R interactive lessons for the Introduction to Quantitative Text Analysis for Linguists textbook.
Install swirl package from CRAN:
install.packages("swirl")Install swirl lessons from GitHub:
library(swirl)
install_course_github("qtalr/Lessons")Run swirl() to start working on the lessons:
swirl()| Lesson | Description | Chapter |
|---|---|---|
| Intro to Swirl | Getting familiar with the Swirl interactive tutorial system for learning R | Preface |
| Workspace | Presents RStudio, a powerful IDE for R programming, explaining its user-friendly interface, the functions of its four main panes (Source, Console, Environment, Files), and how it enhances efficient R coding and project organization. | Text analysis |
| Vectors | Introduces vectors, detailing their creation, types, properties, operations, and variable naming conventions, underscoring the importance of vectors in R's data handling. | Text analysis |
| Objects | Explains R objects, particularly vectors and data frames, detailing object inspection, creation, coercion, and subsetting, and introduces tibbles as modern data frames, essential for mastering object manipulation in R. | Data |
| Packages and Functions | Covers R packages and functions, detailing package management, function usage, argument handling, and introduces the Tidyverse piping concept, demonstrating how to chain functions for efficient data manipulation. | Data |
| Summarizing Data | Provides an in-depth guide to summarizing data in R, showcasing methods for statistical summaries of vectors and data frames with functions like mean(), summary(), table(), and skim(), as well as using {dplyr}'s summarize() and group_by() for detailed and grouped data analysis. | Analysis |
| Visual Summaries | Teaches visual data summarization with {ggplot2} in R, explaining the layering of plots using ggplot(), aes(), and geom_*() functions to create informative graphics that enhance data interpretation and analysis. | Analysis |
| Project Environment | Highlights the importance of the computing environment in R for project management and reproducibility, detailing how to use sessionInfo() and sessioninfo::session_info() to inspect session details and emphasizing the role of Quarto documents in maintaining independent R sessions. | Research |
| Control Statements | Delves into R's control statements, including conditionals and iteration, to improve programming flow control. | Acquire |
| Custom Functions | Covers creating and using custom functions in R, focusing on their development, arguments, and how to return values effectively. | Acquire |
| Pattern Matching | Provides an introduction to pattern matching in text using regular expressions, covering basic syntax, literals, metacharacters, character classes, and quantifiers. | Curate |
| Tidy data | Reviews various R object types and demonstrated how to manipulate data frames, including adding columns, working with nested structures, and using functions like mutate(), group_by(), and unnest(). | Curate |
| Reshape by Rows | Covers how to manipulate the number of rows in a dataset through various methods including separating and collapsing rows, tokenizing and unnesting text, and filtering out rows using functions from the {dplyr}, {tidyr}, {stringr}, {tokenizers}, and {tidytext}. | Transform |
| Reshape by Columns | Explore how to use {stringr}, {tidyr}, and {dplyr} to normalize values, separate and collapse columns, recode values, and join columns, which are key operations for reshaping datasets by their columns. | Transform |
| Advanced Objects | Focuses on matrices and lists, covering their definition, creation, naming, inspection, element access, and calculations, with practical examples and considerations for text analysis research. | Explore |
| Advanced Visualization | A deeper dive into {ggplot2} to enhance visual summaries and provides an introduction to {factoextra} and {ggfortify} that extend {ggplot2} capabilities to model objects. | Predict |
| Advanced Tables | Explore how to enhance dataset summaries using {janitor} and present them effectively with {kableExtra}'s advanced formatting options. | Infer |
| Computing_Environment | Provides an overview of {renv} for pinning R and R package versions; an important component of the reproducible research workflow. | Contribute |