Introduction to Data Science: Data Wrangling and Visualization with R

Cover
Table of contents
Preface
Introduction
Part I - R
	1 - Getting started
		1.1 - Why R?
		1.2 - The R console
		1.3 - Scripts
		1.4 - RStudio
			1.4.1 - The panes
			1.4.2 - Key bindings
			1.4.3 - Running commands while editing scripts
			1.4.4 - Changing global options
		1.5 - Installing R packages
	2 - R basics
		2.1 - Motivating example: US Gun Murders
		2.2 - The very basics
			2.2.1 - Objects
			2.2.2 - The workspace
			2.2.3 - Prebuilt functions
			2.2.4 - Prebuilt objects
			2.2.5 - Variable names
			2.2.6 - Saving your workspace
			2.2.7 - Why use scripts?
			2.2.8 - Commenting your code
		2.3 - Data types
			2.3.1 - Data frames
			2.3.2 - Examining objects
			2.3.3 - The accessor: $
			2.3.4 - Vectors
			2.3.5 - Factors
			2.3.6 - Lists
			2.3.7 - Matrices
		2.4 - Vectors
			2.4.1 - Creating vectors
			2.4.2 - Names
			2.4.3 - Sequences
			2.4.4 - Subsetting
		2.5 - Coercion
		2.6 - Not availables (NA)
		2.7 - Sorting
			2.7.1 - sort
			2.7.2 - order
			2.7.3 - max and which.max
			2.7.4 - rank
		2.8 - Vector arithmetics
			2.8.1 - Rescaling a vector
			2.8.2 - Two vectors
			2.8.3 - Beware of recycling
		2.9 - Indexing
			2.9.1 - Subsetting with logicals
			2.9.2 - Logical operators
			2.9.3 - which
			2.9.4 - match
			2.9.5 - %in%
		2.10 - Basic plots
			2.10.1 - plot
			2.10.2 - hist
			2.10.3 - boxplot
			2.10.4 - image
		2.11 - Exercises
	3 - Programming basics
		3.1 - Conditional expressions
		3.2 - Defining functions
		3.3 - Namespaces
		3.4 - For-loops
		3.5 - Vectorization and functionals
		3.6 - Exercises
	4 - The tidyverse
		4.1 - Tidy data
		4.2 - Refining data frames
			4.2.1 - Adding columns
			4.2.2 - Row-wise subsetting
			4.2.3 - Column-wise subsetting
			4.2.4 - Transforming variables
		4.3 - The pipe
		4.4 - Summarizing data
			4.4.1 - The summarize function
			4.4.2 - Multiple summaries
			4.4.3 - Group then summarize with group_by
			4.4.4 - Extracting variables with pull
		4.5 - Sorting
			4.5.1 - Nested sorting
			4.5.2 - The top n
		4.6 - Tibbles
		4.7 - Tibbles versus data frames
			4.7.1 - Creating tibbles
		4.8 - The placeholder
		4.9 - The purrr package
		4.10 - Tidyverse conditionals
			4.10.1 - case_when
			4.10.2 - between
		4.11 - Exercises
	5 - data.table
		5.1 - Refining data tables
			5.1.1 - Column-wise subsetting
			5.1.2 - Adding or transformin variables
			5.1.3 - Reference versus copy
			5.1.4 - Row-wise subsetting
		5.2 - Summarizing data
			5.2.1 - Multiple summaries
			5.2.2 - Group then summarize
		5.3 - Sorting
		5.4 - Exercises
	6 - Importing data
		6.1 - Navigating and managing the filesystem
			6.1.1 - The filesystem
			6.1.2 - Relative and full paths
			6.1.3 - The working directory
			6.1.4 - Generating path names
		6.2 - File types
			6.2.1 - Text files
			6.2.2 - Binary files
			6.2.3 - Encoding
		6.3 - Parsers
			6.3.1 - Base R
			6.3.2 - readr
			6.3.3 - readxl
			6.3.4 - data.table
			6.3.5 - Downloading files
		6.4 - Organizing data with spreadsheets
		6.5 - Exercises
Part II - Data Visualization
	7 - Visualizing data distributions
		7.1 - Variable types
