The Data Preparation Journey: Finding Your Way with R

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface
About the Author
1. Introduction
	1.1. The origin of data
	1.2. Analyzing data: the data science process
	1.3. Data in the wild
	1.4. Data collection
2. Foundations
	2.1. The data preparation process
		2.1.1. Elements of an iterative process
	2.2. Data quality
		2.2.1. Tidy data
	2.3. Cleaning the data
3. Data documentation
	3.1. Introduction
	3.2. Documentation and recordkeeping
		3.2.1. Elements of effective recordkeeping
	3.3. Code documentation
		3.3.1. Literate programming
		3.3.2. The instruction list
		3.3.3. An example of documented code
	3.4. Project documentation
		3.4.1. README files
		3.4.2. Capturing the directory structure
		3.4.3. The data dictionary or code book
4. Importing data
	4.1. Introduction
	4.2. Data formats
	4.3. Importing data
		4.3.1. Check your results
		4.3.2. Conclusion
5. Importing data: plain-text files
	5.1. Delimited plain-text files
	5.2. Using {readr} to read a CSV file
	5.3. Fixed-width files
		5.3.1. An extreme example of a fixed-width file
6. Importing data: Excel
	6.1. Introduction
	6.2. Extended example
		6.2.1. Excel formatting as data
7. Importing data: statistical software
	7.1. Statistical software
		7.1.1. {labelled} -
		7.1.2. Reading an SPSS survey file: “Video”
		7.1.3. Factors
	7.2. Creating a labelled dataframe from an Excel file
	7.3. Creating a labelled dataframe from SPSS syntax
		7.3.1. National Travel Survey (NTS)
8. Importing data: PDF files
	8.1. PDF files
		8.1.1. Getting started
		8.1.2. Extended example: ferry traffic
9. Data from web sources
	9.1. Introduction
	9.2. R packages for direct connection
		9.2.1. Office of National Statistics (UK)
		9.2.2. Statistics Canada
	9.3. Other R packages for direct access to data
10. Linking to relational databases
	10.1. Relational data
		10.1.1. Connect to the database
		10.1.2. Submit queries
		10.1.3. Using SQL in your R code
	10.2. Running SQL language chunks in R Markdown
		10.2.1. Mutating joins and summary tables
	10.3. Using the {tidylog} package
11. Exploration and validation strategies
	11.1. Identifying dirty data
	11.2. Exploratory data analysis
		11.2.1. Data visualization
	11.3. Validation methods
		11.3.1. The {validate} package
		11.3.2. badpenguins
		11.3.3. Investigating the fails
12. Cleaning techniques
	12.1. Introduction
	12.2. Cleaning dates
		12.2.1. Creating a date-time field
	12.3. Cleaning strings
		12.3.1. Regular expressions
		12.3.2. Data cleaning with {stringr}
		12.3.3. Example: Bureau of Labor Statistics by NAICS code
		12.3.4. Example: NOC: split code from title
	12.4. Creating conditional and calculated variables
		12.4.1. Classification systems
	12.5. Indicator variables
		12.5.1. {fastDummies}
	12.6. Missing values
		12.6.1. Missing as formatting
		12.6.2. Missing numeric values
		12.6.3. Time series imputation
	12.7. Creating labelled factors from numeric variables
13. Recap
	13.1. Some key points
	13.2. Where to from here?
Bibliography
Index