Introduction to Data Science Data Analysis and Prediction Algorithms with R

Preface
Acknowledgements
Introduction
	Case studies
	Who will find this book useful?
	What does this book cover?
	What is not covered by this book?
I R
	Installing R and RStudio
		Installing R
		Installing RStudio
	Getting Started with R and RStudio
		Why R?
		The R console
		Scripts
		RStudio
		Installing R packages
	R Basics
		Case study: US Gun Murders
		The very basics
		Exercises
		Data types
		Data frames
		Exercises
		Vectors
		Coercion
		Exercises
		Sorting
		Exercise
		Vector arithmetics
		Exercises
		Indexing
		Exercises
		Basic plots
		Exercises
	Programming basics
		Conditional expressions
		Defining functions
		Namespaces
		For-loops
		Vectorization and functionals
		Exercises
	The tidyverse
		Tidy data
		Exercises
		Manipulating data frames
		Exercises
		The pipe: %>%
		Exercises
		Summarizing data
		Sorting data frames
		Exercises
		Tibbles
		The dot operator
		do
		The purrr package
		Tidyverse conditionals
		Exercises
	Importing data
		Paths and the working directory
		The readr and readxl packages
		Exercises
		Downloading files
		R-base importing functions
		Text versus binary files
		Unicode versus ASCII
		Organizing Data with Spreadsheets
		Exercises
II Data Visualization
	Introduction to data visualization
	ggplot2
		The components of a graph
		ggplot objects
		Geometries
		Aesthetic mappings
		Layers
		Global versus local aesthetic mappings
		Scales
		Labels and titles
		Categories as colors
		Annotation, shapes, and adjustments
		Add-on packages
		Putting it all together
		Quick plots with qplot
		Grids of plots
		Exercises
	Visualizing data distributions
		Variable types
		Case study: describing student heights
		Distribution function
		Cumulative distribution functions
		Histograms
		Smoothed density
		Exercises
		The normal distribution
		Standard units
		Quantile-quantile plots
		Percentiles
		Boxplots
		Stratification
		Case study: describing student heights (continued)
		Exercises
		ggplot2 geometries
		Exercises
	Data visualization in practice
		Case study: new insights on poverty
		Scatterplots
		Faceting
		Time series plots
		Data transformations
		Visualizing multimodal distributions
		Comparing multiple distributions with boxplots and ridge plots
		The ecological fallacy and importance of showing the data
	Data visualization principles
		Encoding data using visual cues
		Know when to include 0
		Do not distort quantities
		Order categories by a meaningful value
		Show the data
		Ease comparisons
		Think of the color blind
		Plots for two variables
		Encoding a third variable
		Avoid pseudo-three-dimensional plots
		Avoid too many significant digits
		Know your audience
		Exercises
		Case study: impact of vaccines on battling infectious diseases
		Exercises
	Robust summaries
		Outliers
		Median
		The inter quartile range (IQR)
		Tukey's definition of an outlier
		Median absolute deviation
		Exercises
		Case study: self-reported student heights
III Statistics with R
	Introduction to Statistics with R
	Probability
		Discrete probability
		Monte Carlo simulations for categorical data
		Independence
		Conditional probabilities
		Addition and multiplication rules
		Combinations and permutations
		Examples
		Infinity in practice
		Exercises
		Continuous probability
		Theoretical continuous distributions
		Monte Carlo simulations for continuous variables
		Continuous distributions
		Exercises
	Random variables
		Random variables
		Sampling models
		The probability distribution of a random variable
		Distributions versus probability distributions
		Notation for random variables
		The expected value and standard error
		Central Limit Theorem
		Statistical properties of averages
		Law of large numbers
		Exercises
		Case study: The Big Short
		Exercises
	Statistical Inference
		Polls
		Populations, samples, parameters and estimates
		Exercises
		Central Limit Theorem in practice
		Exercises
		Confidence intervals
		Exercises
		Power
		p-values
		Association Tests
		Exercises
	Statistical models
		Poll aggregators
		Data driven models
		Exercises
		Bayesian statistics
		Bayes Theorem simulation
		Hierarchical models
		Exercises
		Case study: Election forecasting
		Exercise
		The t-distribution
	Regression
		Case study: is height hereditary?
		The correlation coefficient
		Conditional expectations
		The regression line
		Exercises
	Linear Models
		Case Study: Moneyball
		Confounding
		Least Squared Estimates
		Exercises
		Linear regression in the tidyverse
		Exercises
		Case study: Moneyball (continued)
		The regression fallacy
		Measurement error models
		Exercises
	Association is not causation
		Spurious correlation
		Outliers
		Reversing cause and effect
		Confounders
		Simpson's paradox
		Exercises
IV Data Wrangling
	Introduction to Data Wrangling
	Reshaping data
		gather
		spread
		separate
		unite
		Exercises
	Joining tables
		Joins
		Binding
		Set operators
		Exercises
	Web Scraping
		HTML
		The rvest package
		CSS selectors
		JSON
		Exercises
	String Processing
		The stringr package
		Case study 1: US murders data
		Case study 2: self reported heights
		How to escape when defining strings
		Regular expressions
		Search and replace with regex
		Testing and improving
		Trimming
		Changing lettercase
		Case study 2: self reported heights (continued)
		String splitting
		Case study 3: extracting tables from a PDF
		Recoding
		Exercises
	Parsing Dates and Times
		The date data type
		The lubridate package
		Exercises
	Text mining
		Case study: Trump tweets
		Text as data
		Sentiment analysis
		Exercises
V Machine Learning
	Introduction to Machine Learning
		Notation
		An example
		Exercises
		Evaluation Metrics
		Exercises
		Conditional probabilities and expectations
		Exercises
		Case study: is it a 2 or a 7?
	Smoothing
		Bin smoothing
		Kernels
		Local weighted regression (loess)
		Connecting smoothing to machine learning
		Exercises
	Cross validation
		Motivation with k-nearest neighbors
		Mathematical description of cross validation
		K-fold cross validation
		Exercises
		Bootstrap
		Exercises
	The caret package
		The caret train functon
		Cross validation
		Example: fitting with loess
	Examples of algorithms
		Linear regression
		Exercises
		Logistic regression
		Exercises
		k-nearest neighbors
		Exercises
		Generative models
		Exercises
		Classification and Regression Trees (CART)
		Random Forests
		Exercises
	Machine learning in practice
		Preprocessing
		k-Nearest Neighbor and Random Forest
		Variable importance
		Visual assessments
		Ensembles
		Exercises
	Large datasets
		Matrix algebra
		Exercises
		Distance
		Exercises
		Dimension reduction
		Exercises
		Recommendation systems
		Exercises
		Regularization
		Exercises
		Matrix factorization
		Exercises
	Clustering
		Hierarchical clustering
		k-means
		Heatmaps
		Filtering features
		Exercises
VI Productivity tools
	Introduction to productivity tools
	Accessing the terminal and installing Git
		Accessing the terminal on a Mac
		Installing Git on the Mac
		Installing Git and Git Bash on Windows
		Accessing the terminal on Windows
	Organizing with Unix
		Naming convention
		The terminal
		The filesystem
		Unix commands
		Some examples
		More Unix commands
		Preparing for a data science project
		Advanced Unix
	Git and GitHub
		Why use Git and GitHub?
		GitHub accounts
		GitHub repositories
		Overview of Git
		Initializing a Git directory
		Using Git and GitHub in RStudio
	Reproducible projects with RStudio and R markdown
		RStudio projects
		R markdown
		Organizing a data science project