• Fundamentals of R Studio
• Data Preparation within R Studio
• Create Visualisations within R Studio
• Build Working Functions
• Work With Loops Such As For, If, Ifelse & While
• Data Science

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Course Content:

Module 1: Introduction to R and RStudio

1. Installing R and RStudio
   • Download and install R from CRAN.
   • Download and install RStudio.
2. Getting Started with RStudio
   • Overview of the RStudio interface.
   • Customizing RStudio settings.
   • Navigating the Source, Console, Environment/History, and Files/Plots/Packages/Help panes.

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Module 2: Basic R Programming Refresher

1. Basic R Syntax
   • Variables and data types.
   • Basic operations and functions.
2. Data Structures in R
   • Vectors, matrices, lists, and data frames.
3. Basic Data Manipulation
   • Subsetting and indexing.
   • Basic data manipulation with base R functions.

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Module 3: Working with RStudio

1. Projects in RStudio
   • Creating and managing projects.
   • Setting working directories.
2. Script Management
   • Creating, saving, and sourcing R scripts.
   • Organizing scripts within a project.

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Module 4: Data Import and Export

1. Reading Data into R
   • Reading CSV, Excel, and other data formats.
   • Using read.csv(), read.table(), and packages like readxl and data.table.
2. Writing Data from R
   • Writing data to CSV and other formats using write.csv().
   • Exporting data with packages like writexl.

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Module 5: Data Manipulation with dplyr

1. Introduction to dplyr
   • Installing and loading dplyr.
   • Basic functions: filter(), select(), mutate(), arrange(), and summarize().
2. Advanced Data Manipulation
   • Grouping data with group_by().
   • Chaining operations with the pipe operator %>%.

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Module 6: Data Visualization with ggplot2

1. Introduction to ggplot2
   • Installing and loading ggplot2.
   • Basic components: aesthetics, geoms, and themes.
2. Creating Basic Plots
   • Scatter plots, line charts, and bar charts.
3. Advanced Plotting Techniques
   • Faceting, customizing themes, and adding annotations.

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Module 7: RMarkdown and Reproducible Research

1. Introduction to RMarkdown
   • Creating RMarkdown documents.
   • YAML header, markdown syntax, and code chunks.
2. Generating Reports
   • Rendering HTML, PDF, and Word documents.
   • Customizing document appearance with themes and templates.

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Module 8: Statistical Analysis in RStudio

1. Basic Statistical Functions
   • Descriptive statistics: mean, median, mode, variance, and standard deviation.
   • Hypothesis testing: t-tests, chi-squared tests.
2. Regression Analysis
   • Linear regression with lm().
   • Interpreting regression outputs.

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Module 9: Working with Git and Version Control

1. Introduction to Git
   • Setting up Git in RStudio.
   • Basic Git commands: clone, commit, push, pull.
2. Version Control in RStudio
   • Using the Git tab in RStudio.
   • Managing repositories and collaboration.

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Module 10: Advanced RStudio Features

1. Debugging and Profiling
   • Using the RStudio debugger.
   • Profiling code performance.
2. Creating Shiny Apps
   • Introduction to Shiny.
   • Basic structure of a Shiny app.
   • Deploying Shiny apps.

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Module 11: Best Practices and Resources

1. Coding Best Practices
   • Writing clean and efficient code.
   • Documenting and commenting code.
2. Resources for Further Learning
   • Online resources, books, and communities.
   • Advanced courses and certifications.

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Who this course is for:

• Beginner R Studio users with an interest in data analysis
• Data Scientists wanting to learn RStudio

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