure statistical accuracy and reliability.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

Adding Variables to Your Multiple Regression Model in R

Master multiple regression techniques by learning to incorporate categorical and numeric variables into statistical models for enhanced data analysis and prediction.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Multiple Regression Analysis Using R Programming - From Beginning to End

Master multiple regression analysis using R programming, from basic concepts to diagnostic tests and assumption validation for accurate predictive modeling.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Data Manipulation and Cleaning in R with dplyr - A Beginner's Guide

Master essential data manipulation techniques using dplyr functions like select, filter, mutate, group_by and summarise to efficiently clean and transform your datasets in R programming.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

Themes in ggplot2 - Making Your Data Visualizations Stand Out

Master the art of customizing ggplot2 themes in R, from applying preset styles to fine-tuning every visual element for creating stunning and professional data visualizations.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Creating Lollipop Plots with R and ggplot2 - Part 3

Master the creation of elegant lollipop plots in R using ggplot2, focusing on data visualization techniques and practical implementation with built-in datasets for hands-on practice.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Handling Missing Data and Missing Values in R Programming - NA Values, Imputation, and naniar Package

Discover effective techniques for handling missing data in R through NA value management, imputation methods, and visualization using the naniar package for accurate data analysis.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

Multiple Regression Analysis - Effect Modifiers and Interactions in R Programming

Master statistical modeling techniques for handling effect modifiers and variable interactions in multiple regression analysis using R programming fundamentals.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

Multiple Regression - Dealing with Outliers and Collinearity in Data Analysis

Master statistical analysis techniques for handling outliers and collinearity in multiple regression, including identification methods and decision-making for model optimization.

• YouTube
• 13 minutes
• Self-Paced
• Free Video