Installing the package | Additional recommended packages: | Creating a Corpus | Currently available corpus sources | Building a corpus from a character vector | Loading in files using the readtext package | Working with a quanteda corpus | Corpus principles | Tools for handling corpus objects | Adding two corpus objects together | Subsetting corpus objects | Exploring corpus texts | Tokenizing texts | "Pre-processing" tokens | Splitting and compounding tokens | Constructing a document-feature matrix | Analysing the document-feature matrix | Grouping documents by document variable | Dictionary functions | Further examples

Introduction | Overview of Functions | Verbs That Operate on Rows | Filtering documents | Slicing documents by position | Arranging documents | Keeping distinct documents | Verbs That Operate on Columns | Selecting docvars | Renaming docvars | Relocating docvars | Creating and modifying docvars | Extracting docvars | Getting an overview | Verbs That Operate on Groups of Rows | Counting observations | Verbs That Operate on Pairs of Data Frames | Joining with external data | Special handling of document names | Piping Operations

Naive Bayes | References

1. Introduction | 2. Reading one or more text files | 2.1 Plain text files (.txt) | 2.2 Comma- or tab-separated values (.csv, .tab, .tsv) | 2.3 JSON data (.json) | 2.4 PDF files | 2.5 Microsoft Word files (.doc, .docx) | 2.6 Text from URLs | 2.7 Text from archive files (.zip, .tar, .tar.gz, .tar.bz) | 3. Inter-operability with quanteda | 4. Solving common problems | 4.1 Remove page numbers using regular expressions | 4.2 Read files with different encodings

Introduction | Starting a spacyr session | Tokenizing and tagging texts | Extracting language properties from texts | Using other language models | Integrating spacyr with other text analysis packages | Finishing a session