Introduction to R
Launch your data analysis career with R, the open-source language trusted by statisticians, researchers, and data scientists worldwide. This course takes you from installation to exploratory analysis, covering data wrangling, visualization, and reproducible reporting. Whether you're switching careers or leveling up your analytical toolkit, you'll gain hands-on skills that apply immediately on the job.
What you will learn:
Configure a fully functional R and RStudio environment ready for real data work.
Build and manipulate vectors, data frames, and lists to handle diverse dataset structures.
Transform and clean messy datasets using dplyr and tidyr from the tidyverse.
Create compelling, publication-quality visualizations with ggplot2's grammar of graphics.
Conduct systematic exploratory data analysis and summarize findings with descriptive statistics.
Produce reproducible, shareable reports by combining code, output, and narrative in R Markdown.
How you study in practice Introduction to R
How you practice Introduction to R
For companies that want to train their team
With Dedika for Business, the course includes exercises and examples tailored to your own business and the way your company needs.
Course content
8 Chapters • 35 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsGetting Started with R
Getting Started with R
Lesson 1 • Navigating the RStudio Interface
Identifies the four RStudio panes and their functions. Students practice running code interactively and saving scripts.
Lesson 2 • What R Is and Why It Matters
R's origins, strengths, and common use cases in data science and statistics. Establishes motivation before any syntax is introduced.
Lesson 3 • Getting Help in R
Covers built-in help functions, documentation pages, and community resources. Teaches self-sufficiency when encountering unfamiliar functions.
Lesson 4 • Installing R and RStudio
Step-by-step installation of R and RStudio on major operating systems. Ensures every student has a functional environment from day one.
Chapter 2HideHide detailsSee detailsR Syntax and Core Data Types
R Syntax and Core Data Types
Lesson 1 • Atomic Data Types in R
Introduces numeric, integer, character, logical, and complex types. Students use typeof() and class() to inspect values.
Lesson 2 • Basic Syntax and Expressions
Covers arithmetic, logical, and comparison operators with correct R syntax. Forms the grammatical foundation for all subsequent code.
Lesson 3 • Variables and the Workspace
Explains variable naming rules, assignment, and workspace management. Students practice listing, removing, and inspecting objects.
Lesson 4 • Working with Missing Values
Defines NA, NaN, NULL, and Inf and explains when each appears. Students learn to detect and handle missing data from the start.
Chapter 3HideHide detailsSee detailsVectors, Matrices, and Arrays
Vectors, Matrices, and Arrays
Lesson 1 • Vectorized Operations
Demonstrates element-wise arithmetic and recycling rules. Students understand why R avoids explicit loops for most numeric tasks.
Lesson 2 • Matrices: Creation and Operations
Constructs matrices with matrix() and performs row/column operations. Connects 2D structure to linear algebra and tabular data concepts.
Lesson 3 • Creating and Indexing Vectors
Builds vectors with c(), seq(), and rep() and accesses elements by position and name. Vectors underpin nearly every R operation.
Lesson 4 • Arrays and Multidimensional Data
Extends matrix concepts to n-dimensional arrays using array(). Students index and slice higher-dimensional structures.
Chapter 4HideHide detailsSee detailsLists and Data Frames
Lists and Data Frames
Lesson 1 • Subsetting and Filtering Data Frames
Applies logical conditions and index notation to extract rows and columns. Directly prepares students for data cleaning tasks.
Lesson 2 • Modifying and Merging Data Frames
Covers adding columns, renaming variables, and joining two data frames. Students produce analysis-ready datasets from multiple sources.
Lesson 3 • Understanding and Creating Lists
Defines lists as ordered collections of arbitrary objects. Students create nested lists and access elements with [, [[, and $.
Lesson 4 • Introduction to Data Frames
Presents data frames as the primary tabular structure in R. Students create, inspect, and understand the column-as-vector model.
