Data Analysis with R Programming

Module 1 introduces learners to the exciting world of programming and provides an overview of R and RStudio. Learners will explore programming languages, the R-versus-Python debate, and ways to learn about programming. The module also covers the installation and use of R and RStudio, as well as hands-on activities to reinforce learning.

Module 2 delves into programming using RStudio, covering programming fundamentals, operators, vectors, lists, and other common data structures. Learners will also explore working with pipes, tidyverse, and R packages, with hands-on activities to practice coding in RStudio and using tidyverse for data analysis.

Module 3 focuses on working with data in R, covering data frames, cleaning and organizing data, transforming data, and data-import basics. Learners will gain practical experience through hands-on activities, including creating and working with data frames, cleaning and transforming data, and comparing data cleaning on different platforms.

Module 4 explores visualizations in R, aesthetics, and annotations, providing an in-depth understanding of generating visualizations using ggplot and enhancing visualizations in R. Learners will also practice creating visualizations, working with aesthetics and filters, and adding annotations to visualizations.

Module 5 focuses on documentation and reports, covering R Markdown, structure of markdown documents, code chunks, and exporting documentation. Learners will be able to create their own R Markdown notebook, add code chunks, and export the notebook, gaining practical skills in documenting and reporting using R.