R Programming for Data Science

3.

Data Import and Export

3.1.

File System Navigation

3.1.1.

Working Directory Concepts

3.1.2.

Setting and Getting Working Directory

3.1.3.

File Path Construction

3.1.4.

Relative vs Absolute Paths

3.2.

Reading Structured Data

3.2.1.

3.2.1.1.

Basic CSV Reading

3.2.1.2.

Handling Headers

3.2.1.3.

Custom Delimiters

3.2.1.4.

Quote Characters

3.2.1.5.

Missing Value Representation

3.2.1.6.

Data Type Specification

3.2.2.

Tab-Delimited Files

3.2.3.

Fixed-Width Files

3.2.4.

Custom Delimited Files

3.2.5.

Large File Handling Strategies

3.3.

Excel File Integration

3.3.1.

Excel Package Options

3.3.2.

Reading Single Worksheets

3.3.3.

Reading Multiple Worksheets

3.3.4.

Handling Excel Formatting

3.3.5.

Cell Range Specification

3.4.

Statistical Software Data

3.4.1.

SPSS File Import

3.4.2.

SAS File Import

3.4.3.

Stata File Import

3.4.4.

Package-specific Considerations

3.5.

Database Connectivity

3.5.1.

Database Connection Concepts

3.5.2.

DBI Package Framework

3.5.3.

SQLite Integration

3.5.4.

MySQL Integration

3.5.5.

PostgreSQL Integration

3.5.6.

Connection Management

3.5.7.

SQL Query Execution

3.5.8.

Result Set Handling

3.6.

Web Data Sources

3.6.1.

Reading from URLs

3.6.2.

API Data Access

3.6.3.

Web Scraping Basics

3.6.4.

JSON Data Handling

3.6.5.

XML Data Processing

3.7.

Data Export Operations

3.7.1.

3.7.2.

3.7.3.

Database Writing

3.7.4.

Custom Format Export

3.8.

R Native Formats

3.8.1.

3.8.1.1.

Single Object Storage

3.8.1.2.

Compression Options

3.8.2.

3.8.2.1.

Multiple Object Storage

3.8.2.2.

Workspace Saving

3.8.3.

Loading Saved Objects

3.8.4.

Version Compatibility

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4. Data Manipulation with tidyverse