Foundations of R Programming and Basic Data Manipulation

Overview

Class Central Tips

Embark on a transformative journey into R programming and data manipulation with this comprehensive course. It starts with an in-depth overview of R and RStudio, covering installation, configuration, and key features. You'll master navigating RStudio, managing projects, and handling diverse file formats for efficient workflows. The course delves into Rmarkdown for dynamic documentation, blending code, narrative, and visualizations. You'll explore essential data types and structures through hands-on labs, including matrices, arrays, lists, data frames, strings, and DateTime objects. The R programming section covers operators, loops, and functions, enabling you to write clean, modular code. Advanced topics include data import/export, web scraping, and sophisticated data manipulation techniques using piping, filtering, aggregation, reshaping, and joining datasets. You'll create impactful visualizations with ggplot2, plotly, leaflet, and dygraphs. Ideal for aspiring data scientists, analysts, and professionals, this course requires a basic programming understanding and targets beginners to intermediate learners, ensuring you transform raw data into actionable insights and compelling visualizations.

Syllabus

Course Introduction

In this module, we will embark on a guided tour of the course layout, covering essential tools and resources like R, RStudio, and course code access. We will also establish our project setup and understand various file formats, culminating in dynamic documentation using Rmarkdown.

Data Types and Structures

In this module, we will delve into fundamental data types and structures in R. From basic types like integers and logical values to complex structures like matrices, arrays, and data frames, we will explore and manipulate these elements to build a solid data science foundation.

R Programming

In this module, we will explore the core programming constructs in R. We will cover operators, loops, and functions, providing both theoretical understanding and practical experience through labs, enabling us to automate tasks and write efficient code.

Data Import and Export

In this module, we will learn to handle data import and export processes in R. From fetching data from diverse origins to saving and sharing results, we will also explore web scraping techniques to extract valuable information from online sources.

Basic Data Manipulation

In this module, we will master essential data manipulation techniques in R. We will learn to construct efficient data pipelines, filter data subsets, aggregate large datasets, and reshape data structures. Labs will reinforce our practical skills through real-world challenges.

Data Visualization

In this module, we will explore data visualization techniques using R. From static plots with ggplot2 to interactive visualizations with plotly and leaflet, we will learn to communicate data insights effectively. Practical labs will enhance our skills in creating compelling visual narratives.

Advanced Data Manipulation

In this module, we will tackle advanced data manipulation techniques. We will learn to detect and manage outliers and missing data, ensuring robust analyses. Additionally, we will explore regular expressions for powerful text data manipulation, enhancing our data processing capabilities.