R Programming Language

?R is a versatile programming language and environment designed for statistical computing and graphics. It excels in data analysis, visualization, and machine learning.

With a rich ecosystem of packages, R facilitates diverse statistical techniques. Its syntax is concise, emphasizing vectorized operations. Data frames, a core structure, simplify handling tabular data. R supports dynamic graphics, allowing real-time interaction with plots.

Popular packages like ggplot2 enhance data visualization, while dplyr streamlines data manipulation. R's open-source nature encourages community contributions, fostering a vast repository of packages. Its integration with other languages and tools, coupled with active user forums, solidifies R's position in statistical computing.

?In R programming classes, we begin with an introduction to R's fundamentals, emphasizing its role in statistical analysis and data science. Students set up their R environment, installing both R and RStudio, before delving into basic syntax and data structures.

The curriculum covers essential programming concepts such as variables, data types, and control structures. Practical exercises and hands-on coding sessions are integrated to reinforce theoretical knowledge. As the course progresses, we explore advanced topics like data manipulation, visualization, and statistical analysis using popular R packages.

Throughout the training, emphasis is placed on real-world applications and problem-solving, ensuring participants gain practical skills for data analysis and decision-making using R. The interactive nature of the classes encourages active participation and provides a comprehensive learning experience for students of varying programming backgrounds.

Syllabus:

• Module 1: Introduction to R Programming
• Overview of R and its applications
• Installation and setup of R and RStudio
• Basic R syntax and data types
• Module 2: Working with Data in R
• Data import/export (CSV, Excel, etc.)
• Data structures: vectors, matrices, data frames
• Data manipulation using dplyr
• Module 3: Data Visualization with ggplot2
• Introduction to ggplot2
• Creating various types of plots (scatter plots, bar charts, histograms)
• Customizing plots and adding aesthetics
• Module 4: R Functions and Control Structures
• Writing functions in R
• Conditional statements (if-else)
• Loops (for, while) and their applications
• Module 5: Statistical Analysis with R
• Descriptive statistics
• Inferential statistics and hypothesis testing
• Linear regression analysis
• Module 6: Working with Time Series Data
• Handling time-based data in R
• Time series analysis and visualization
• Introduction to forecasting
• Module 7: R Packages and Libraries
• Understanding and installing R packages
• Exploring popular libraries (tidyverse, caret, etc.)
• Module 8: Data Cleaning and Preprocessing
• Identifying and handling missing data
• Dealing with outliers
• Feature scaling and transformation
• Module 9: Advanced Data Visualization
• Interactive visualizations with Shiny
• Creating dashboards with flexdashboard
• Module 10: Machine Learning with R
• Overview of machine learning in R
• Building and evaluating machine learning models
• Classification and regression algorithms
• Module 11: Web Scraping with R
• Basics of web scraping
• Using rvest and other packages for web scraping
• Module 12: Geospatial Data Analysis
• Introduction to spatial data in R
• Working with spatial data packages (sf, sp)
• Creating maps with leaflet
• Module 13: Version Control with Git and GitHub
• Basics of version control with Git
• Collaborating on projects using GitHub
• Module 14: R Markdown and Reproducible Research
• Creating dynamic documents with R Markdown
• Reproducible research practices
• Module 15: R in Production
• Deploying R models in production
• Integration with other languages and systems
• Best practices for scalable R code