Data Science, Analytics and Machine Learning with R

Cover
Front Matter
Copyright
Dedication
Epigraph
Overview of data science, analytics, and machine learning
	Introduction
	Overview of the book
	Final remarks
Introduction to R-based language
	Introduction
	How to use this work
	R-based language installation
	Installing RStudio
		The Script Editor
		The console
		The Environment, History, Connections, and Tutorial tabs
		The Files, Plots, Packages, Help, and Viewer tabs
	Objects
	Functions and arguments
	Packages
	Loading datasets
		Loading a dataset using the mouse
		Loading a dataset using codes
		Opening datasets present in R-based language
	Brief notion of data manipulation
	Final remarks
	Supplementary data sets
Types of variables, measurement scales, and accuracy scales*
	Introduction
	Types of variables
		Nonmetric or qualitative variables
		Metric or quantitative variables
	Types of variables and scales of measurement
		Nonmetric variables: Nominal scale
		Nonmetric variables: Ordinal scale
		Quantitative variables: Interval scale
		Quantitative variables: Ratio scale
	Types of variables based on number of categories and scales of accuracy
		Dichotomous or binary variables: Dummy
		Polychotomous variables
		Discrete quantitative variables
		Continuous quantitative variables
	Final remarks
	Exercises
Univariate descriptive statistics
	Introduction
	Frequency distribution table
		Frequency distribution table for qualitative variables
		Frequency distribution table for discrete data
		Frequency distribution table for continuous data grouped into classes
	Graphical representation of the results
		Graphical representation for qualitative variables
			Bar chart
			Pie chart
			Pareto chart
		Graphical representation for quantitative variables
			Line graph
			Scatter plot
			Histogram
			Stem-and-leaf plot
			Boxplot or box-and-whisker diagram
	The most common summary measures in univariate descriptive statistics
		Measures of dispersion or variability
		Coefficient of skewness in R
		Coefficient of kurtosis in R
	Final remarks
	Exercises
	Supplementary data sets
Bivariate descriptive statistics
	Introduction
	Association between two qualitative variables
		Joint frequency distribution tables (Fávero and Belfiore, 2019)
			Elaborating contingency tables in R
		Measures of association (Fávero and Belfiore, 2019)
			Chi-square statistic
				Solving the chi-square statistic in R
			Solution: calculating Phi, contingency, and Cramérs V coefficients in R
				Spearmans coefficient (Fávero and Belfiore, 2019)
				Calculating Spearmans coefficient in R
	Correlation between two quantitative variables
		Constructing a scatter plot in R
		Solution (calculation of covariance and Pearsons correlation coefficient) in R
	Final remarks
	Exercises
	Supplementary data sets
Hypotheses tests
	Introduction
	Univariate tests for normality
		Solving tests for normality in R
	Tests for homogeneity of variance
		Solving tests for homogeneity of variance in R
	Hypotheses tests regarding a population mean (μ) from one random sample
		Solving the z-test and the Students t-test for a single sample in R
	Students t-test to compare two population means from two independent random samples
		Solving the Students t-test for two independent samples in R
	Students t-test to compare two population means from two paired random samples
		Solving Students t-test for two paired samples in R
	Analysis of variance to compare the means of more than two populations
		Factorial ANOVA test
	Final remarks
	Exercises
	Supplementary data sets
Data visualization and multivariate graphs
	Introduction
	The library ggplot2
	Bar chart with ggplot2
	Pareto chart with ggplot2
	Line graph with ggplot2
	Scatter plot with ggplot2
	Histogram with ggplot2
	Boxplot with ggplot2
	Final remarks
	Exercises
	Appendix
		Main colors and color range accepted by R-based language
		Pie charts with ggplot2 and an easier solution
	Supplementary data sets
Webscraping and handcrafted robots
	Introduction
	CSS selector and XPATH
	The tool SelectorGadget
	The library rvest
		Example 1: Using the Function HTML_TEXT()
		Example 2: Using the Function html_table()
	The library RSelenium
	Requirements necessary for using RSelenium
	Creating a robot with RSelenium
	Final remarks
	Exercises
Using application programming interfaces to collect data
	Introduction
	Verbs about API
	Example 1: Who is in the space stations?
	Example 2: Where is the ISS now?
	Example 3: When will the ISS fly over a certain point on the globe?
