Week 1: Getting Started with R

Day 1: Introduction to R and Installing Tools

• Objective: Familiarize yourself with R's basics, importance, and applications.
• Tasks:
• Install R from CRAN and RStudio from RStudio’s website.
• Learn R's history, strengths, and when to use it versus other languages (e.g., Python).

Day 2: Basics of R Syntax

• Objective: Understand R’s syntax, including variables, comments, and data types.
• Tasks:
• Use <- for variable assignment.
• Explore data types: numeric, character, logical, and factor.
• Write a simple script to create and print variables.

Day 3: Data Structures in R

• Objective: Discover R’s core data structures for manipulation.
• Tasks:
• Work with vectors, matrices, lists, and data frames.
• Use length(), str(), and summary() to inspect structures.

Day 4: Working with Vectors and Matrices

• Objective: Perform data storage and manipulation with vectors and matrices.
• Tasks:
• Execute vector operations (addition, subtraction, element-wise multiplication).
• Subset vectors and matrices; practice basic matrix operations.

Day 5: Data Frames in Depth

• Objective: Master data frames for data manipulation and analysis.
• Tasks:
• Create and modify data frames.
• Add/remove columns, and select specific rows/columns.

Day 6: Importing Data

• Objective: Learn to import data from various file types.
• Tasks:
• Use read.csv() for CSV files and explore other formats (Excel, JSON).

Day 7: Basic Data Manipulation

• Objective: Build foundational data manipulation skills.
• Tasks:
• Filter, reorder, and transform data with functions like subset(), filter(), and select().
• 
  

Week 2: Intermediate R Programming

Day 8: Control Structures

• Objective: Implement control structures (loops, conditionals).
• Tasks:
• Practice if, else, else if, for loops, and while loops.

Day 9: Functions in R

• Objective: Create reusable functions.
• Tasks:
• Write functions with default arguments and return values.
• Create a function to clean a dataset.

Day 10: Working with Dates and Strings

• Objective: Manipulate date-time and string data.
• Tasks:
• Work with as.Date(), lubridate, and stringr.

Day 11: Data Cleaning Techniques

• Objective: Learn essential data cleaning techniques.
• Tasks:
• Handle missing data, standardize columns, and manage outliers.

Day 12: Introduction to Tidyverse

• Objective: Use Tidyverse packages for data manipulation.
• Tasks:
• Install Tidyverse; learn tidy data principles and dplyr functions like filter(), arrange(), mutate().

Day 13: Exploratory Data Analysis (EDA)

• Objective: Conduct EDA to gain insights into data.
• Tasks:
• Calculate statistics (mean, median) and create visualizations (histograms, boxplots).

Day 14: Data Visualization with ggplot2

• Objective: Use ggplot2 for customizable visualizations.
• Tasks:
• Create and customize plots (bar charts, scatterplots).

Week 3: Advanced Data Handling and Visualization

Day 15: Advanced ggplot2

• Objective: Enhance visualizations with advanced ggplot2 techniques.
• Tasks:
• Use facets, multi-layered plots, and add interactivity with ggplotly.

Day 16: Joining and Reshaping Data

• Objective: Merge and reshape data using dplyr and tidyr.
• Tasks:
• Practice joins and pivot data formats.

Day 17: Advanced Data Manipulation with dplyr

• Objective: Perform complex transformations.
• Tasks:
• Chain operations, group, and summarize data.

Day 18: Statistical Analysis in R

• Objective: Conduct statistical tests.
• Tasks:
• Practice hypothesis testing, correlation, and linear regression.

Day 19: Working with Large Datasets

• Objective: Handle large datasets efficiently.
• Tasks:
• Use data.table and memory-efficient techniques for large files.

Day 20: Introduction to Machine Learning with R

• Objective: Start with machine learning concepts.
• Tasks:
• Explore packages (caret, mlr) and build a predictive model.

Week 4: Real-World Projects and Advanced Topics

Days 21–23: Project #1 — Exploratory Data Analysis

• Objective: Apply EDA skills to a dataset.
• Tasks:
• Import, clean, analyze, and visualize data; summarize findings.

Days 24–25: Project #2 — Data Visualization

• Objective: Focus on creating informative visualizations.
• Tasks:
• Use ggplot2 for polished visualizations.

Days 26–28: Project #3 — Building a Predictive Model

• Objective: Build and evaluate a predictive model.
• Tasks:
• Prepare data, apply a model (e.g., decision trees), and assess performance

Day 29: Advanced Machine Learning with R

• Objective: Explore advanced machine learning techniques.
• Tasks:
• Study ensemble learning, model tuning, and cross-validation.

Day 30: Review and Practice

• Objective: Solidify and plan next steps.
• Tasks:
• Review, identify areas for improvement, and create a learning plan.

Conclusion

Congratulations! You've completed a comprehensive 30-day journey through R programming fundamentals, advanced data handling, and introductory machine learning. With this roadmap, you've established a strong foundation in data analysis and R’s capabilities.