This course provides a comprehensive introduction to the Tidyverse, a powerful ecosystem of R packages designed for efficient and intuitive data analysis. Students will learn how to import, clean, transform, visualize, and model data using modern R tools.
In data science, raw data is often messy, requiring careful cleaning and transformation before meaningful analysis can take place. The Tidyverse simplifies these tasks by providing a consistent and user-friendly set of tools for data manipulation, visualization, and modeling. Mastering the Tidyverse allows analysts and researchers to work more efficiently, ensuring data is well-structured and insights are clearly communicated.
The course is divided into four key sections, each focusing on a critical phase of the data analysis pipeline:
(1) Data Import & Cleaning (Tidying the Data): Data comes in various formats: CSV files, Excel spreadsheets, databases, and even raw text files. Before any meaningful analysis can take place, data must be imported, structured, and cleaned. This section introduces the tools and best practices for handling messy data.
Key topics include:
- Importing data using the readr, readxl, and haven packages, ensuring seamless integration with different file types (e.g., CSV, Excel, JSON, SPSS, Stata).
- Understanding the concept of tidy data, a structured format that simplifies analysis and makes transformations more intuitive.
- Reshaping data using the tidyr package, including pivoting between long and wide formats, separating and uniting columns, and handling hierarchical data structures.
- Dealing with missing values, duplicate entries, and inconsistencies, ensuring a clean and reliable dataset for analysis.
(2) Data Transformation (Joining & Manipulating Data): Once the data is clean, the next step is data transformation: filtering, summarizing, and modifying datasets to extract meaningful insights. This section focuses on dplyr, one of the most powerful packages in the Tidyverse, which provides a grammar for working with structured data efficiently.
Key topics include:
- Filtering and selecting data based on conditions using functions like filter(), select(), and arrange().
- Summarizing and aggregating data with group_by() and summarize(), allowing for deeper insights into trends and patterns.
- Joining multiple datasets using different types of joins (inner, left, right, full), enabling the integration of information from multiple sources.
- Creating new variables dynamically using mutate() and case_when(), which help derive additional insights from existing data.
- Functional programming with purrr, which allows for efficient iteration and manipulation of lists and nested data structures.
(3) Data Visualization: After cleaning and transforming the data, the next step is to create effective visualizations that communicate findings clearly. This section introduces ggplot2, the most widely used R package for data visualization, enabling students to craft informative and aesthetically pleasing plots.
Key topics include:
- The Grammar of Graphics: understanding how ggplot2 structures visualizations and why this approach is so powerful.
- Creating basic plots, including scatter plots, line charts, histograms, and bar charts.
- Customizing aesthetics, such as color schemes, themes, labels, legends, and annotations to make plots more informative.
- Faceting and grouping data, allowing comparisons across different categories or time periods.
- Combining multiple plots into dashboards or complex visualizations.
- Best practices in data visualization, ensuring clarity, accuracy, and impact in communicating insights.
(4) Data Modeling: The final step in the data analysis workflow is modeling, where data is used to make predictions or uncover hidden relationships. This section introduces the tidymodels framework, which provides a consistent and streamlined approach to building machine learning models in R.
Key topics include:
- Introduction to machine learning models: understanding different types of models (e.g., regression, classification) and their applications.
- Building predictive models using tidymodels, a modern framework for machine learning in R.
- Evaluating model performance with accuracy, precision-recall, ROC curves, and cross-validation.
- Feature engineering and selection, refining models for better accuracy and interpretability.
- Interpreting model results and integrating them into the broader data analysis pipeline.
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