		7.2 - Case study: describing student heights
		7.3 - Distributions
			7.3.1 - Histograms
			7.3.2 - Smoothed density
			7.3.3 - The normal distribution
		7.4 - Boxplots
		7.5 - Stratification
		7.6 - Case study continued
		7.7 - Exercises
	8 - ggplot2
		8.1 - The components of a graph
		8.2 - Initializing an object with data
		8.3 - Adding a geometry
		8.4 - Aesthetic mappings
		8.5 - Other layers
		8.6 - Global aesthetic mappings
		8.7 - Non-aesthetic arguments
		8.8 - Categories as colors
		8.9 - Updating ggplot objects
		8.10 - Scales
		8.11 - Annotations
		8.12 - Add-on packages
		8.13 - Putting it all together
		8.14 - Geometries
			8.14.1 - Barplots
			8.14.2 - Histograms
			8.14.3 - Density plots
			8.14.4 - Boxplots
			8.14.5 - Images
		8.15 - Grids of plots
		8.16 - Exercises
	9 - Data visualization principles
		9.1 - Encoding data using visual cues
		9.2 - Know when to include 0
		9.3 - Do not distort quantities
		9.4 - Order categories by a meaningful value
		9.5 - Show the data
		9.6 - Ease comparisons
			9.6.1 - Use common axes
			9.6.2 - Aligning plots for comparisons
		9.7 - Transformations
		9.8 - Visual cues to be compared should be adjacent
		9.9 - Think of the color blind
		9.10 - Plots for two variables
			9.10.1 - Slope charts
			9.10.2 - Bland-Altman plot
		9.11 - Encoding a third variable
		9.12 - Avoid pseudo-three-dimensional plots
		9.13 - Avoid too many significant digits
		9.14 - Know your audience
		9.15 - Exercises
	10 - Data visualization in practice
		10.1 - Case study 1: new insights on poverty
		10.2 - Scatterplots
		10.3 - Faceting
			10.3.1 - facet_wrap
			10.3.2 - Fixed scales for better comparisons
		10.4 - Time series plots
		10.5 - Data transformations
			10.5.1 - Log transformation
			10.5.2 - Which base?
			10.5.3 - Transform the values or the scale?
		10.6 - Multimodal distributions
		10.7 - Comparing distributions
			10.7.1 - Boxplots
			10.7.2 - Ridge plots
			10.7.3 - Example: 1970 versus 2010 income distributions
			10.7.4 - Accessing computed variables
			10.7.5 - Weighted densities
		10.8 - Case study 2: the ecological fallacy
			10.8.1 - Logistic transformation
			10.8.2 - Show the data
		10.9 - Case study 3: vaccines and infectious diseases
			10.9.1 - Vaccine data
			10.9.2 - Trend plots
		10.10 - Heatmaps
		10.11 - Exercises
Part III - Data Wrangling
	11 - Reshaping data
		11.1 - pivot_longer
		11.2 - pivot_wider
		11.3 - Separating variables
		11.4 - Reshaping with data.table
			11.4.1 - pivot_longer is melt
			11.4.2 - pivot_wider is dcast
			11.4.3 - Separating variables
		11.5 - The janitor package
		11.6 - Exercises
	12 - Joining tables
		12.1 - Joins
			12.1.1 - Left join
			12.1.2 - Right join
			12.1.3 - Inner join
			12.1.4 - Full join
			12.1.5 - Semi join
			12.1.6 - Anti join
		12.2 - Binding
			12.2.1 - Binding columns
			12.2.2 - Binding by rows
		12.3 - Set operators
			12.3.1 - Intersect
			12.3.2 - Union
			12.3.3 - setdiff
			12.3.4 - setequal
		12.4 - Joining with data.table
		12.5 - Exercises
	13 - Parsing dates and times
		13.1 - The date data type
		13.2 - The lubridate package
		13.3 - Dates and times with data.table
		13.4 - Exercises
