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

3 Data Loading

The first step in any data analysis is loading the data. In this lesson, we'll learn how to load data from different formats.

The first step in any data analysis is loading the data. In this lesson, we'll learn how to load data from different formats.

  • In Tabular Data we introduce the readr package to load data from CSV files into a data frame or Tibble. We also learn how to load other tabular file formats like Excel, Google Sheets, and R-Data Source (RDS) files.

  • The term Tidy Data refers to a certain format we want our data to be in before we start analyzing it. We'll introduce what we mean by this and how we can achieve this.

  • When we successfully loaded a new data set, we usually start by Exploring New Data. In this lesson, we introduce some useful functions for that.

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