	Example 4: Health indicators of the World Health Organization
	Final remarks
	Exercises
Managing data
	Introduction
	The operator %>%
	The function rename()
	The function mutate()
	The function filter()
	The function arrange()
	The function group_by()
	The function select()
	The function summarise()
	The functions separate() and unite()
	The functions gather() and spread()
	Join functions
		The function left_join()
		The function right_join()
		The function full_join()
		The function inner_join()
		The functions semi_join() and anti_join()
	Final remarks
	Exercise
	Supplementary data sets
Cluster analysis
	Cluster analysis with hierarchical and nonhierarchical agglomeration schedules in R
		Elaborating hierarchical agglomeration schedules in R
		Elaborating nonhierarchical k-means agglomeration schedules in R
	Final remarks
	Exercise
	Supplementary data sets
Principal component factor analysis
	Principal component factor analysis in R
	Final remarks
	Exercise
	Supplementary data sets
Simple and multiple correspondence analysis
	Applications in R
		Correspondence Analysis
		Multiple correspondence analysis
	Final remarks
	Exercises
	Appendix
	Supplementary data sets
Simple and multiple regression models
	Estimation of regression models in R
		Estimation of a simple linear regression model in R
		Estimation of a multiple linear regression model in R
	Final remarks
	Exercises
	Supplementary data sets
Binary and multinomial logistic regression models
	Estimation of binary and multinomial logistic regression models in R
		Estimation of a binary logistic regression model in R
		Estimation of a multinomial logistic regression model in R
	Final remarks
	Exercises
	Supplementary data sets
Count-data and zero-inflated regression models
	Estimating regression models for count data in R
		Poisson regression model in R
		Negative binomial regression model in R
		Zero-inflated Poisson regression model in R
		Zero-inflated negative binomial regression model in R
	Final remarks
	Exercise
	Supplementary data sets
Generalized linear mixed models
	Estimation of hierarchical linear models in R
		Estimation of a two-level hierarchical linear model (HLM2) with clustered data in R
	Final remarks
	Exercise
	Supplementary data sets
Support vector machines
	Introduction
	Separating hyperplanes
	Maximal margin classifiers
	Support vector classifiers
	Support vector machines
	Support vector machines in R
		Construction of a support vector machine classification plot in R
		Support vector machines application with a linear kernel in R
		Training and validation samples, tuning, and other support vector machine estimations in R
		Comparison of SVM models performance to a binary logistic regression model
	Final remarks
	Exercise
	Supplementary data sets
Classification and regression trees
	Introduction
	CARTs estimation methods
		The entropy of information
		The Gini index
	Variance
	Overfitting
	Pruning
	Hyperparameters
	Estimating CART models in R
	Classification trees in R
		Regression trees in R
	Final remarks
	Exercises
	Supplementary data sets
Boosting and bagging
	Introduction
	Boosting
		Main hyperparameters for boosting
			Number of trees
			Learning rate
			Tree depth
			Minimum number of observations in leaf nodes
			Subsampling
	Bagging
		Main hyperparameters for bagging
			Number of trees
		Minimum number of observations in leaf nodes
	Boosting and bagging applications in R
		Boosting in R
		Bagging in R
	Final remarks
	Exercise
	Supplementary data sets
Random forests
	Introduction
	Random forests
	Hyperparameters
		The number of predictive variables selected at each iterative step
		The number of model iterations
	Random forests applications in R
	Final remarks
	Exercise
	Supplementary data sets
Artificial neural networks
	Introduction
	Artificial neural networks
	Activation functions and estimations of the ouput values of each layer
		Linear activation function
		Sigmoid or logistic activation function
		Hyperbolic tangent activation function
		Softmax activation function
		Softplus activation function
		Rectifier linear unit activation function
	Demonstration of calculations of layer output values
	Method of calculation of estimation errors for iteration feeding
	Hyperparameters
		Defining an activation function
		Choosing a number of hidden layers
		Defining the number of neurons in hidden layers
		Learning rate
		The threshold to evaluate the misclassification rate
		The number of iterations
	Artificial neural networks applications in R
		Estimation of an artificial neural network for a metric-type phenomenon
		Estimation of an artificial neural network for a categorical type phenomenon
	Final remarks
	Exercise
	Supplementary data sets
Working on shapefiles
	Introduction
	Using shapefiles
	Carring a shapefile
	Incorporating information into a shapefile
	Plotting information from a dataset on a map
	Dismembering shapefiles
	Joining shapefiles
	Final considerations
	Supplementary data sets
Dealing with simple feature objects
	Introduction
	Working with simple features
	Creating a simple feature object
	Using layers in simple feature objects
	Combining simple feature objects with shapefiles
	Using R like geographic information systems software
		Buffering
		Buffer union
		Kernel densities
	Combining simple feature layers and objects in search of insight
	Example of using a robot to capture space data
	Final considerations
	Supplementary data sets
Raster objects
	Introduction
	Loading a raster file
	Plotting the raster file information
	Combining a raster object with a shapefile
	Loading raster objects entirely into the computers RAM
	Cutting out raster objects
		Cutting out raster objects with the aid of a mouse
		Cutting raster objects with vector aid
	Final considerations
Exploratory spatial analysis
	Introduction
	Establishing neighborhoods
		Contiguity spatial weights matrix W
		Geographic proximity spatial weights matrix W
		k-Nearest neighbors spatial weights matrix W
		Socioeconomic proximity spatial weights matrix W
	Standardization of matrices
		Row standardization of the matrix W
		Double standardization of the matrix W
		Variance stabilizing of the matrix W
	Techniques for verification of spatial autocorrelation
		Global autocorrelation: Morans I statistic
		Moran scatter plot
		Local autocorrelation: The local Morans statistic
		Local autocorrelation: The Getis and Ords G statistic
	Final remarks
	Exercise
	Supplementary data sets
Enhanced and interactive graphs
	Introduction
	The library plotly
	Scatter plot with plotly
	Line graph with plotly
	Bar chart with plotly
	Pareto chart with plotly
	Histogram with plotly
	Boxplot with plotly
	Pie charts with plotly
	Final remarks
	Exercises
	Supplementary data sets
Dashboards with R
	Introduction
	First steps in the library shiny
	Creating the first dashboard in the library shiny
	Reactive programming
	Construction of a complex dashboard
		First step: Preparing the ui.R and server.R Scripts
		Second step: Introducing the dataset
		Third step: Introducing univariate descriptive statistics and frequency tables of the dataset variables
		Fourth step: Variable distributions graphics
		Fifth step: Including a predictive model
	Final remarks
	Exercise
	Supplementary data sets
References
Answers
	Chapter 3
	Chapter 4
	Chapter 5
	Chapter 6
	Chapter 7
	Chapter 11
	Chapter 12
	Chapter 13
	Chapter 14
	Chapter 15
	Chapter 16
	Chapter 17
	Chapter 18
	Chapter 19
	Chapter 21
	Chapter 22
	Chapter 26
	Chapter 27
Index