Duration 3 days – 21 hrs
Overview
This entry-level course introduces participants to the R programming language and the RStudio environment, equipping them with the foundational skills to perform basic data analysis, data visualization, and reporting. Through hands-on exercises and guided labs, participants will gain confidence in working with data, writing R scripts, and using RStudio’s features for efficient coding and analysis.
Objectives
- Understand the basics of the R language and its syntax
- Navigate the RStudio interface effectively
- Import, manipulate, and clean data using R
- Create basic data visualizations (bar charts, scatter plots, etc.)
- Perform basic statistical analysis
- Use packages such as tidyverse for efficient data handling
- Write and run R scripts and R Markdown reports
Audience
- Data Analysts
- Researchers
- Students and Academics
- Business Analysts
- Professionals new to R and data science
Pre-requisites
- Basic computer literacy
- Familiarity with spreadsheets or any data-related tasks (optional)
- No prior programming experience required
Course Content
Module 1: Introduction to R and RStudio
- What is R and RStudio?
- Installing R and RStudio
- Overview of the RStudio interface: Script, Console, Environment, Plots
- Creating and saving R scripts
Module 2: Basic R Programming
- Variables and data types
- Operators and expressions
- Functions and packages
- Using install.packages() and library()
Module 3: Data Structures in R
- Vectors, matrices, lists, and data frames
- Indexing and subsetting data
- Exploring data using summary(), str(), head(), and tail()
Module 4: Data Import and Export
- Reading CSV, Excel, and text files using read.csv() and readxl
- Writing output to files
- Working with file paths in RStudio
Module 5: Data Manipulation with Tidyverse
- Introduction to the tidyverse
- Using dplyr for filtering, selecting, mutating, and summarizing
- Sorting and grouping data
- Combining datasets with join functions
Module 6: Data Visualization with ggplot2
- Introduction to ggplot2
- Creating bar charts, line graphs, scatter plots
- Customizing plots: labels, themes, colors
Module 7: Basic Statistical Analysis
- Descriptive statistics (mean, median, standard deviation)
- Frequency tables and cross-tabulations
- Introduction to correlation and basic testing (e.g., t-test)
Module 8: Introduction to R Markdown (Optional)
- Creating dynamic reports with R Markdown
- Embedding code, plots, and text
- Exporting to PDF, HTML, and Word formats
