R Programming

Overview:

Introduction:

This training program provides participants with essential knowledge and skills in R programming. It empowers them to use R for data analysis, statistical computing, and data visualization effectively.

Program Objectives:

At the end of this program, participants will be able to:

• Understand the basics of R programming language.

• Perform data manipulation and analysis using R.

• Create visualizations to represent data insights.

• Apply statistical methods and models using R.

• Utilize R for reproducible research and reporting.

Targeted Audience:

• Data Analysts.

• Statisticians.

• Data Scientists.

• Researchers.

• Professionals interested in data analysis.

Program Outline:

Unit 1:

Introduction to R Programming:

• Overview of R and its applications.

• Setting up the R environment.

• Basic syntax and data types in R.

• Working with vectors, matrices, and lists.

• Introduction to R packages and libraries.

Unit 2:

Data Manipulation in R:

• Importing and exporting data.

• Data cleaning and preprocessing.

• Using dplyr for data manipulation.

• Handling missing values and outliers.

• Summarizing and aggregating data.

Unit 3:

Data Visualization with R:

• Introduction to data visualization principles.

• Creating basic plots using base R.

• Advanced visualizations with ggplot2.

• Customizing and saving plots.

• Interactive visualizations with plotly.

Unit 4:

Statistical Analysis with R:

• Descriptive statistics and exploratory data analysis.

• Inferential statistics: hypothesis testing, t-tests, chi-square tests.

• Correlation and regression analysis.

• Analysis of variance (ANOVA).

• Time series analysis.

Unit 5:

Advanced Topics and Best Practices:

• Writing and debugging R scripts.

• Functional programming in R.

• Reproducible research with RMarkdown.

• Introduction to Shiny for building web applications.

• Best practices for efficient R programming.