Chapter 5HideHide detailsSee detailsControl Flow and Functions
Control Flow and Functions
Lesson 1 • Conditional Statements
Implements if, else if, and else logic and the vectorized ifelse(). Enables data-driven branching within scripts.
Lesson 2 • Loops in R
Covers for, while, and repeat loops with break and next controls. Students recognize when loops are appropriate versus vectorized alternatives.
Lesson 3 • Scope and the Apply Family
Explains lexical scoping and replaces loops with lapply(), sapply(), and vapply(). Students write cleaner, more idiomatic R code.
Lesson 4 • Writing Custom Functions
Defines functions with arguments, default values, and explicit return values. Promotes code reuse and modular script design.
Chapter 6HideHide detailsSee detailsImporting, Cleaning, and Transforming Data
Importing, Cleaning, and Transforming Data
Lesson 1 • Data Transformation with dplyr
Applies the five core dplyr verbs to filter, select, mutate, arrange, and summarize. Students chain operations with the pipe operator.
Lesson 2 • Reshaping Data with tidyr
Converts between wide and long formats using pivot_longer() and pivot_wider(). Students recognize tidy data principles and apply them.
Lesson 3 • Data Cleaning Fundamentals
Addresses duplicates, inconsistent strings, and type mismatches. Establishes systematic cleaning habits before any analysis begins.
Lesson 4 • Exporting Cleaned Data
Writes processed data to CSV, RDS, and Excel formats for sharing. Completes the import-clean-export workflow students will use repeatedly.
Lesson 5 • Reading Data into R
Imports CSV, Excel, and delimited files using base R and readr. Students handle encoding issues and inspect imported data immediately.
Chapter 7HideHide detailsSee detailsData Visualization with ggplot2
Data Visualization with ggplot2
Lesson 1 • Faceting and Multi-Panel Plots
Uses facet_wrap() and facet_grid() to display subgroup comparisons. Students reveal patterns across categories without cluttering a single plot.
Lesson 2 • Common Chart Types
Implements scatter plots, bar charts, histograms, and box plots. Students select the appropriate chart type for each data question.
Lesson 3 • Saving and Exporting Plots
Exports plots to PNG, PDF, and SVG at specified dimensions and resolution. Ensures visuals meet quality standards for reports and presentations.
Lesson 4 • Grammar of Graphics Foundations
Explains ggplot2's layered model: data, aesthetics, and geoms. Students construct their first plots and understand how layers combine.
Lesson 5 • Scales, Labels, and Themes
Customizes axes, color palettes, titles, and overall plot appearance. Transforms default plots into polished, presentation-ready visuals.
Chapter 8HideHide detailsSee detailsExploratory Data Analysis and Reporting
Exploratory Data Analysis and Reporting
Lesson 1 • Introduction to R Markdown
Creates reproducible documents that weave code, output, and prose. Students render HTML and PDF reports from a single source file.
Lesson 2 • Building a Complete EDA Report
Integrates cleaning, analysis, and visualization into one R Markdown report. Students practice the full analytical workflow on a real dataset.
Lesson 3 • Grouped Summaries and Comparisons
Aggregates data by category and compares distributions across groups. Directly applies dplyr skills to answer business and research questions.
Lesson 4 • Identifying Patterns and Outliers
Uses visual and statistical methods to detect anomalies and relationships. Prepares students to ask better questions before modeling.
Lesson 5 • Descriptive Statistics in R
Computes measures of center, spread, and shape using base R and dplyr. Provides the numerical foundation for interpreting any dataset.
Your valid completion certificate
This course is for you:
Biologists and lab researchers: need to analyze experimental data beyond spreadsheets.
Business analysts: want to replace manual Excel workflows with scalable R scripts.
Graduate students: must handle datasets for theses but lack formal programming training.
Journalists and policy researchers: seek to visualize and interpret data-driven stories.
Career changers: aiming to break into data roles without a computer science background.
Marketing professionals: ready to move from dashboards to hands-on statistical analysis.
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