Hands-On R Programming: Build Real World Data Projects

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Hands-on learning with R: Analyze, visualize, and interpret real world data like a pro.

What you will learn


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What is R?

History and applications of R

Installing and Configuring R and RStudio

Basic R Syntax and Data Types

Vectors, Matrices, and Arrays

Data Frames and Lists

Conditional Statements (if-else)

Loops (for, while)

Creating and Using Functions in R

Function Arguments and Scoping

Data Manipulation with dplyr (filter, select, mutate, arrange)

Data Tidying with Tidyr (pivot_longer, pivot_wider)

Joining and Merging Data Frames

Creating Various Types of Plots (scatter plots, bar plots, line plots, histograms)

Customizing Plot Aesthetics (colors, labels, themes)

Creating Interactive Plots

Descriptive Statistics (mean, median, standard deviation, quartiles)

Hypothesis Testing (t-tests, chi-squared tests)

Regression Analysis (linear regression, multiple regression)

Add-On Information:

  • Master the R Ecosystem: Go beyond basic syntax to understand the practical philosophy behind R, enabling you to tackle diverse data challenges effectively.
  • Develop a Data Analysis Workflow: Learn to structure your R projects, ensuring reproducibility and scalability from initial data import to final interpretation.
  • Cultivate Problem-Solving Skills: Apply R to solve common data-related scenarios, building your confidence in interpreting results and drawing meaningful conclusions.
  • Visualize Data Intuitively: Move from standard plots to creating compelling, publication-ready visualizations that communicate complex insights clearly.
  • Automate Repetitive Tasks: Discover how to leverage R’s power to streamline your data cleaning, transformation, and reporting processes, saving you valuable time.
  • Build Robust Data Pipelines: Gain the skills to chain together data manipulation and analysis steps, creating efficient workflows for ongoing data projects.
  • Interpret Statistical Outputs: Understand the practical meaning of statistical tests and model coefficients, translating numerical results into actionable business or research insights.
  • Communicate Findings Effectively: Learn to present your data stories through well-crafted analyses and impactful visualizations, catering to both technical and non-technical audiences.
  • Leverage the R Community: Understand how to access and utilize the vast resources and packages available within the R community to enhance your analytical capabilities.
  • Become a Data Storyteller: Transform raw data into compelling narratives that drive understanding and decision-making.
  • Develop Intuitive Data Wrangling: Gain a deep understanding of how to shape and reshape your data for analysis, making complex transformations feel natural.
  • Implement Advanced Statistical Techniques: Apply sophisticated statistical methods to uncover deeper patterns and relationships within your data.
  • PROS:
  • Highly Practical Focus: Emphasis on building tangible data projects translates directly to real-world applicability.
  • Comprehensive Skill Development: Covers a broad spectrum of R functionalities essential for data professionals.
  • Strong Foundation for Advanced Topics: Provides the necessary skills to confidently explore more specialized areas of data science.
  • CONS:
  • Requires Consistent Practice: Success hinges on actively applying concepts through project work.
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