Crime by the Numbers: A Criminologist’s Guide to R

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface
About the author
I. Introduction
	1. A soup to nuts project example
		1.1. Big picture data example
		1.2. Little picture data example
			1.2.1. Loading packages
			1.2.2. Loading data
			1.2.3. Cleaning
			1.2.4. Aggregating
			1.2.5. Graphing
		1.3. Reusing and modifying code
	2. Introduction to R and RStudio
		2.1. Using RStudio
			2.1.1. Opening an R Script
			2.1.2. Setting the working directory
			2.1.3. Changing RStudio
			2.1.4. Helpful cheat sheets
		2.2. Assigning variables
		2.3. What are functions (and packages)?
		2.4. Reading data into R
			2.4.1. Loading data
		2.5. First steps to exploring data
	3. Data types and structures
		3.1. Data types
		3.2. Numeric, character, and logical (boolean)
		3.3. Data structures
			3.3.1. Vectors (collections of “things”)
			3.3.2. Data.frames
			3.3.3. Other data structures
	4. Reading and writing data
		4.1. Reading data into R
			4.1.1. R
			4.1.2. Excel
			4.1.3. Stata
			4.1.4. SAS
			4.1.5. SPSS
			4.1.6. Fixed-width ASCII
		4.2. Writing data
			4.2.1. R
			4.2.2. Excel
			4.2.3. Stata
			4.2.4. SAS
			4.2.5. SPSS
II. Project Management
	5. Mise en place
		5.1. Starting with a pencil and paper
			5.1.1. Tables and graphs
		5.2. R Projects
			5.2.1. Folders
		5.3. Modular R scripts
		5.4. Modular code
			5.4.1. Section labels
			5.4.2. Helper R scripts
	6. Collaboration
		6.1. Code review
			6.1.1. Style guidelines
		6.2. Documentation
			6.2.1. Comments
			6.2.2. Vignettes
	7. R Markdown
		7.1. Code
			7.1.1. Hiding code in the output
		7.2. Inline Code
		7.3. Tables
		7.4. Footnotes
		7.5. Citation
		7.6. Spell check
		7.7. Making the output file
	8. Testing your code
		8.1. Why test your code?
		8.2. Unit tests
			8.2.1. Modular test scripts
			8.2.2. How to write unit tests
			8.2.3. What to test
		8.3. Test-driven development (TDD)
	9. Git
		9.1. What is Git, and why do I need it?
		9.2. Git basics
		9.3. Using Git
			9.3.1. Setting up Git
			9.3.2. Setting up GitHub
		9.4. Setting up Git on an already-made R Project
		9.5. Using Git through RStudio
		9.6. When to commit
		9.7. Other resources
III. Clean
	10. Subsetting: Making big things small
		10.1. Select specific values
		10.2. Logical values and operations
			10.2.1. Matching a single value
			10.2.2. Matching multiple values
			10.2.3. Does not match
			10.2.4. Greater than or less than
			10.2.5. Combining conditional statements - or, and
		10.3. Subsetting a data.frame
			10.3.1. Select specific columns
			10.3.2. Select specific rows
			10.3.3. Subset Colorado data
	11. Exploratory data analysis
		11.1. Summary and Table
		11.2. Graphing
		11.3. Aggregating (summaries of groups)
		11.4. Pipes in dplyr
	12. Regular Expressions
		12.1. Finding patterns in text with grep()
		12.2. Finding and replacing patterns in text with gsub()
		12.3. Useful special characters
			12.3.1. Multiple characters []
			12.3.2. n-many of previous character {n}
			12.3.3. n-many to m-many of previous character {n,m}
			12.3.4. Start of string
			12.3.5. End of string $
			12.3.6. Anything
			12.3.7. One or more of previous +
			12.3.8. Zero or more of previous *
			12.3.9. Multiple patterns |
			12.3.10. Parentheses ()
			12.3.11. Optional text ?
		12.4. Changing capitalization
	13. Reshaping data
		13.1. Reshaping a single column
		13.2. Reshaping multiple columns
IV. Visualize
	14. Graphing with ggplot2
		14.1. What does the data look like?
		14.2. Graphing data
		14.3. Time-series plots
		14.4. Scatter plots
		14.5. Color blindness
	15. More graphing with ggplot2
		15.1. Exploring data
		15.2. Graphing a single numeric variable
			15.2.1. Histogram
			15.2.2. Density plot
			15.2.3. Count graph
		15.3. Graphing a categorical variable
			15.3.1. Bar graph
		15.4. Graphing data over time
		15.5. Pretty graphs
			15.5.1. Themes
	16. Hotspot maps
		16.1. A simple map
		16.2. What really are maps?
		16.3. Making a hotspot map
			16.3.1. Colors
	17. Choropleth maps
		17.1. Spatial joins
		17.2. Making choropleth maps
	18. Interactive maps
		18.1. Why do interactive graphs matter?
			18.1.1. Understanding your data
			18.1.2. Police departments use them
		18.2. Making the interactive map
		18.3. Adding popup information
		18.4. Dealing with too many markers
		18.5. Interactive choropleth maps
V. Collect
	19. Webscraping with rvest
		19.1. Scraping one page
		19.2. Cleaning the webscraped data
	20. Functions
		20.1. A simple function
		20.2. Adding parameters
		20.3. Making a function to scrape recipes
	21. For loops
		21.1. Basic for loops
		21.2. Scraping multiple recipes
	22. Scraping tables from PDFs
		22.1. Scraping the first table
		22.2. Making a function
	23. More scraping tables from PDFs
		23.1. Texas jail data
		23.2. Pregnant women incarcerated
		23.3. Making PDF-scraped data available to others
	24. Geocoding
		24.1. Geocoding a single address
		24.2. Geocoding San Francisco marijuana dispensary locations
Bibliography