The WordLens-Project
  • The WordLens-Project
  • Course Overview
  • Part 1: Transform and Visualize Data
    • 1 Working Environment
    • 2 R and the Tidyverse
    • 3 Data Loading
      • Tabular Data
      • Tidy Data
      • Exploring New Data
    • 4 Data Transformation
      • Select Columns
      • Filter Rows
      • Sort Rows
      • Add Or Change Columns
        • Calculate New Columns
        • Change Data Types
        • Rename Columns
        • Joining Data Sets
      • Summarize Rows
    • 5 Data Visualization
      • Pleas for Visualization
      • Fast and Simple Plots
      • Grammar of Graphics
  • Part 2: Rule-Based NLP
    • 6 Unstructured Data
    • 7 Searching Text
    • 8 Tokenizing Text
      • Filter or Sample Data
      • Clean and Normalize Text
      • Split Text Into Tokens
      • Removing Stop Words
      • Enrich Tokens
    • 9 Topic Classification
      • Deductive
      • Inductive
    • 10 Sentiment Analysis
    • 11 Text Classification
    • 12 Word Pairs and N-Grams
  • Part 3: NLP with Machine Learning
    • 13 Text Embeddings
    • 14 Part-Of-Speech
    • 15 Named Entities
    • 16 Syntactic Dependency
    • 17 Similarity
    • 18 Sentiment
    • 19 Text Classification
    • 20 Transformers
    • 21 Training a Model
    • 22 Large Language Models
  • Appendix
  • Resources
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  1. Part 1: Transform and Visualize Data
  2. 3 Data Loading

Tidy Data

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Last updated 2 years ago

To learn about the concept of tidy data, I highly recommend you read the chapter "" from the book "" by Hadley Wickham and Garret Grolemund.

To learn about the concept of tidy data in the context of text data, I refer you to the chapter 1 "" from the book "" by Julia Silge and David Robinson:

6 Data Tidying
R for Data Science
The tidy text format
Text Mining with R