Free ebook on practical R programming for data analysis, from importing and transforming data to ggplot2 charts and R Markdown reports.
Free ebook content
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R Programming Foundations for Data Analysis
+ Exercise: In a script-first workflow in RStudio, which action best supports reproducible results when you change your code?
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Working with Data Frames in R: Tibbles, Indexing, and Inspection
+ Exercise: When cleaning the sales tibble, how can you reliably detect invalid date strings in order_date?
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Importing Real-World Files into R (CSV, Excel, and Text Data)
+ Exercise: When importing a real-world delimited text file into R, which approach best improves reliability and helps prevent silent data issues?
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Data Transformation with Tidy Principles Using dplyr
+ Exercise: In a dplyr workflow, what is the main effect of using group_by(customer_id, month) followed by summarise(...) when building a customer-by-month table?
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Reshaping and Combining Data: Joins, Bindings, and Pivoting
+ Exercise: You have an orders table and a customers table. Some orders have customer_id values that do not exist in customers. You want to keep all orders, but clearly flag missing customer attributes after merging. What approach best achieves this?
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Clear Data Visualization in R with ggplot2
+ Exercise: In ggplot2, how should you apply an aesthetic like alpha when you want every point to have the same transparency regardless of the data?
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From Analysis to Shareable Results: R Markdown Reporting Workflow
+ Exercise: In an R Markdown report, which chunk option is best when you want the code to run but hide both the code and its output (e.g., for a setup chunk that sets global defaults)?
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A Practical End-to-End R Data Analysis Project
+ Exercise: Why does the workflow recompute revenue and profit after cleaning the data instead of trusting the imported totals?
About the free ebook
R Programming for Data Analysis: The Practical Starter Guide
Build practical R skills for turning raw data into clear, useful analysis. This free ebook introduces a hands-on workflow for working with datasets, applying tidy data principles, creating visualizations, and sharing reproducible results.
Learn a practical R workflow
Start with core R programming concepts and learn how to inspect data frames and tibbles with confidence. You will work with common real-world file formats, including CSV, Excel, and text files, then prepare data for analysis using clear, repeatable steps.
- Import and inspect structured datasets
- Filter, select, sort, summarize, and mutate data with dplyr
- Combine datasets with joins and bindings
- Reshape data using pivoting techniques
- Create readable charts with ggplot2
- Produce shareable analysis documents with R Markdown
Move from raw files to meaningful results
The ebook emphasizes the decisions that make analysis dependable: checking data types, handling missing values, choosing appropriate transformations, and communicating findings clearly. Rather than treating code as isolated commands, it presents R as a complete data-analysis environment.
Apply your knowledge in an end-to-end project
A practical project brings the workflow together, from importing source data through transformation, visualization, and reporting. By following the examples, you can develop a reusable foundation for business intelligence, reporting, research, and data science tasks.
Use this guide as a reference while practicing in R, adapting the techniques to your own datasets and analytical questions.
How do I import CSV and Excel files into R?
The ebook explains importing CSV, Excel, and text files, then inspecting their structure and data types.
What R functions are used for data transformation with dplyr?
It covers common dplyr operations for selecting, filtering, sorting, creating, grouping, and summarizing variables.
How can I create a reproducible report in R?
The guide introduces an R Markdown workflow for combining analysis code, visualizations, and written findings.
This ebook includes:
8 content chapters
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
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