/Lessons

Interactive R lessons

Primary LanguageHTMLGNU General Public License v3.0GPL-3.0

R Lessons

R interactive lessons for the Introduction to Quantitative Text Analysis for Linguists textbook.

Installation

Install swirl package from CRAN:

install.packages("swirl")

Install swirl lessons from GitHub:

library(swirl)
install_course_github("qtalr/Lessons")

Usage

Run swirl() to start working on the lessons:

swirl()

Overview

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 Introduces strategies for creating reproducible computing environments including hardware, operating system, and software using Docker and {renv}. Contribute