About
This project is a comprehensive guide to the R programming language, built with Quarto. It covers a wide range of topics, from the fundamentals of R to advanced applications in data manipulation, visualization, and publishing.
Key Sections:
- Intro to R:
- Basic R: Covers fundamental concepts like working with files, handling errors, using conditional statements (if/else), loops (
for,while,map), creating functions, managing packages, and using version control withrenv. It also touches on interacting with Python usingreticulate. - Probability: Explores concepts like random numbers, permutations, combinations, conditional probability, the derangement problem, and key probability distributions (Binomial, Normal). It also demonstrates how to check for normality using histograms, Q-Q plots, and the Shapiro-Wilk test.
- Statistics: Introduces basic statistical concepts, including different types of variables, measures of centrality and spread, covariance, and correlation.
- Error Handling: Provides practical tips for troubleshooting common issues, such as Python version conflicts with
reticulate.
- Basic R: Covers fundamental concepts like working with files, handling errors, using conditional statements (if/else), loops (
- Data Manipulation:
- I/O: Reading and writing data from various formats.
- Data Structures: Understanding and working with R’s data structures (vectors, lists, data frames, etc.).
- Tidyverse: A deep dive into the
tidyverseecosystem for data manipulation and wrangling. - data.table: High-performance data manipulation using the
data.tablepackage. - Recipes: Preprocessing data for modeling using the
recipespackage. - Resampling: Techniques for creating training and testing sets for model validation.
- SQL Databases: Interacting with SQL databases from within R.
- Data Management: Best practices for organizing and managing data in your projects.
- Plotting:
- ggplot2: Creating a wide variety of static visualizations.
- plotly: Building interactive plots.
- Image Processing: Basic image manipulation and analysis.
- Financial Data: Visualizing financial time-series data.
- Mapping: Creating maps and spatial visualizations.
- Publishing:
- Shiny: Building interactive web applications.
- Quarto: Creating dynamic documents, presentations, and websites.
- Dashboards: Designing and building data dashboards.
- Email: Sending emails from R, potentially with embedded reports or plots.
- Git: Using Git for version control and collaboration.