	14 - Locales
		14.1 - Locales in R
		14.2 - The locale function
		14.3 - Example: wrangling a Spanish dataset
		14.4 - Exercises
	15 - Extracting data from the web
		15.1 - Scraping HTML
			15.1.1 - HTML
			15.1.2 - The rvest package
			15.1.3 - CSS selectors
		15.2 - JSON
		15.3 - Data APIs
			15.3.1 - API types and concepts
			15.3.2 - The httr2 package
		15.4 - Exercises
	16 - String processing
		16.1 - The stringr package
		16.2 - Case study 1: self-reported heights
		16.3 - Escaping
		16.4 - Regular expressions
			16.4.1 - Strings are a regex
			16.4.2 - Special characters
			16.4.3 - Character classes
			16.4.4 - Anchors
			16.4.5 - Bounded quantifiers
			16.4.6 - White space
			16.4.7 - Unbounded quantifiers: *, ?, +
			16.4.8 - Not
			16.4.9 - Groups
			16.4.10 - Search and replace
			16.4.11 - Search and replace using groups
			16.4.12 - Lookarounds
			16.4.13 - Separating variables
		16.5 - Trimming
		16.6 - Case conversion
		16.7 - Case study 1 continued: Putting it all together
		16.8 - Case study 2: extracting tables from a PDF
		16.9 - Renaming levels
		16.10 - Exercises
	17 - Text analysis
		17.1 - Case study: Trump tweets
		17.2 - Text as data
		17.3 - Sentiment analysis
		17.4 - Exercises
Part IV - Productivity Tools
	18 - Organizing with Unix
		18.1 - Naming convention
		18.2 - The terminal
		18.3 - The filesystem
			18.3.1 - Directories and subdirectories
			18.3.2 - The home directory
			18.3.3 - Working directory
			18.3.4 - Paths
		18.4 - Unix commands
			18.4.1 - ls: Listing directory content
			18.4.2 - mkdir and rmdir: make and remove a directory
			18.4.3 - cd: navigating the filesystem by changing directories
			18.4.4 - Examples
		18.5 - More Unix commands
			18.5.1 - mv: moving files
			18.5.2 - cp: copying files
			18.5.3 - rm: removing files
			18.5.4 - less: looking at a file
		18.6 - Case study: Preparing for a project
		18.7 - Advanced Unix
			18.7.1 - Arguments
			18.7.2 - Getting help
			18.7.3 - Pipes
			18.7.4 - Wild cards
			18.7.5 - Environment variables
			18.7.6 - Shells
			18.7.7 - Executables
			18.7.8 - Permissions and file types
			18.7.9 - Commands you should learn
			18.7.10 - File manipulation in R
	19 - Git and GitHub
		19.1 - Why use Git and GitHub?
		19.2 - Overview of Git
		19.3 - GitHub accounts
		19.4 - GitHub repositories
		19.5 - Connecting Git and GitHub
		19.6 - Initial setup
		19.7 - Git basics
			19.7.1 - The working directory
			19.7.2 - add
			19.7.3 - commit
			19.7.4 - push
			19.7.5 - fetch and merge
			19.7.6 - pull
			19.7.7 - clone
		19.8 - .gitignore
		19.9 - Git in RStudio
	20 - Reproducible projects
		20.1 - RStudio projects
		20.2 - Markdown
			20.2.1 - The header
			20.2.2 - R code chunks
			20.2.3 - Global execution options
			20.2.4 - knitR
			20.2.5 - Learning more
		20.3 - Organizing a data science project
			20.3.1 - Create directories in Unix
			20.3.2 - Create an RStudio project
			20.3.3 - Editting R scripts
			20.3.4 - Saving processed data
			20.3.5 - The main analysis file
			20.3.6 - Other directories
			20.3.7 - The README file
			20.3.8 - Initializing a Git directory
			20.3.9 - Add, commit and push files using RStudio
Index